GEOSTEERING CONTROL FRAMEWORK

Description

BACKGROUND

Geosteering may provide for directional control of a drill bit of a drillstring using downhole geological logging measurements, for example, to keep a directional wellbore within a pay zone. In various scenarios, geosteering may be used to keep a wellbore in a particular section of a reservoir to minimize gas or water breakthrough and maximize hydrocarbon production.

SUMMARY

A method may include receiving resistivity data acquired by a downhole tool of a drillstring disposed at least in part in a borehole in a subsurface environment, where the borehole includes a downhole end, and where the borehole defines a borehole axis; receiving seismic data for the subsurface environment, wherein the seismic data include spatial locations; and generating a displacement field using the resistivity data and the seismic data, wherein the displacement field specifies displacement values with respect to the spatial locations of the seismic data. A system may include a processor; memory accessible to the processor; and processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive resistivity data acquired by a downhole tool of a drillstring disposed at least in part in a borehole in a subsurface environment, where the borehole includes a downhole end, and where the borehole defines a borehole axis; receive seismic data for the subsurface environment, where the seismic data include spatial locations; and generate a displacement field using the resistivity data and the seismic data, where the displacement field specifies displacement values with respect to the spatial locations of the seismic data. One or more non-transitory computer-readable storage media may include processor-executable instructions executable to instruct a processor to: receive resistivity data acquired by a downhole tool of a drillstring disposed at least in part in a borehole in a subsurface environment, where the borehole includes a downhole end, and where the borehole defines a borehole axis; receive seismic data for the subsurface environment, where the seismic data include spatial locations; and generate a displacement field using the resistivity data and the seismic data, where the displacement field specifies displacement values with respect to the spatial locations of the seismic data. Various other apparatuses, systems, methods, etc., are also disclosed.

This summary is provided to introduce a selection of concepts that are further described below in the detailed description. This summary is not intended to identify key or essential features of the claimed subject matter, nor is it intended to be used as an aid in limiting the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

Features and advantages of the described implementations may be more readily understood by reference to the following description taken in conjunction with the accompanying drawings.

FIG. 1 illustrates examples of equipment in a geologic environment;

FIG. 2 illustrates an example of a system and examples of types of holes;

FIG. 3 illustrates an example of a geologic environment with a borehole and an example of a portion of a drillstring that may include various components;

FIG. 4 illustrates an example of a portion of a drillstring that may include various components;

FIG. 5 illustrates examples of logs;

FIG. 6 illustrates an example of a system;

FIG. 7 illustrates an example of a system and an example of a method;

FIG. 8 illustrates an example of a graphical user interface;

FIG. 9 illustrates an example of a graphical user interface;

FIG. 10 illustrates an example of a graphical user interface;

FIG. 11 illustrates an example of a graphical user interface;

FIG. 12 illustrates an example of a graphical user interface;

FIG. 13 illustrates an example of a method and an example of a system; and

FIG. 14 illustrates examples of computing and networking equipment.

DETAILED DESCRIPTION

The following description includes embodiments of the best mode presently contemplated for practicing the described implementations. This description is not to be taken in a limiting sense, but rather is made merely for the purpose of describing the general principles of the implementations. The scope of the described implementations should be ascertained with reference to the issued claims.

As mentioned, geosteering may provide for directional control of a drill bit of a drillstring using downhole geological logging measurements, for example, to keep a directional wellbore within a pay zone where, in various scenarios, geosteering may be used to keep a wellbore in a particular section of a reservoir to minimize gas or water breakthrough and maximize hydrocarbon production.

A borehole may be referred to as a wellbore and may include an openhole portion or an uncased portion and/or may include a cased portion. A borehole may be defined by a bore wall that is composed of rock that bounds the borehole. As to a well or a borehole, whether for one or more of exploration, sensing, production, injection or other operation(s), it may be planned. Such a process may be referred to generally as well planning, a process by which a path may be mapped in a geologic environment. Such a path may be referred to as a trajectory, which may include coordinates in a three-dimensional coordinate system where a measure along the trajectory may be a measured depth (MD), a total vertical depth (TVD) or another type of measure.

As an example, drilling may include using one or more logging tools that may perform one or more logging operations while drilling or otherwise with a drillstring (e.g., while stationary, while tripping in, tripping out, etc.). As an example, drilling or one or more other operations may occur responsive to measurements. For example, a logging while drilling operation may acquire measurements and adjust drilling based at least in part on such measurements. In such an example, adjustments may be made by actuating one or more geosteering actuators that may provide for orienting a drill bit of a drillstring.

FIG. 1 shows an example of a system 100 that includes a workspace framework 110 that may provide for instantiation of, rendering of, interactions with, etc., a graphical user interface (GUI) 120. In the example of FIG. 1, the GUI 120 may include graphical controls for computational frameworks (e.g., applications, etc.) 121, projects 122, visualization features 123, one or more other features 124, data access 125, and data storage 126.

In the example of FIG. 1, the workspace framework 110 may be tailored to a particular geologic environment such as an example geologic environment 150. For example, the geologic environment 150 may include layers (e.g., stratification) that include a reservoir 151 and that may be intersected by a fault 153. As an example, the geologic environment 150 may be outfitted with a variety of sensors, detectors, actuators, etc. For example, equipment 152 may include communication circuitry to receive and to transmit information with respect to one or more networks 155. Such information may include information associated with downhole equipment 154, which may be equipment to acquire information, to assist with resource recovery, etc. Other equipment 156 may be located remote from a wellsite and include sensing, detecting, emitting or other circuitry. Such equipment may include storage and communication circuitry to store and to communicate data, instructions, etc. As an example, one or more satellites may be provided for purposes of communications, data acquisition, etc. For example, FIG. 1 shows a satellite 170 in communication with the network 155 that may be configured for communications, noting that the satellite may additionally or alternatively include circuitry for imagery (e.g., spatial, spectral, temporal, radiometric, etc.).

FIG. 1 also shows the geologic environment 150 as optionally including equipment 157 and 158 associated with a well that includes a substantially horizontal portion that may intersect with one or more fractures 159. For example, consider a well in a shale formation that may include natural fractures, artificial fractures (e.g., hydraulic fractures) or a combination of natural and artificial fractures. As an example, a well may be drilled for a reservoir that is laterally extensive. In such an example, lateral variations in properties, stresses, etc. may exist where an assessment of such variations may assist with planning, operations, etc. to develop a laterally extensive reservoir (e.g., via fracturing, injecting, extracting, etc.). As an example, the equipment 157 and/or 158 may include components, a system, systems, etc. for fracturing, seismic sensing, analysis of seismic data, assessment of one or more fractures, etc.

In the example of FIG. 1, the GUI 120 shows some examples of computational frameworks, including the DRILLPLAN, DRILLOPS, PETREL, TECHLOG, PETROMOD, ECLIPSE, PIPESIM, and INTERSECT frameworks (SLB, Houston, Texas).

The DRILLPLAN framework provides for digital well construction planning and includes features for automation of repetitive tasks and validation workflows, enabling improved quality drilling programs (e.g., digital drilling plans, etc.) to be produced quickly with assured coherency.

The DRILLOPS framework may execute a digital drilling plan and ensures plan adherence, while delivering goal-based automation. The DRILLOPS framework may generate activity plans automatically individual operations, whether they are monitored and/or controlled on the rig or in town. Automation may utilize data analysis and learning systems to assist and optimize tasks, such as, for example, setting ROP to drilling a stand. A preset menu of automatable drilling tasks may be rendered, and, using data analysis and models, a plan may be executed in a manner to achieve a specified goal, where, for example, measurements may be utilized for calibration. The DRILLOPS framework provides flexibility to modify and replan activities dynamically, for example, based on a live appraisal of various factors (e.g., equipment, personnel, and supplies). Well construction activities (e.g., tripping, drilling, cementing, etc.) may be continually monitored and dynamically updated using feedback from operational activities. The DRILLOPS framework may provide for various levels of automation based on planning and/or re-planning (e.g., via the DRILLPLAN framework), feedback, etc.

The PETREL framework may be part of the DELFI environment for utilization in geosciences and geoengineering, for example, to analyze subsurface data from exploration to production of fluid from a reservoir. The DELFI cognitive exploration and production (E&P) environment (SLB, Houston, Texas), referred to herein as the DELFI environment or DELFI framework, is a secure, cognitive, cloud-based collaborative environment that integrates data and workflows with digital technologies, such as artificial intelligence and machine learning.

The PETREL framework provides components that allow for optimization of various exploration, development and production operations. The PETREL framework includes seismic to simulation software components that may output information for use in increasing reservoir performance, for example, by improving asset team productivity. Through use of such a framework, various professionals (e.g., geophysicists, geologists, and reservoir engineers) may develop collaborative workflows and integrate operations to streamline processes (e.g., with respect to one or more geologic environments, etc.). Such a framework may be considered an application (e.g., executable using one or more devices) and may be considered a data-driven application (e.g., where data is input for purposes of modeling, simulating, etc.).

The TECHLOG framework may handle and process field and laboratory data for a variety of geologic environments (e.g., deepwater exploration, shale, etc.). The TECHLOG framework may structure wellbore data for analyses, planning, etc.

The PETROMOD framework provides petroleum systems modeling capabilities that may combine one or more of seismic, well, and geological information to model the evolution of a sedimentary basin. The PETROMOD framework may predict if, and how, a reservoir has been charged with hydrocarbons, including the source and timing of hydrocarbon generation, migration routes, quantities, and hydrocarbon type in the subsurface or at surface conditions.

The ECLIPSE framework provides a reservoir simulator (e.g., as a computational framework) with numerical solutions for fast and accurate prediction of dynamic behavior for various types of reservoirs and development schemes.

The INTERSECT framework provides a high-resolution reservoir simulator for simulation of detailed geological features and quantification of uncertainties, for example, by creating accurate production scenarios and, with the integration of precise models of the surface facilities and field operations, the INTERSECT framework may produce reliable results, which may be continuously updated by real-time data exchanges (e.g., from one or more types of data acquisition equipment in the field that may acquire data during one or more types of field operations, etc.). The INTERSECT framework may provide completion configurations for complex wells where such configurations may be built in the field, may provide detailed enhanced-oil-recovery (EOR) formulations where such formulations may be implemented in the field, may analyze application of steam injection and other thermal EOR techniques for implementation in the field, advanced production controls in terms of reservoir coupling and flexible field management, and flexibility to script customized solutions for improved modeling and field management control. The INTERSECT framework, as with the other example frameworks, may be utilized as part of the DELFI environment, for example, for rapid simulation of multiple concurrent cases. For example, a workflow may utilize one or more of the DELFI environment on demand reservoir simulation features.

The aforementioned DELFI environment provides various features for workflows as to subsurface analysis, planning, construction and production, for example, as illustrated in the workspace framework 110. As shown in FIG. 1, outputs from the workspace framework 110 may be utilized for directing, controlling, etc., one or more processes in the geologic environment 150 and, feedback 160, may be received via one or more interfaces in one or more forms (e.g., acquired data as to operational conditions, equipment conditions, environment conditions, etc.).

As an example, a workflow may progress to a geology and geophysics (“G&G”) service provider, which may generate a well trajectory, which may involve execution of one or more G&G frameworks (e.g., consider the PETREL framework, etc.).

In the example of FIG. 1, the visualization features 123 may be implemented via the workspace framework 110, for example, to perform tasks as associated with one or more of subsurface regions, planning operations, constructing wells and/or surface fluid networks, and producing from a reservoir.

As an example, a visualization process may implement one or more of various features that may be suitable for one or more web applications. For example, a template may involve use of the JAVASCRIPT object notation format (JSON) and/or one or more other languages/formats. As an example, a framework may include one or more converters. For example, consider a JSON to PYTHON converter and/or a PYTHON to JSON converter. Such an approach may provide for compatibility of devices, frameworks, etc., with respect to one or more sets of instructions.

As an example, visualization features may provide for visualization of various earth models, properties, etc., in one or more dimensions. As an example, visualization features may provide for rendering of information in multiple dimensions, which may optionally include multiple resolution rendering. In such an example, information being rendered may be associated with one or more frameworks and/or one or more data stores. As an example, visualization features may include one or more control features for control of equipment, which may include, for example, field equipment that may perform one or more field operations. As an example, a workflow may utilize one or more frameworks to generate information that may be utilized to control one or more types of field equipment (e.g., drilling equipment, wireline equipment, fracturing equipment, etc.).

As to a reservoir model that may be suitable for utilization by a simulator, consider acquisition of seismic data as acquired via reflection seismology, which finds use in geophysics, for example, to estimate properties of subsurface formations. As an example, reflection seismology may provide seismic data representing waves of elastic energy (e.g., as transmitted by P-waves and S-waves, in a frequency range of approximately 1 Hz to approximately 100 Hz). Seismic data may be processed and interpreted, for example, to understand better composition, fluid content, extent and geometry of subsurface rocks. Such interpretation results may be utilized to plan, simulate, perform, etc., one or more operations for production of fluid from a reservoir (e.g., reservoir rock, etc.).

As an example, a model may be a simulated version of a geologic environment. As an example, a simulator may include features for simulating physical phenomena in a geologic environment based at least in part on a model or models. A simulator, such as a reservoir simulator, may simulate fluid flow in a geologic environment based at least in part on a model that may be generated via a framework that receives seismic data. A simulator may be a computerized system (e.g., a computing system) that may execute instructions using one or more processors to solve a system of equations that describe physical phenomena subject to various constraints. In such an example, the system of equations may be spatially defined (e.g., numerically discretized) according to a spatial model that that includes layers of rock, geobodies, etc., that have corresponding positions that may be based on interpretation of seismic and/or other data. A spatial model may be a cell-based model where cells are defined by a grid (e.g., a mesh). A cell in a cell-based model may represent a physical area or volume in a geologic environment where the cell may be assigned physical properties (e.g., permeability, fluid properties, etc.) that may be germane to one or more physical phenomena (e.g., fluid volume, fluid flow, pressure, etc.). A reservoir simulation model may be a spatial model that may be cell-based.

While several simulators are illustrated in the example of FIG. 1, one or more other simulators may be utilized, additionally or alternatively. For example, consider the VISAGE geomechanics simulator (SLB, Houston Texas) or the PIPESIM network simulator (SLB, Houston Texas), etc.

As an example, a workflow may utilize one or more types of data for one or more processes (e.g., stratigraphic modeling, basin modeling, completion designs, drilling, production, injection, etc.). As an example, one or more tools may provide data that may be used in a workflow or workflows that may implement one or more frameworks (e.g., PETREL, TECHLOG, PETROMOD, ECLIPSE, etc.).

In the example of FIG. 1, drilling may be performed in the geologic environment 150, for example, to access the reservoir 151, which may be accessed from land or offshore. In FIG. 1, the downhole equipment 154 may be, for example, part of a bottom hole assembly (BHA). The BHA may be used to drill a well. The downhole equipment 154 may communicate information to equipment at the surface, and may receive instructions and information from the equipment at the surface. During a well construction process, a variety of operations (such as cementing, wireline evaluation, testing, etc.) may be conducted. In such embodiments, data collected by tools and sensors and used for reasons such as reservoir characterization may be collected and transmitted.

A well may include a substantially horizontal portion (e.g., lateral portion) that may intersect with one or more fractures. For example, a well in a shale formation may pass through natural fractures, artificial fractures (e.g., hydraulic fractures), or a combination thereof. Such a well may be constructed using directional drilling techniques as described herein. However, these same techniques may be used in connection with other types of directional wells (such as slant wells, S-shaped wells, deep inclined wells, and others) and are not limited to horizontal wells.

FIG. 2 shows an example of a wellsite system 200 (e.g., at a wellsite that may be onshore or offshore). As shown, the wellsite system 200 may include a mud tank 201 for holding mud and other material (e.g., where mud may be a drilling fluid that may help to transport cuttings, etc.), a suction line 203 that serves as an inlet to a mud pump 204 for pumping mud from the mud tank 201 such that mud flows to a vibrating hose 206, a drawworks 207 for winching drill line or drill lines 212, a standpipe 208 that receives mud from the vibrating hose 206, a kelly hose 209 that receives mud from the standpipe 208, a gooseneck or goosenecks 210, a traveling block 211, a crown block 213 for carrying the traveling block 211 via the drill line or drill lines 212 (see, e.g., the crown block 173 of FIG. 1), a derrick 214 (see, e.g., the derrick 172 of FIG. 1), a kelly 218 or a top drive 240, a kelly drive bushing 219, a rotary table 220, a drill floor 221, a bell nipple 222, one or more blowout preventors (BOPs) 223, a drillstring 225, a drill bit 226, a casing head 227 and a flow pipe 228 that carries mud and other material to, for example, the mud tank 201.

In the example system of FIG. 2, a borehole 232 is formed in subsurface formations 230 by rotary drilling; noting that various example embodiments may also use directional drilling or one or more other types of drilling.

As shown in the example of FIG. 2, the drillstring 225 is suspended within the borehole 232 and has a drillstring assembly 250 that includes the drill bit 226 at its lower end. As an example, the drillstring assembly 250 may be a bottom hole assembly (BHA).

The wellsite system 200 may provide for operation of the drillstring 225 and other operations. As shown, the wellsite system 200 includes the platform 215 and the derrick 214 positioned over the borehole 232. As mentioned, the wellsite system 200 may include the rotary table 220 where the drillstring 225 passes through an opening in the rotary table 220.

As shown in the example of FIG. 2, the wellsite system 200 may include the kelly 218 and associated components, etc., or a top drive 240 and associated components. As to a kelly example, the kelly 218 may be a square or hexagonal metal/alloy bar with a hole drilled therein that serves as a mud flow path. The kelly 218 may be used to transmit rotary motion from the rotary table 220 via the kelly drive bushing 219 to the drillstring 225, while allowing the drillstring 225 to be lowered or raised during rotation. The kelly 218 may pass through the kelly drive bushing 219, which may be driven by the rotary table 220. As an example, the rotary table 220 may include a master bushing that operatively couples to the kelly drive bushing 219 such that rotation of the rotary table 220 may turn the kelly drive bushing 219 and hence the kelly 218. The kelly drive bushing 219 may include an inside profile matching an outside profile (e.g., square, hexagonal, etc.) of the kelly 218; however, with slightly larger dimensions so that the kelly 218 may freely move up and down inside the kelly drive bushing 219.

As to a top drive example, the top drive 240 may provide functions performed by a kelly and a rotary table. The top drive 240 may turn the drillstring 225. As an example, the top drive 240 may include one or more motors (e.g., electric and/or hydraulic) connected with appropriate gearing to a short section of pipe called a quill, that in turn may be screwed into a saver sub or the drillstring 225 itself. The top drive 240 may be suspended from the traveling block 211, so the rotary mechanism is free to travel up and down the derrick 214. As an example, a top drive 240 may allow for drilling to be performed with more joint stands than a kelly/rotary table approach.

In the example of FIG. 2, the mud tank 201 may hold mud, which may be one or more types of drilling fluids. As an example, a wellbore may be drilled to produce fluid, inject fluid or both (e.g., hydrocarbons, minerals, water, etc.).

In the example of FIG. 2, the drillstring 225 (e.g., including one or more downhole tools) may be composed of a series of pipes threadably connected together to form a long tube with the drill bit 226 at the lower end thereof. As the drillstring 225 is advanced into a wellbore for drilling, at some point in time prior to or coincident with drilling, the mud may be pumped by the pump 204 from the mud tank 201 (e.g., or other source) via the lines 206, 208 and 209 to a port of the kelly 218 or, for example, to a port of the top drive 240. The mud may then flow via a passage (e.g., or passages) in the drillstring 225 and out of ports located on the drill bit 226 (see, e.g., a directional arrow). As the mud exits the drillstring 225 via ports in the drill bit 226, it may then circulate upwardly through an annular region between an outer surface(s) of the drillstring 225 and surrounding wall(s) (e.g., open borehole, casing, etc.), as indicated by directional arrows. In such a manner, the mud lubricates the drill bit 226 and carries heat energy (e.g., frictional or other energy) and formation cuttings to the surface where the mud (e.g., and cuttings) may be returned to the mud tank 201, for example, for recirculation (e.g., with processing to remove cuttings, etc.).

The mud pumped by the pump 204 into the drillstring 225 may, after exiting the drillstring 225, form a mudcake that lines the wellbore which, among other functions, may reduce friction between the drillstring 225 and surrounding wall(s) (e.g., borehole, casing, etc.). A reduction in friction may facilitate advancing or retracting the drillstring 225. During a drilling operation, the entire drillstring 225 may be pulled from a wellbore and optionally replaced, for example, with a new or sharpened drill bit, a smaller diameter drillstring, etc. As mentioned, the act of pulling a drillstring out of a hole or replacing it in a hole is referred to as tripping. A trip may be referred to as an upward trip or an outward trip or as a downward trip or an inward trip depending on trip direction.

As an example, consider a downward trip where upon arrival of the drill bit 226 of the drillstring 225 at a bottom of a wellbore, pumping of the mud commences to lubricate the drill bit 226 for purposes of drilling to enlarge the wellbore. As mentioned, the mud may be pumped by the pump 204 into a passage of the drillstring 225 and, upon filling of the passage, the mud may be used as a transmission medium to transmit energy, for example, energy that may encode information as in mud-pulse telemetry.

As an example, mud-pulse telemetry equipment may include a downhole device configured to effect changes in pressure in the mud to create an acoustic wave or waves upon which information may modulated. In such an example, information from downhole equipment (e.g., one or more components of the drillstring 225) may be transmitted uphole to an uphole device, which may relay such information to other equipment for processing, control, etc.

As an example, telemetry equipment may operate via transmission of energy via the drillstring 225 itself. For example, consider a signal generator that imparts coded energy signals to the drillstring 225 and repeaters that may receive such energy and repeat it to further transmit the coded energy signals (e.g., information, etc.).

As an example, the drillstring 225 may be fitted with telemetry equipment 252 that includes a rotatable drive shaft, a turbine impeller mechanically coupled to the drive shaft such that the mud may cause the turbine impeller to rotate, a modulator rotor mechanically coupled to the drive shaft such that rotation of the turbine impeller causes said modulator rotor to rotate, a modulator stator mounted adjacent to or proximate to the modulator rotor such that rotation of the modulator rotor relative to the modulator stator creates pressure pulses in the mud, and a controllable brake for selectively braking rotation of the modulator rotor to modulate pressure pulses. In such example, an alternator may be coupled to the aforementioned drive shaft where the alternator includes at least one stator winding electrically coupled to a control circuit to selectively short the at least one stator winding to electromagnetically brake the alternator and thereby selectively brake rotation of the modulator rotor to modulate the pressure pulses in the mud.

In the example of FIG. 2, an uphole control and/or data acquisition system 262 may include circuitry to sense pressure pulses generated by telemetry equipment 252 and, for example, communicate sensed pressure pulses or information derived therefrom for process, control, etc.

The assembly 250 of the illustrated example includes a logging-while-drilling (LWD) module 254, a measurement-while-drilling (MWD) module 256, an optional module 258, a rotary-steerable system (RSS) and/or motor 260, and the drill bit 226. Such components or modules may be referred to as tools where a drillstring may include a plurality of tools.

As to an RSS, it involves technology utilized for direction drilling. Directional drilling involves drilling into the Earth to form a deviated bore such that the trajectory of the bore is not vertical; rather, the trajectory deviates from vertical along one or more portions of the bore. As an example, consider a target that is located at a lateral distance from a surface location where a rig may be stationed. In such an example, drilling may commence with a vertical portion and then deviate from vertical such that the bore is aimed at the target and, eventually, reaches the target. Directional drilling may be implemented where a target may be inaccessible from a vertical location at the surface of the Earth, where material exists in the Earth that may impede drilling or otherwise be detrimental (e.g., consider a salt dome, etc.), where a formation is laterally extensive (e.g., consider a relatively thin yet laterally extensive reservoir), where multiple bores are to be drilled from a single surface bore, where a relief well is desired, etc.

One approach to directional drilling involves a mud motor; noting that a mud motor may present some challenges depending on factors such as rate of penetration (ROP), transferring weight to a bit (e.g., weight on bit, WOB) due to friction, etc. A mud motor may be a positive displacement motor (PDM) that operates to drive a bit during directional drilling. A PDM operates as drilling fluid is pumped through it where the PDM converts hydraulic power of the drilling fluid into mechanical power to cause the bit to rotate. A PDM may operate in a so-called sliding mode, when the drillstring is not rotated from the surface.

An RSS may drill directionally where there is continuous rotation from surface equipment, which may alleviate the sliding of a steerable motor (e.g., a PDM). An RSS may be deployed when drilling directionally (e.g., deviated, horizontal, or extended-reach wells). An RSS may aim to minimize interaction with a borehole wall, which may help to preserve borehole quality. An RSS may aim to exert a relatively consistent side force akin to stabilizers that rotate with the drillstring or orient the bit in the desired direction while continuously rotating at the same number of rotations per minute as the drillstring.

The LWD module 254 may be housed in a suitable type of drill collar and may contain one or a plurality of selected types of logging tools (e.g., NMR unit or units, etc.). It will also be understood that one or more LWD and/or MWD modules may be employed at one or more positions. An LWD module may include capabilities for measuring, processing, and storing information, as well as for communicating with the surface equipment. In the illustrated example, the LWD module 254 may include a seismic measuring device (e.g., sonic, etc.), an NMR measuring device, a resistivity measuring device, etc.

The MWD module 256 may be housed in a suitable type of drill collar and may contain one or more devices for measuring characteristics of the drillstring 225 and the drill bit 226. As an example, the MWD module 256 may include equipment for generating electrical power, for example, to power various components of the drillstring 225. As an example, the MWD module 256 may include the telemetry equipment 252, for example, where the turbine impeller may generate power by flow of the mud; it being understood that other power and/or battery systems may be employed for purposes of powering various components.

As an example, the MWD module 256 may include one or more of the following types of measuring devices: a weight-on-bit measuring device, a torque measuring device, a vibration measuring device, a shock measuring device, a stick slip measuring device, a direction measuring device, and an inclination measuring device.

As an example, one or more measuring devices may be included in a drillstring (e.g., a BHA, etc.) where, for example, measurements may support one or more of geosteering, geostopping, trajectory optimization, etc.

FIG. 2 also shows some examples of types of holes that may be drilled. For example, consider a slant hole 272, an S-shaped hole 274, a deep inclined hole 276 and a horizontal hole 278.

As an example, a drilling operation may include directional drilling where, for example, at least a portion of a well includes a curved axis. For example, consider a radius that defines curvature where an inclination with regard to the vertical may vary until reaching an angle between about 30 degrees and about 60 degrees or, for example, an angle to about 90 degrees or possibly greater than about 90 degrees. As an example, a trajectory and/or a drillstring may be characterized in part by a dogleg severity (DLS), which may be a two-dimensional parameter specified in degrees per 30 meters (e.g., or degrees per 100 feet).

As an example, a directional well may include several shapes where each of the shapes may aim to meet particular operational demands. As an example, a drilling process may be performed on the basis of information as and when it is relayed to a drilling engineer. As an example, inclination and/or direction may be modified based on information received during a drilling process.

As an example, deviation of a bore may be accomplished in part by use of a downhole motor and/or a turbine. As to a motor, consider a drillstring that may include a positive displacement motor (PDM).

As an example, a system may be a steerable system and include equipment to perform a method such as geosteering. As mentioned, a steerable system may be or include an RSS. As an example, a steerable system may include a PDM and/or a turbine on a lower part of a drillstring which, just above a drill bit, a bent sub may be mounted. Geosteering equipment of a drillstring may include one or more geosteering actuators that may provide for orienting a drill bit of the drillstring. For example, an actuator that may include a piston that moves a pad for providing a force that may be exerted against a borehole wall thus steering a bottom hole assembly (e.g., orienting a drill bit of the bottom hole assembly). As an example, an actuator may be a bent downhole motor, which may be actuated via one or more processes. As an example, a bent drilling motor may be used with a fixed bend that cannot be varied during normal operation or with a variable bend that, for example, may be varied based on a geosteering command. As an example, for a variable bend drilling motor, one or more actuators may be included that may be configured to create or vary a bend, thereby affecting the steering behavior of the steering system. As an example, an actuator may be a downhole actuator that may adjust orientation downhole and/or an actuator may be a surface actuator that may perform an action uphole (e.g., at surface) to adjust orientation downhole.

As an example, above a PDM, MWD equipment that provides real time or near real time data of interest (e.g., inclination, direction, pressure, temperature, real weight on the drill bit, torque stress, etc.) and/or LWD equipment may be installed. As to the latter, LWD equipment may make it possible to send to the surface various types of data of interest, including for example, geological data (e.g., gamma ray log, resistivity, density and sonic logs, etc.).

The coupling of sensors providing information on the course of a well trajectory, in real time or near real time, with, for example, one or more logs characterizing the formations from a geological viewpoint, may allow for implementing a geosteering method. Such a method may include navigating a subsurface environment, for example, to follow a desired route to reach a desired target or targets.

As an example, a drillstring may include one or more of an azimuthal density neutron (ADN) tool for measuring density and porosity; a MWD tool for measuring inclination, azimuth and shocks; a compensated dual resistivity (CDR) tool for measuring resistivity and gamma ray related phenomena; a combinable magnetic resonance (CMR) tool for measuring properties (e.g., relaxation properties, etc.); one or more variable gauge stabilizers; one or more bend joints; and a geosteering tool, which may include a motor and optionally equipment for measuring and/or responding to one or more of inclination, resistivity and gamma ray related phenomena.

As an example, a tool such as the ECOSCOPE tool (SLB, Houston, Texas) may be utilized to acquire measurements. Such a tool may include one or more PNGs and associated detectors. Such a tool may include features for one or more of resistivity, neutron porosity, azimuthal gamma ray, density, elemental capture spectroscopy and sigma measurements. For example, consider features for one or more of 2 MHz and 400 kHz propagation resistivity, elemental capture spectroscopy, neutron-gamma density, capture cross section (sigma), azimuthal bulk density, azimuthal photoelectric factor, azimuthal natural gamma ray, density caliper, ultrasonic caliper, annular pressure and temperature while drilling, triaxial shocks and vibration, and near-bit borehole inclination. Such a tool may be operatively coupled to one or more telemetry systems that may provide for real-time acquisition and, for example, real-time decision making, rendering of graphics, etc. As an example, such a tool may be operatively coupled to one or more types of circuitries, which may, for example, perform computations downhole using measurements acquired downhole.

As an example, a tool such as the PERISCOPE tool (SLB, Houston, Texas) may be utilized to acquire measurements. For example, consider measurements such as resistivity, which may be acquired using one or more types of receivers. As an example, a receiver may be or include an antenna. For example, the PERISCOPE tool may include tilted, axial, and transverse antenna. As an example, data acquired from such a tool may provide for identification of layers, number of layers, position of a layer or layers, within a distance of 1 meter or more (e.g., up to or more than 8 meters).

As to sigma measurements (e.g., sigma data), sigma is the macroscopic cross section for the absorption of thermal neutrons, or capture cross section, of a volume of matter, measured in capture units (c.u.). A sigma log is the principal output of a pulsed neutron capture log, which may be used for one or more purposes.

As an example, one or more types of nuclear measurements may be acquired by one or more tools where such nuclear measurements may include one or more of electron density (ρ_e), hydrogen index (HI), and thermal neutron capture cross section (sigma or Σ).

As an example, geosteering may include intentional directional control of a wellbore based on results of downhole geological logging measurements in a manner that aims to keep a directional wellbore within a desired region, zone (e.g., a pay zone), etc. As an example, geosteering may include directing a wellbore to keep the wellbore in a particular section of a reservoir, for example, to minimize gas and/or water breakthrough and, for example, to maximize economic production from a well that includes the wellbore.

Referring again to FIG. 2, the wellsite system 200 may include one or more sensors 264 that are operatively coupled to the control and/or data acquisition system 262. As an example, a sensor or sensors may be at surface locations. As an example, a sensor or sensors may be at downhole locations. As an example, a sensor or sensors may be at one or more remote locations that are not within a distance of the order of about one hundred meters from the wellsite system 200. As an example, a sensor or sensor may be at an offset wellsite where the wellsite system 200 and the offset wellsite are in a common field (e.g., oil and/or gas field).

As an example, one or more of the sensors 264 may be provided for tracking pipe, tracking movement of at least a portion of a drillstring, etc.

As an example, the system 200 may include one or more sensors 266 that may sense and/or transmit signals to a fluid conduit such as a drilling fluid conduit (e.g., a drilling mud conduit). For example, in the system 200, the one or more sensors 266 may be operatively coupled to portions of the standpipe 208 through which mud flows. As an example, a downhole tool may generate pulses that may travel through the mud and be sensed by one or more of the one or more sensors 266 (e.g., consider mud-pulse telemetry). In such an example, the downhole tool may include associated circuitry such as, for example, encoding circuitry that may encode signals, for example, to reduce demands as to transmission. As an example, circuitry at the surface may include decoding circuitry to decode encoded information transmitted at least in part via mud-pulse telemetry. As an example, circuitry at the surface may include encoder circuitry and/or decoder circuitry and circuitry downhole may include encoder circuitry and/or decoder circuitry. As an example, the system 200 may include a transmitter that may generate signals that may be transmitted downhole via mud (e.g., drilling fluid) as a transmission medium.

Analysis of formation information acquired by one or more tools may reveal features such as, for example, vugs, dissolution planes (e.g., dissolution along bedding planes), stress-related features, dip events, etc. As an example, a tool may acquire information that may help to characterize a reservoir, optionally a fractured reservoir where fractures may be natural and/or artificial (e.g., hydraulic fractures). A reservoir may be a porous formation where fluid may be within various pores of the porous formation and amenable to movement (e.g., to produce fluid from the reservoir). As an example, information acquired by a tool or tools may be analyzed using a framework such as the TECHLOG framework (SLB, Houston, Texas). As an example, the TECHLOG framework may be interoperable with one or more other frameworks such as, for example, the PETREL framework (SLB, Houston, Texas). As an example, a computational environment such as, for example, the DELFI environment (SLB, Houston, Texas) may be utilized, which may provide for utilization of the PETREL framework and other frameworks, optionally in interrelated manners.

FIG. 3 shows an example of a drilling assembly 300 in a geologic environment 301 that includes a borehole 303 where the drilling assembly 300 (e.g., a drillstring) includes a bit 304 and a motor section 310 where the motor section 310 may drive the bit 304 (e.g., cause the bit 304 to rotate and deepen the borehole 303).

As shown, the motor section 310 may include a dump valve 312, a power section 314, a surface-adjustable bent housing 316, a transmission assembly 318, a bearing section 320 and a drive shaft 322, which may be operatively coupled to a bit such as the bit 304. The motor section 310 of FIG. 3 may be a POWERPAK family motor section (SLB, Houston, Texas) or another type of motor section.

A power section may convert hydraulic energy from drilling fluid into mechanical power to turn a bit. For example, consider the reverse application of the Moineau pump principle. During operation, drilling fluid may be pumped into a power section at a pressure that causes the rotor to rotate within the stator where the rotational force is transmitted through a transmission shaft and drive shaft to a bit.

FIG. 3 also shows examples of components 340 such as, for example, sensors 350, circuitry 360 and a geosteering actuator 370. As shown, the sensors 350 may include a conductivity and dielectric sensor 352, a gamma sensor 354 and one or more other sensors 356. As shown, the circuitry 360 may include a processor 362, memory 364 and one or more other types of circuitries 366. As shown, the geosteering actuator 370 may be operatively coupled to the circuitry 360 and the sensors 350. For example, the circuitry 360 may process signals (e.g., measurements or sensor data) of the sensors 350 to generate one or more commands for actuation of the geosteering actuator 370. In the example of FIG. 3, the geosteering actuator 370 may provide for one or more of PDM actuation and bent sub actuation, for example, to orient the drill bit 304.

FIG. 4 shows an example of a drilling assembly 400 (e.g., a portion of a drillstring) that includes a bit 404 and a rotary steerable system (RSS) 410. As mentioned, an RSS may be utilized for directional drilling, including geosteering. As an example, the RSS 410 may include one or more features of a POWERDRIVE ARCHER RSS (SLB, Houston, Texas).

FIG. 4 also shows examples of components 440 such as, for example, sensors 450, circuitry 460 and a geosteering actuator 470. As shown, the sensors 450 may include a conductivity and dielectric sensor 452, a gamma sensor 454 and one or more other sensors 456. As shown, the circuitry 460 may include a processor 462, memory 464 and one or more other types of circuitries 466. As shown, the geosteering actuator 470 may be operatively coupled to the circuitry 460 and the sensors 450. For example, the circuitry 460 may process signals (e.g., measurements or sensor data) of the sensors 450 to generate one or more commands for actuation of the geosteering actuator 470. In the example of FIG. 4, the geosteering actuator 470 may provide for RSS actuation, for example, to orient the drill bit 404.

As an example, the drilling assembly 400 may include one or more of a near-bit continuous inclination and azimuth measurement unit or sub, a near-bit azimuthal gamma ray measurement unit or sub, and one or more other types of measurement units or subs.

As an example, a drilling assembly may include one or more types of circuitries. For example, consider a processing unit with a processor and associated memory where one or more sensors may generate signals that may be received by the processing unit. In such an example, the processing unit may perform computations that may utilize information in the signals (e.g., measurements, etc.) to generate commands for geosteering. In such an example, a drilling assembly may be capable of performing, at least in part, downhole geosteering according to geosteering commands generated downhole without transmission of information uphole to a controller and subsequent transmission of information downhole to geosteering equipment. In such an example, at least some types of geosteering processes may be performed more rapidly in response to sensor signals. For example, consider sensor signals indicative of one or more of presence of clay, an amount of clay, a type of clay, and a boundary as an interface between layers, where downhole geosteering equipment may act to steer a drill bit based on one or more of such sensor signals.

As an example, an electromagnetic conductivity measurement tool (ECM tool) may be implemented as a wireline tool and/or implemented as a LWD tool to generate permittivity and conductivity measurements at each frequency for one or more frequencies, which may be interpreted using a petrophysical model. In such an example, output parameters of the model may include water-filled porosity (hence water saturation if the total porosity is known) and water salinity. As an example, parameters that may be output using ECM tool measurements (e.g., induction, propagation, etc.) may include one or more of bulk formation cation exchange capacity (CEC), water saturation (S_w), connate water salinity, Archie cementation exponent and Archie saturation exponent.

FIG. 5 shows example logs 500 that include various measurements acquired by one or more downhole tools. For example, the logs 500 include spontaneous potential (mV), gamma ray (gAPI), resistivity (ohm.m), neutron porosity (percent), and bulk density (g.c⁻³). The gamma ray response (track 1) distinguishes the low gamma ray value of sand from the higher value of shale. The spontaneous potential curve generally follows a trend similar to that of the gamma ray. The next column, referred to as a depth track (track 2), indicates the depth at which measurements have been acquired. Across the sandstone formation, the resistivity measurements (track 3) are noticeably higher in the hydrocarbon zone than in the water-saturated zone in the lower part of the sand. Both neutron porosity and bulk density (track 4) provide measures of porosity. Within the hydrocarbon-bearing zone, the separation of the curves varies depending on the type of fluid encountered.

As an example, logs may be acquired as to formation parameters versus depth where, from such logs, lithologies may be identified that may differentiate various type of rock. For example, consider differentiating between porous and nonporous rock, which may provide for identification of one or more pay zones in subsurface formations. In a given field or local geological province, certain formations may have distinctive characteristics that appear similar from one well to the next, providing geologists with a basis for locating the depths of various strata in the subsurface. For example, consider identification of formation tops, which may be tracked from logs of one well to logs of another well. In the example of FIG. 5, the logs 500 include variations with respect to shale and sand where a first interface may be referred to as formation top X and a second interface may be referred to as formation top X+1. In such an example, an interface may be referred to as a boundary, which may also be identifiable in one or more other types of data such as, for example, seismic data. As an example, a workflow may include correlation of seismic picks to geologic picks, such as formation tops interpreted from well logs, to improve model building, etc.

In 1942, the relationship between resistivity, porosity and water saturation (and thus its inverse: hydrocarbon saturation) was established by G. E. Archie, paving the way for a quantitative evaluation of formation properties using well logs. The Archie equation or relationship may be expressed between the formation factor (F) and porosity (phi) as F=1/phi^m, where the porosity exponent, m, is a constant for a particular formation or type of rock, which may be referred to as the Archie cementation exponent (e.g., consider values between 1.8 and 2.0 for consolidated sandstones, and close to 1.3 for loosely consolidated sandstones).

As to resistivity of rock, it is a measure of the degree to which rock may impede the flow of an electric current. As shown, resistivity may be expressed in units of ohm.m, noting that it may be measured in ohm.m²/m. The reciprocal of resistivity is conductivity, which is typically expressed in terms of millimhos or mmhos. The ability to conduct electrical current is a function of the conductivity of water contained in pore space of rock. Pure water does not conduct electricity; whereas, salt ions found in most formation waters do provide for conduction of electricity. Brine-saturated rocks tend to have high conductivity and low resistivity, which may be seen in the resistivity log data of FIG. 5 at depths about 7,200 feet. Hydrocarbons, which are nonconductive, cause resistivity values to increase as the pore spaces within a rock become more saturated with oil or gas.

As to spontaneous potential (SP), it is a measurement of voltage difference between a movable electrode in a wellbore and a fixed electrode at the surface. This electrical potential is primarily generated as a result of exchanges of fluids of different salinities (e.g., salinity of drilling fluid and salinity of formation fluid). During the course of drilling, permeable rock within a wellbore may become invaded by drilling mud filtrate where, if the filtrate is less saline than formation fluid, negatively charged chlorine ions from formation water may cause the SP curve to deflect to the left from an arbitrary baseline established across impermeable shale formations. The magnitude of the deflection is influenced by a number of factors, including permeability, porosity, formation water salinity and mud filtrate properties. Permeable formations filled with water that is fresher than the filtrate will cause the curve to deflect to the right. Hence, by the nature of deflections, an SP log may indicate which formations are permeable. A permeable formation with a high resistivity may be more likely to contain hydrocarbons.

As shown in the logs 500, a gamma ray (GR) log may be included, along with one or more of multiple resistivity logs and porosity readings obtained from density, neutron, and/or sonic logs. As to GR log acquisition, a downhole tool may measure naturally occurring radioactivity from a formation where a GR log may help differentiate non-reservoir rocks (e.g., shales and clays) from reservoir rocks (e.g., sandstone and carbonates). Shales and clays tend to be derived from rocks that tend to contain naturally occurring radioactive elements, primarily potassium, uranium and thorium. As a consequence, shales and clays are more radioactive than clean sandstones and carbonates. Quartz and calcium carbonate produce almost no radiation. A log analysis may look for formations with low background radiation because they may have potential to contain moveable hydrocarbons.

Various resistivity tools may measure a formation at different depths of investigation (e.g., shallow, medium and deep). A resulting log may present shallow, medium and deep tracks. A shallow curve, charting the smallest radius of investigation, may indicate resistivity of a flushed zone surrounding a borehole; a medium curve may indicate resistivity of an invaded zone; and a deepest curve may indicate resistivity of an uncontaminated zone, which may be presumed to be a true formation resistivity; noting that such a curve may still be affected by the presence of mud filtrate. By evaluating separations between curves at different depths of investigation, an analysis may provide an estimation of a diameter of invasion by mud filtrate and may be able to determine which zones are more permeable than others.

As to formation bulk density, it provides a measure of porosity. The bulk density of a formation is based on a ratio of a measured interval's mass to its volume. In general, rock porosity tends to be inversely related to rock density. Formation bulk density may be derived from electron density of a formation. Such a measurement may be obtained by a logging device that emits gamma rays into a formation. Gamma rays may collide with electrons in a formation, giving off energy and scattering in a process known as Compton scattering. The number of such collisions is directly related to the number of electrons in a formation. In low-density formations, more of these scattered gamma rays are able to reach a detector than in formations of higher density.

As hydrogen tends to be a major constituent of both water and hydrocarbons and because water and hydrocarbons concentrate in rock pores, the concentration of hydrogen atoms may be used to determine fluid-filled porosity of a formation. Hydrogen atoms have nearly the same mass as neutrons. Neutron logging tools emit neutrons using a chemical source or an electronic neutron generator. When these neutrons collide with hydrogen atoms in a formation, they lose the maximal energy, slow down and eventually reach a very-low-energy state (e.g., a thermal state). The rate at which neutrons reach the thermal state is proportional to the hydrogen concentration or index (HI). Various neutron porosity tools measure HI, which may be converted to neutron porosity.

As an example, a sonic log may be used to determine porosity by charting the speed of a compressional sound wave as it travels through a formation. Interval transit time (Δt), measured in microseconds per meter or foot and often referred to as slowness, is the reciprocal of velocity. Lithology and porosity affect Δt. Dense, consolidated formations characterized by compaction at depth generally result in a faster (shorter) Δt while fluid-filled porosity results in a slower (longer) Δt. Measurements may be affected by formation and borehole conditions. In various instances, quality control processes may be performed on data. As an example, gas, fractures and lack of compaction may demand adjustments to be applied to a sonic log. Lithologies affect the density, neutron and sonic logs. Invasion of mud filtrate into porous formations affects resistivity readings, and temperature affects the resistivity of both filtrate and saline formation water.

As an example, directional drilling may involve drilling a number of different sections such as, for example, a build section, a landing section and a lateral section. In such an example, a build section may be a portion of a directional wellbore curve that may extend from a kick-off point (KOP) to another point. As to a landing section, it may be a portion of a wellbore beyond a build section where steering may be controlled in an effort to hit a target. A landing section may be composed of segments such as, for example, an upper segment, which may be referred to as an approach section, and a lower segment, which may be referred to as a taper section. In the approach section, the magnitude of changes may tend to be greater than in the taper section as the taper section may aim to form a wellbore that smoothly transition at the end of the landing as the drillstring enters a target zone (e.g., a target formation). As to a lateral section, it may be a portion of a wellbore that extends substantially horizontally from an end of a landing taper, out to an end of the wellbore. A course change within a lateral section may affect a reservoir for better or for worse. As an example, a lateral section may be drilled using a BHA, which may include a mud motor, an RSS, etc. In various scenarios, inclination and/or azimuth of a lateral section may be maintained through a combination of sliding and rotating of a drillstring.

As an example, directional drilling may include geosteering as part of a landing job (e.g., drilling a landing section). In a landing job for a well, estimated well tops in the current well may lack accuracy. For example, estimated well tops may be rough estimates based on data from one or more offset wells as may be visually assessed by one or more individuals. As explained, a drillstring may include one or more logging tools to acquire measurements while drilling (e.g., MWD, LWD, etc.). Thus, when a current well is being drilled, real-time log measurements may be acquired. Where such measurements are available, an assessment may involve performing a comparison of a current well's log data and log data from one or more other wells (e.g., log data from one or more offset wells) to generate a more accurate estimate of one or more well tops. Such an assessment may be referred to as log correlation during geosteering. During directional drilling, accurate estimation of well tops may provide for decision making. For example, consider decision making as to whether drilling has arrived one or more points along a trajectory (e.g., planned trajectory points, safety points, etc.). In various instances, a point may be associated with an operation (e.g., a downhole operation, etc.) that is to be performed. During a landing job, a decision may relate to termination of a landing section or a transition from one landing segment to another.

As explained, directional drilling may involve performing log correlation visually, for example, using a number of logs rendered to a display. In such an example, one or more well placement engineers may interact with a graphical user interface that may provide for rendering logs to a display and manually adjusting positions of logs with respect to one another, picking well tops, etc.

As an example, a framework may include one or more components and/or operatively coupled to one or more components for implementing an integrated predictive geosteering workflow. In such an example, components may provide for executing a structural update, a resistivity forward prediction and an uncertainty prediction.

As an example, a framework may include one or more plug-in components. For example, consider one or more PETREL framework plug-in components. As to the PETREL framework, it may operate in conjunction with one or more plug-ins. For example, a plug-in may instruct an instance of PETREL as to performance of one or more of techniques (e.g., import, export, computation, etc.). As an example, a plug-in may provide for launching one or more components within a PETREL framework environment, for example, executing using local resources and/or executing using remote resources (e.g., consider one or more of a workstation, a networked HPC cluster, a cloud platform, etc.). As an example of a plug-in for the PETREL framework, consider the PETREL multi-physics plug-in, which provides tools to integrate electromagnetic (EM) and potential fields data with geological knowledge, seismic data, and well logs. Such a plug may include components for magnetotellurics (MT), controlled source EM (CSEM), gravity and magnetics (GM), etc.

As an example, a workflow may be implemented for execution in real-time using at least in part field data. In such an example, components may be coordinated to expedite execution, for example, by reducing number of calls and responses, logistic waiting times, etc. For example, consider a plug-in that may be a unified plug-in for implementation of sub-workflows in an integrated predictive geosteering workflow (IPG workflow). In such an example, the sub-workflows may include a structural update sub-workflow, a resistivity forward prediction sub-workflow and an uncertainty prediction sub-workflow. In such an example, an IPG workflow may be executed using a one-click approach, for example, after setting of inputs, which may be or may include common inputs. As an example, a framework may provide a set of advanced settings for advanced users to investigate more detailed aspects of each sub-workflow and, for example, a dependency management component that may re-run corresponding sub-workflows automatically according to input and/or one or more setting changes. As an example, a framework may include one or more visualization components. For example, consider a component that may provide for implementing a method that creates visualizations of results from a number of sub-workflows, which may provide for improved interpretations.

FIG. 6 shows an example of a system 600 that includes inputs 610, IPG framework components 650, and outputs 680. As an example, one or more other inputs 620 may be provided for one or more purposes (e.g., as to uncertainty, etc.). As shown in the example of FIG. 6, the IPG framework components 650 may include various components, which may include components to precondition input data (e.g., resampling, noise removal, etc.), to perform geometrical updates to input data, forward prediction for reservoir properties (e.g. spatial distribution of resistivity, acoustic impedance, porosity, water saturation, volume of shale) and/or geometry, confidence of prediction, techniques to estimate uncertainty of forward prediction of resistivity and geometry, etc. In the example of FIG. 6, a structural update component 652, a spatial assessment component 653, a forward prediction component 654, and an uncertainty prediction component 656 are shown as some examples of components.

As shown, a method to perform a geometrical update may include computing spatial displacement between various input data and/or data derived from input data. As an example, spatial displacement may be determined using one or more techniques. For example, consider a subtraction technique, a matching technique (e.g., non-rigid matching, etc.), an implicit technique, etc. As an example, a technique may be for one or more dimensions. For example, consider a 1D, 2D, or a 3D technique. As an example, a multidimensional non-rigid matching (NRM) technique may be employed. As an example, one or more of a resampling different inputs to common spatial positions technique, an interpolation technique, a differencing of coordinates technique, etc., may be applied.

An article by Pappu et al., “A Framework for Non-rigid Matching and Correspondence,” Neural Information Processing Systems (1995), is incorporated by reference herein. In the aforementioned article, a framework for non-rigid matching is described that begins with solving the basic affine point matching problem where the approach iteratively updates the affine parameters and correspondence in turn, each as a function of the other. In such an approach, the affine transformation is solved in closed form, noting the formulation may be used in 2D or 3D. In such an approach, the correspondence is solved by using a softassign procedure, in which two-way assignment constraints are solved without penalty functions. The accuracy of the correspondence is improved by the integration of multiple features. Non-rigid parameter estimation is developed, based on the assumption of a well-articulated model with distinct regions, each of which may move in an affine fashion, or can be approximated as such.

As an example, one or more techniques may be utilized to assess, match, etc. As an example, a technique may involve accessing image data (e.g., 1D, 2D, 3D, etc.) that may be matched, which may involve matching non rigidly. As an example, one or more techniques may be utilized, which may include, for example, a classification in intensity technique, a feature-based technique, etc.

An article by Innmann et al., “VolumeDeform: Real-time Volumetric Non-rigid Reconstruction”, 2016, arXiv: 1603.08161v2 (https://doi.org/10.48550/arXiv.1603.08161) is incorporated by reference herein. The aforementioned article utilizes a dense depth-based constraint formulation and casts finding the optimal deformation of space as a non-linear regularized variational optimization problem by enforcing local smoothness and proximity to the input constraints.

As an example, one or more ML models may be utilized to perform an assessment, matching, etc. An article by Boz̆ic̆ et al., “DeepDeform: Learning Non-rigid RGB-D Reconstruction with Semi-supervised Data”, 2020, arXiv:1912.04302v2 (https://doi.org/10.48550/arXiv.1912.04302), is incorporated by reference herein. The aforementioned article describes generation of a corpus and a data-driven non-rigid feature matching approach, which is integrated into an optimization-based reconstruction pipeline. The data-driven approach utilizes a neural network that operates on RGB-D frames, while maintaining robustness under large non-rigid deformations and producing accurate predictions.

As an example, a machine learning model-based approach may utilize one or more learning techniques, which may include one or more of supervised, semi-supervised, self-supervised, unsupervised, etc.

As an example, an assessment technique (e.g., matching, etc.) may utilize data from a single borehole, from multiple boreholes, etc. As an example, a data-driven approach may be improved as data are acquired, drilling performed, etc. As an example, a technique may improve as drilling in a pay zone continues such that accuracy may include as a borehole becomes longer (e.g., within a pay zone, etc.).

As an example, a method may utilize resistivity data acquired by a downhole tool as dense data within a region proximate to a borehole for morphing other data such that the impact of the dense data may be extended to improve accuracy of other data such that drilling may be improved (e.g., directional drilling, geosteering, etc.). In such an example, the dense data may be spatially fine while the other data may be spatially coarse (e.g., sparser, etc.). As explained, a method may leverage dense data to improve accuracy of other data, particularly as to locations of structural features in a subsurface environment.

As an example, the IPG framework 650 may provide for computing displacements in a spatial domain, a temporal domain and/or a frequency domain. As explained, data such as seismic data may be represented in a spatial, temporal, or frequency domain. As explained, seismic data may be acquired using receivers that record amplitude with respect to time. As an example, resistivity data may be acquired in one or more domains.

As an example, the IPG framework 650 may provide for receiving and/or generating reflectivity data, which may be for one or more dimensions. For example, consider a 3D seismic reflectivity volume that may be a three-dimensional representation of the reflectivity distribution within a volume of a subsurface region. As an example, the IPG framework 650 may provide for generating reflectivity data from resistivity data. For example, consider a technique that involves identification of one or more structures using resistivity data and then generating reflectivity data based at least in part on the one or more structures. In such an example, consider utilizing a function and convolving the function with the one or more structures to generate reflectivity data. In such an example, the function may be a wavelet, which may be a selected wavelet, an extracted wavelet (e.g. extracted from seismic data), etc.

As an example, resistivity data may have a resolution that is finer than a resolution of seismic data (e.g., raw, processed, reflectivity, etc.). In such an example, the resistivity data may be utilized to generate a finer representation of the reflectivity distribution in one or more dimensions. As an example, such a finer representation may be more accurate spatially and may be less distorted than a coarser representation (e.g., based on acquired seismic data, etc.). As an example, a finer representation of reflectivity generated from resistivity may be more limited spatially in that it occupies a relatively small region compared to a region of reflectivity encompassed by a seismic survey. However, as the finer representation may be within a region that is quite relevant for purposes of directional drilling, it may be utilized to effectively adjust a coarser representation whereby such an adjusted coarser representation may become more accurate, spatially, in the region that is quite relevant for purposes of directional drilling, and, for example, extend beyond the reach of resistivity measurements, particularly to characterize structure ahead of a bit, to one or more sides of a bit, etc. In such an example, the effect of resistivity measurements may be imparted to reflectivity of seismic data, where such seismic data exist at least in part beyond the boundary of the resistivity measurements. In such an example, a technique such as, for example, NRM may provide for some amount of extension of resistivity knowledge to characterize structure derived from seismic data.

As an example, a framework may implement an approach that may extend the effect of resistivity measurements beyond the boundary of such resistivity measurements. In such an example, the framework may employ one or more forward prediction techniques, which may be accompanied by uncertainty (e.g., one or more uncertainty metrics). As to how far the framework may extend, consider, for example, directional drilling that utilizes stands of drillpipe where each stand is approximately 30 meters in length. In such an example, an ability to improve structural knowledge ahead of an end of a borehole (e.g., beyond a bit on-bottom position) may provide for controlling the directional drilling for a stand that has been just added to a drillstring. As an example, a framework may provide for extending knowledge of structure by 10 meters, 20 meters, 30 meters, 40, meters, 50 meters, 60 meters, or more. For example, consider extending knowledge of structure by a distance greater than or equal to a number of stands. As an example, consider a framework where resistivity data may be leveraged along with seismic data to improve knowledge of structure for directional drilling of at least three stands ahead (e.g., 90 meters or more).

As explained, resistivity data may provide for improving structural knowledge within a subsurface region via a spatial assessment technique (e.g., matching, etc.) and/or via a forward prediction technique. As an example, a forward prediction technique may provide for more spatial extension in a more accurate manner; whereas, in a spatial assessment technique, the technique chosen and applied may determine reach and/or accuracy.

As to NRM as a spatial assessment technique, consider a process that may involve seismic conditioning enabling seismic resampling and seismic trace alignment based on non-rigid matching (NRM) colored, deterministic simultaneous stochastic inversion for pre-stack seismic data and/or post-stack seismic data.

As an example, a seismic inversion process may be implemented as a pre-stacking inversion and/or as a post-stacking inversion. For example, to obtain multiple impedance attributes, pre-stacking inversion may be implemented to transform well and seismic data into P-impedance, S-impedance, and density. One or more techniques may be employed to perform pre-stacking inversion (e.g., simultaneous, elastic, etc.). As to post-stacking inversion, it may transform a single seismic volume (e.g., a seismic cube, etc.) into an acoustic impedance volume (e.g., an impedance cube, etc.) by using seismic data, well data, and knowledge in stratigraphy for interpretation. In post-stacking inversion, by removing the wavelet from seismic data, this type of inversion may facilitate creation of a high-resolution image of a subsurface region. One or more techniques may be employed to perform post-stacking inversion (e.g., colored, model-based, sparse-spike, band-limited impedance (BLI) inversion, etc.).

As an example, stratigraphic interpretation may be more readily performed using impedance data as, for example, P-impedance may be characterized as a layer property. As an example, an inversion may provide for relatively high-resolution of layers by reducing wavelet effects, side lobes, and tuning.

As an example, P-impedance may be directly computed and compared to well data. As an example, there may be a relationship between porosity and acoustic impedance. As an example, a color inversion may be used with or without a well data and/or a background model. In various instances, post-stacking may be computationally less demanding and easier to process while, for example, pre-stacking may be more efficient to identify lithology and fluid content; noting that pre-stacking may provide for identification of acoustic impedance and shear impedance. In various instances, pre-stacking inversion may be suitable to estimate Vp/Vs ratio. In generally, post-stacking inversion may utilize a single seismic trace while pre-stacking inversion may utilize a linear model (e.g., consider a linear model of AVO).

As explained, a seismic inversion process may be implemented to reconstruct physical properties of subsurface material. For example, consider a seismic inversion that may combine seismic data and well data (e.g., logs, etc.) to predict rock properties (e.g., lithology, fluid content, porosity, etc.) for at least a portion of a region of a seismic survey. In such an example, rock properties may be utilized in a workflow that may aim to identify boundaries, fluids, one or more reservoirs, etc. As an example, an inversion process may include receiving seismic data and receiving well data (e.g., sonic data, density data, etc.) where horizon tracking (e.g., reflector tracking) may be performed using the seismic data and where logs may be prepared using the well data, which may be or include log data. In such an example, the prepared logs and the horizons from horizon tracking may be utilized for well correlation and seismic wavelet extraction. In such an example, an initial model may be constructed of a subsurface region using the tracked horizons (e.g., structure), the well correlations, the extracted wavelet, and the prepared logs. In such an example, given the initial model, an inversion may be performed that may generate acoustic impedance values for the subsurface region, which may be involve performing the inversion in an iterative manner.

In various instances, rock properties may be identified by using well log data (e.g., gamma ray, water saturation, shale volume, etc.) or seismic data (e.g., consider surface seismic, etc.). As explained, a seismic inversion may be performed to obtain one or more additional rock properties such as, for example, impedance and its attributes (e.g., consider one or more of P-impedance, S-impedance, Poisson's Ratio, Vp/Vs, Lambda*Rho, Mu*Rho, etc.). Such properties tend to be linked to fluid content, porosity, and lithology. For example, for a given lithology, if P-impedance is known (e.g., or estimated), a workflow may provide for prediction of porosity. From such a relationship, by combining impedance at a well and impedance computed from seismic data, a workflow may include predicting hydrocarbons across at least a portion of a survey region.

As an example, impedance may be computed as P-impedance being equal to the product of density and P-velocity while S-impedance may be equal to the product of density and S-velocity; noting that one or more other attributes may be computed from impedance.

As an example, from well log data, a workflow may utilize density and velocity data to get impedance and Poisson's ratio and, for example, from seismic data, a workflow may obtain impedance by using seismic inversion which converts seismic data from a boundary property to a layer property. As an example, well data may show properties of a rock's layer and seismic data may show information about a boundary between rock layers. As explained, through a seismic inversion process, seismic data may be transformed into impedance data (e.g., impedance values).

As shown in the example of FIG. 6, the inputs 610 may include acoustic impedance, which may be defined as a physical property whose change determines reflection coefficients at normal incidence, that is, seismic P-wave velocity (e.g., P-velocity) multiplied by density. Because reflection coefficients change with angle, the term elastic impedance may be utilized at times when referring to non-normal incidence situations; noting that an equation for elastic impedance may or may not be utilized.

As indicated, in the example system 600, acoustic impedance may be utilized by the spatial assessment component 653. As shown, displaced acoustic impedance may be utilized by the forward prediction component 654. In particular, the forward prediction component 654 may utilize input resistivity, output of the structural update component 652, and the displaced acoustic impedance to generate, for example, a forward predicted resistivity mean (e.g., 2D, etc.), a forward predicted resistivity standard deviation (std) (e.g., 2D, etc.), and a forward predicted acoustic impedance (e.g., 2D, etc.).

As shown in the example of FIG. 6, the structural update component 652 may receive various types of input to generate output for the forward prediction component 654 and the uncertainty prediction component 656.

As to the uncertainty prediction component 656, it may receive one or more of the other inputs 620, which may include a surface-in-resistivity (SIR) mean, an SIR standard deviation (std), and a surface-in-seismic (SIS) standard deviation (std). Using surfaces derived from seismic data and other received information, the uncertainty prediction component 656, may provide for outputting one or more of a predicted surface mean, a predicted surface mean plus a number of standard deviations (e.g., consider +2*std in 3D), and a predicted surface mean minus a number of standard deviations (e.g., consider −2*std in 3D). In such an example, various IPG framework components may provide for generation of uncertainty as to a surface, for example, by outputting a mean and deviations from the mean, which may be positive and/or negative deviations.

As explained, geosteering may aim to steer a drillstring within a particular layer that may be defined at least in part by a surface. As explained, an IPG framework may provide for generating surface location predictions (e.g., in multiple dimensions) with uncertainty information as to location. Such an approach may provide for improved control of geosteering such that reservoir contact may be increased between a borehole and a reservoir. As an example, improved reservoir contact may improve production of fluid from a fluid reservoir. For example, drilling and a completed well may be more efficient at collecting fluid from a fluid reservoir where reservoir contact is improved.

As explained, a framework may provide for implementation of an IPG workflow that, for example, includes sub-workflows (e.g., structural update, resistivity forward prediction, and uncertainty prediction). In such an example, the framework may provide for managing input and output (I/O), automatically processing intermediate data as may occur between sub-workflows, managing sub-workflows and dependencies therebetween, and managing advanced settings, where desired or appropriate.

As an example, a framework may utilize a session object for improved I/O, versioning, historical data/results access, comparisons, etc. As an example, a framework may include or be operatively coupled to one or more optimizers, for example, such that automated parameter optimization may be performed (e.g., when output quality control logic is available, etc.).

As an example, a framework may provide for generating and interacting with one or more graphical user interfaces (GUIs) where visualizations may be rendered that may include various graphical controls. As an example, a dashboard may be rendered that may allow an individual to visualize comprehensive results within a GUI, which may facilitate further interpretation, quality control, etc.

As an example, an IPG workflow may be applied to resistivity 2D curtain sections, to 2D transverse panes, 3D volumes, etc. As explained, resistivity data may be higher in resolution than seismic survey data where resistivity data are acquired downhole while seismic survey data may be acquired at surface (e.g., land, sea, seabed, etc.). Depending on drilling scenario, a driller may be concerned with features within a 2D plane or, for example, may want to visualize features within a 2D for one or more purposes. As explained, an IPG workflow may utilize 2D curtain sections or 2D transverse panes or, for example, a desired 2D plane that may be oriented in an effort to include various features of interest (e.g., one or more surfaces relevant to geosteering, etc.). As explained, a volumetric approach may be taken, for example, where one or more of transparencies, cutaways, borehole perspectives, etc., may be utilized to facilitate geosteering.

As explained, an IPG workflow may provide for outputting ahead of the bit predictions of reservoir rock properties. In such an example, a driller (e.g., human and/or machine) may steer a drill bit based at least in part on one or more predicted reservoir rock properties, for example, to assure or improve reservoir contact for a borehole being drilled.

As explained with respect to FIG. 6, an IPG framework may include features that allow for seamless and efficiency integration of components to run through sub-workflows, which may be in an on-demand mode. Such an approach may help to speed up field adoption to improve geosteering. As an example, an IPG framework may provide for implementation of an automatic real-time mode where the IPG framework may operate on real-time data as they become available from one or more downhole sensors (e.g., resistivity sensors, etc.).

As explained, a framework may include various components for implementing an IPG workflow. As an example, such a workflow may be performed for performing one or more well placement tasks, one or more well assessment tasks, one or more well control tasks, etc. As an example, the IPG framework components 650 may be components that a user may not necessarily be individually aware of as such components 650 may operate collectively in an integrated manner based on the inputs 610 and/or the other inputs 620 to generate one or more of the outputs 680. As an example, an IPG framework may be driven via one or more interfaces (e.g., GUI, API, etc.), which may be tailored for human and/or machine interactions.

FIG. 7 shows an example of a system 700 that includes an input 710, IPG framework components 750, and output 780 and an example of a method 701 that includes reception blocks 702 and 703, a generation block 704, a technique(s) block 705, a generation block 706, and an implementation block 708.

As an example, a resistivity distribution may be from ultra deep azimuthal resistivity, interpreted resistivity models, PERISCOPE tool (SLB, Houston, Texas), etc. As an example, seismic data may include raw data, derived products, etc. As an example, seismic data may include interpreted geometrical objects (surfaces, faults, geobodies), surfaces, spatial distribution of acoustic impedance, signal processing products (e.g., wavelet extraction, filtering, etc.), etc. As an example, input may include integration control parameters, which may include, for example, parameters to determine one or more aspects pertaining to the IPG framework components 750. For example, consider expected correlation lengths, uncertainties of input data and/or derived products (e.g., uncertainty of interpreted surfaces, uncertainty of well trajectory, uncertainty of resistivity distribution, etc.).

As an example, a geometrical update may refer to updating spatial positions of seismic data and/or derived products. As an example, the system 700 may provide for extrapolating data and/or derived products that are available generally behind a bit (e.g., resistivity, reservoir properties such as porosity, water saturation, volume of shale, etc.) to positions ahead of the bit and/or around a borehole.

As an example, the system 700 may provide for estimating uncertainty in a resulting geometry update and/or forward prediction. For example, consider determination of confidence limits of an interpreted surface extrapolated to positions away from a drill bit, uncertainty of resistivity that has been predicted in front of a drill bit, uncertainty of reservoir properties (e.g., porosity, saturation, etc.), etc.

As an example, the system 700 may provide for repositioning of seismic data and/or derived products (e.g., surfaces, faults, acoustic impedance data, etc.).

As an example, the system 700 may provide for generation of a newly created distribution of reservoir properties (e.g., resistivity, porosity, water saturation, shale content, lithology, etc.), which may be, for example, extrapolated to positions in front of a drill bit (e.g., beyond an end of a borehole).

As an example, the system 700 may provide for generation of newly created geometrical objects, such as, for example, one or more of surfaces, fault planes, geobodies, which may be used to quantify and/or display for analysis uncertainty of a prediction and/or updated position of seismic data and/or derived products.

As an example, the system 700 may provide for generation of newly created reservoir properties distributions, which may be utilized for one or more purposes (e.g., quantify uncertainty of a reservoir property distribution, etc.).

As shown, the method 701 may include the reception block 702 and 703 for receiving seismic data and resistivity data, respectively, which may be in one or more forms (e.g., real, modeled, synthetic, etc.) that are based at least in part on some amount of knowledge of a subsurface region that includes a borehole to be lengthened through drilling. As shown, the generation block 704 may generate synthetic seismic data utilizing the one or more techniques 705. As explained, one technique may involve generating reflectivity data from resistivity data. As shown, the generation block 706 may provide for generating a displacement field. For example, consider comparing or otherwise assessing received seismic to synthetic seismic. As explained, one approach may involve comparing reflectivity data to reflectivity data derived from resistivity. As an example, an assessment may involve applying subtraction, matching, fitting, etc. As shown, the method 701 may include an implementation block 708 for implementing the displacement field. For example, consider implementing the displacement field in geosteering to steer a drill bit in a borehole to length the borehole.

As an example, the method 701 may be a real-time or online method whereby the method 701 is executed responsive to receipt of the resistivity data. For example, consider a directional drilling operation where a tool of a drillstring acquires resistivity data that may be transmitted to one or more framework components to generate synthetic seismic data. In such an example, a displacement field may be generated, for example, using already received seismic data, etc., which, in turn, may be utilized to control the drillstring in a manner that causes a drill bit to move in a particular direction in view of locations of subsurface structures positioned at least in part via the displacement field. In such an example, the method 701 and/or parts thereof may be performed in a loop (e.g., a feedback loop, etc.). As an example, the method 701 may be part of a control method for controlling one or more aspects of directional drilling.

FIG. 8 shows an example of a GUI 800 that includes various graphical controls for performing one or more tasks associated with geosteering, which may be or include predictive geosteering. As shown, the GUI 800 may include an input region where inputs for a name, a well, surfaces, seismic data, wavelet, resistivity, measured depth (MD), faults, acoustic impedance, etc., may be rendered graphical for user interaction. As shown, the GUI 800 may include a task region where one or more types of tasks may be selected, as may be associated with one or more components of the IPG framework components 650 and 750. For example, consider a structural update task, a resistivity forward prediction task, and an uncertainty prediction task. Such tasks may be selectable on an individual basis, for example, to tailor a task to particular parameters, which may include a base set of parameters and optionally an advanced set of parameters.

In the example of FIG. 8, the task region of the GUI 800 is rendered for performing a structural update task where, for example, one or more reflector dimming parameters may be selected. As shown, an option exists for “none”, while other options exist for depth, surface, positive scaling factor and negative scaling factor. As an example, such parameters may be utilized by the structural update component 652 as shown in FIG. 6; noting that the example of FIG. 6 also shows associated advanced settings.

FIG. 9 shows an example of a GUI 900 that may include various graphical controls of the input region of the GUI 800; however, as to the task region, resistivity forward prediction is selected. As shown, for a resistivity forward prediction task, options exist for resistivity cut-off (e.g., from 2 to infinity, etc.), for a prior model, and for advanced parameters. As to the prior model parameters, consider resistivity with associated mean and standard deviation (e.g., constant, 50, and 50), consider acoustic impedance with associated mean and standard deviation (e.g., constant, 7000000, and 3000000), consider an estimate with an associated correlation (e.g., 0.500), etc. As an example, such parameters may be utilized by the forward prediction component 654 as shown in FIG. 6; noting that the example of FIG. 6 also shows associated advanced settings.

FIG. 10 shows an example of a GUI 1000 that may include various graphical controls of the input region of the GUI 800; however, as to the task region, uncertainty prediction is selected. As shown, for an uncertainty prediction task, options exist for enabling observations and/or distributions. For example, as to observations consider one or more surfaces as may be measured (e.g., observed) using one or more downhole tools (e.g., consider the ECOSCOPE tool, the PERISCOPE tool, etc.) resistivity cut-off (e.g., from 2 to infinity, etc.), for a prior model, and for advanced parameters. As to the prior model parameters, consider resistivity with associated mean and standard deviation (e.g., constant, 50, and 50), consider acoustic impedance with associated mean and standard deviation (e.g., constant, 7000000, and 3000000), consider an estimate with an associated correlation (e.g., 0.500), etc. As an example, such parameters may be utilized by the resistivity forward prediction component 654 as shown in FIG. 6; noting that the example of FIG. 6 also shows associated advanced settings.

As an example, one or more features of the GEOSCOPE application (SLB, Houston, Texas) may be utilized. For example, the GEOSCOPE application may receive resistivity measurements from one or more tools (e.g., ECOSCOPE tool, PERISCOPE tool, etc.). The GEOSCOPE application may provide for use of directional electromagnetic measurements to generate results for reservoir mapping-while-drilling, which may provide for identifying subsurface-bedding and fluid-contact details a distance from a borehole (e.g., more than 45 meters from a borehole). Such an application may be integrated into and/or operatively coupled to an IPG framework. For example, consider generating result (e.g., outputs) that may facilitate landing a borehole, reducing drilling risk, maximizing reservoir contact (e.g., reservoir exposure), etc. As an example, a framework may provide for integrating one or more real-time subsurface data-driven maps with one or more seismic surveys such that an understanding of reservoir structure and geometry may be improved to guide geosteering of a drill bit.

As explained, improved geosteering may improve borehole characteristics such that completions and production may be improved, for example, by reducing the number of wells to be placed, by creating better wells with greater certainty, by meeting production targets, by reducing CO2 emissions, etc. For example, by achieving a production target for a field or an area thereof with fewer wells, energy may be conserved and, correspondingly, emissions of one or more greenhouse gases (GHGs) such as CO2.

As an example, a framework may provide for improved understanding of a subsurface environment at a borehole, particularly where such subsurface environment may be complex, heterogenous, etc. As an example, a framework may provide for delineation of structural features and/or fluid.

As an example, a framework may receive data from one or more downhole tools. For example, consider receiving 360-degree tensor data from a downhole tool where such data may be transmitted to surface equipment via one or more of mud pulse telemetry and wired drillpipe. As an example, a framework may utilize one or more types of computing resources, which may be unified or distributed. For example, consider a distributed approach where sufficient network capabilities exist at a field site for transmission of data to and receipt of data from resources of a cloud computing platform. As an example, as to local computing resources, consider a localized cluster, which may include an array of processing units or cores (e.g., CPUs, GPUs, etc.). As an example, a framework may include one or more components for performing inversions of large datasets. As an example, consider a 2D azimuthal pixel-based inversion technique. As an example, output may be in the form of one or more multi-dimensional resistivity volumes. In such an example, consider filtering of one or more volumes to facilitate identification of geometrical relationships of resistive geobodies that may be present near a borehole. As an example, a framework may provide for calibrating seismic data and, for example, feeding into one or more reservoir modeling workflows (e.g., for stress modeling, for fluid flow modeling, etc.).

As an example, a framework may provide for integrating a 1D longitudinal resistivity inversion and a 2D transverse resistivity inversion map in a 2D or 3D steering workflow. Such an approach may provide for optimizing steering, for example, by adjusting a trajectory. As an example, a framework may provide for generating multi-dimensional real-time transverse resistivity inversions, for example, to identify one or more formation tops in one or more sections. As an example, a framework may provide for generating lateral inversions, for example, to provide for multi-dimensional structural understanding of a reservoir while drilling.

As an example, a workflow may include instantiating an interactive IPG framework session for generation of one or more GUIs. In such an example, a user may setup inputs such that the IPG framework, in response, automatically executes appropriate sub-workflows, for example, depending on the given inputs. As explained with respect to the GUIs 800, 900, and 1000, such GUIs may be utilized for inputs as to parameters and/or for one or more default parameters.

FIG. 11 shows an example GUI 1100 that may be utilized to visualize, select, adjust, etc., one or more settings and/or outputs, which may be organized using session objects. As shown, the GUI 1100 may include regions for seismic, PGS structural updates, resistivity forward prediction, uncertainty prediction, etc. For example, consider structural updates graphics as being associated with the structural update component 652 of FIG. 6 and the GUI 800 of FIG. 8, resistivity forward prediction graphics as being associated with the forward prediction component 654 of FIG. 6 and the GUI 900 of FIG. 9, and uncertainty prediction graphics as being associated with the uncertainty prediction component 656 of FIG. 6 and the GUI 1000 of FIG. 10.

FIG. 12 shows an example of a GUI 1200 that includes various outputs in an integrated visualization. In particular, in the example GUI 1200, the visualization integrates the updated surfaces (3D), the updated faults (3D), the forward predicted resistivity mean (2D), the predicted surface mean (3D) and the predicted surface mean +/− twice the standard deviation (e.g., +/−2*std). Such outputs are shown in FIG. 6 amongst the output 680. As an example, the system 600 may be rendered as a GUI or a part thereof where, for example, various ones of the outputs 680 may be selected for generating a result such as a visualization and/or other result for geosteering (e.g., consider a machine-readable result for automated geosteering, etc.). In the example of FIG. 12, the GUI 1200 includes a number of the outputs 680.

As an example, advanced settings may be utilized for one or more types of tasks, workflows, etc. For example, consider research tasks or workflows, well placement tasks or workflows, etc. As an example, advanced settings of each sub-workflow may provide for more detailed investigation of each run, for example, based on given information provided by within a master, integrated workflow. As an example, an integrated workflow may automatically re-run one or more corresponding sub-workflows according to inputs and/or one or more settings changes. As an example, session objects may provide an opportunity for a user to compare results from different settings.

As an example, inputs and/or outputs may be utilized to generate a visualization in a curtain section view. For example, consider a PETREL framework curtain section view.

As an example, an integrated workflow may be suitable for receipt of various types of resistivity data, which may be available via one or more downhole tools and/or one or more applications that may process such data. As an example, consider resistivity data and/or results that may be from a tool and/or a service that may provide for 1D inversion, 2D longitudinal inversion, 2D transverse inversion, 3D volumes, etc. As mentioned, an application such as an ultra-deep azimuthal resistivity framework may be utilized, which may be considered to be a service.

As an example, a framework may provide for generation of results according to one or more metrics. For example, consider a reservoir property metric, a reservoir character metric, a reservoir quality index, etc. As an example, a framework may provide for prediction of one or more metrics in a region proximate to a borehole being drilled such that geosteering may be controlled based at least in part on one or more of such metrics. As an example, a metric may be a fluid associated metric, a matrix associated metric, etc. As an example, one or more metrics may be combined with one or more structural features such as, for example, a layer boundary. For example, consider a visualization where one or more values for one or more metrics may be rendered for a material on one side of a boundary and/or for a material on another side of a boundary. In such an example, geosteering may be controlled on the basis of a boundary (e.g., geometrically) where knowledge of material characteristics (e.g., fluid, matrix, etc.) may be taken into account. In such an example, consider steering closer to a boundary where material on the other side of the boundary does not pose a substantial risk to a borehole being drilled; whereas, if the material on the other side of the boundary does pose a substantial risk, then a drill bit may be steered to drill a borehole that maintains a suitable distance form the boundary. As to a risk, consider a risk of borehole integrity (e.g., a material that may compromise borehole strength), consider a risk of borehole fluid influx (e.g., a material that may have a considerable amount of water), etc.

As an example, an IPG framework may provide for implementation of an automated mode where, for example, the IPG framework responds to receipt of real-time data (e.g., as transmitted using mud-pulse telemetry, wired pipe, etc.). In such an example, the IPG framework may provide for generation of output that may be readily consumed for purposes of improved geosteering, etc. (e.g., consider QC output, metric output, structural feature output, etc.).

As an example, a framework may be utilized in combination with one or more other frameworks. For example, consider utilization of the PETREL framework, which may provide for data access for pre-job modeling. As an example, during drilling, a framework may be implemented in combination with the DRILLOPS framework.

As an example, a framework may implement a machine learning model trained using data from a number of offset wells where the machine learning model may be trained and implemented without testing of the machine learning model.

As an example, a tool string may include an embedded framework that may provide for downhole automated control of one or more operations of the tool string, which may include, for example, geosteering. As an example, a rig control system (RCS) may include an embedded framework that may provide for control of one or more operations, which may include, for example, geosteering. In such an example, one or more levels of automation may be implemented such that the framework forms part of a control loop, which may be a closed control loop and/or a human-in-the-loop (HITL) type of control loop. As an example, a cloud platform may be utilized for one or more purposes. As an example, where a model is to be updated, an updated model may be provided via one or more environments for implementation in the field, for example, at a rig site environment and/or in a tool string environment.

As an example, one or more components of a framework may utilize one or more machine learning models. For example, consider a machine learning model that may be utilized to assess, compare, match, etc., one or more datasets, types of data, etc. As an example, a machine learning model may provide for generation of and/or application of a displacement field or displacement fields. For example, consider generation of multiple displacement fields, which may be local, and/or generation of an overarching displacement field that may encompass more than one borehole. As an example, resistivity data from multiple downhole tools in multiple boreholes may be acquired and utilized to generate one or more displacement fields for a single seismic data set that covers a region that includes the multiple boreholes and, for example, one or more regions ahead of such boreholes. In such an example, a displacement field may effectively morph seismic and/or other data such that structural features, properties, etc., may be more accurately represented, which, in turn, may provide for controlling directional drilling in one or more of the boreholes. As an example, an image-based approach may be utilized, which may provide for single or multidimensional displacement field generation and/or application.

As an example, voxels may be utilized where a set of resistivity data derived voxels may be of a finer resolution that a set of seismic data derived voxels. In such an example, spatial positioning of seismic data and/or data derived therefrom may be adjusted through use of one or more displacement field generation and/or application techniques. Such an approach may provide for continual improvement of representations in a subsurface environment upon receipt of resistivity data from one or more boreholes where updates may be made iteratively as data are received, whether from a single borehole or for multiple boreholes. As an example, input to a machine learning model may include a field to be morphed and a smaller region or regions of the field to be utilized for morphing the field where resolution of each of the smaller region or regions may be finer than that of the field to be morphed.

As explained, adjusting, morphing, etc., may provide for adjusting spatial locations, angles, etc., of structural features that may be relevant to directional drilling, for example, to control a drillstring to create a borehole that is within a pay zone (e.g., a reservoir zone). In various instances, a zone outside of a pay zone may be innocuous or it may be detrimental. For example, if a zone adjacent to a pay zone does not introduce a risk of borehole collapse, fluid intrusion, loss of fluid, etc., then it may be innocuous, though it may still diminish reservoir contact; whereas, a zone adjacent to a pay zone that introduces one or more risks, may be catastrophic to a project and demand abandonment of borehole and/or one or more other mitigation actions.

As an example, a framework may provide for generation and/or application of a displacement field along with generation of a borehole trajectory based at least in part on the displacement field. For example, consider output that includes a structural model, image, etc., in one or more dimensions that indicates a pay zone and one or more adjacent zones along with a borehole trajectory as a possible optimized borehole trajectory for drilling. As explained, knowledge of what lies ahead can be beneficial in drilling, particularly where the knowledge of what lies ahead extends a sufficient distance (in one or more dimensions) to meaningfully allow for practical decisions in drilling. As an example, a framework may provide for real-time and/or near real-time output that is actionable given constraints in drilling, which may include ROP, borehole quality, length of stands, number of drillpipes per stand, etc. As an example, a framework may provide for at least a stand ahead improved view of a subsurface region. As an example, consider a two to three stands ahead improved view of a subsurface region, where, for example, the view may be accompanied by uncertainty, which may facilitate decision-making, control, etc., of drilling.

As to types of machine learning models, consider one or more of a support vector machine (SVM) model, a k-nearest neighbors (KNN) model, an ensemble classifier model, a neural network (NN) model, etc. As an example, a machine learning model may be a deep learning model (e.g., deep Boltzmann machine, deep belief network, convolutional neural network, stacked auto-encoder, etc.), an ensemble model (e.g., random forest, gradient boosting machine, bootstrapped aggregation, AdaBoost, stacked generalization, gradient boosted regression tree, etc.), a neural network model (e.g., radial basis function network, perceptron, back-propagation, Hopfield network, etc.), a regularization model (e.g., ridge regression, least absolute shrinkage and selection operator, elastic net, least angle regression), a rule system model (e.g., cubist, one rule, zero rule, repeated incremental pruning to produce error reduction), a regression model (e.g., linear regression, ordinary least squares regression, stepwise regression, multivariate adaptive regression splines, locally estimated scatterplot smoothing, logistic regression, etc.), a Bayesian model (e.g., naïve Bayes, average on-dependence estimators, Bayesian belief network, Gaussian naïve Bayes, multinomial naïve Bayes, Bayesian network), a decision tree model (e.g., classification and regression tree, iterative dichotomiser 3, C4.5, C5.0, chi-squared automatic interaction detection, decision stump, conditional decision tree, M5), a dimensionality reduction model (e.g., principal component analysis, partial least squares regression, Sammon mapping, multidimensional scaling, projection pursuit, principal component regression, partial least squares discriminant analysis, mixture discriminant analysis, quadratic discriminant analysis, regularized discriminant analysis, flexible discriminant analysis, linear discriminant analysis, etc.), an instance model (e.g., k-nearest neighbor, learning vector quantization, self-organizing map, locally weighted learning, etc.), a clustering model (e.g., k-means, k-medians, expectation maximization, hierarchical clustering, etc.), etc.

As an example, a machine model, which may be a machine learning model (ML model), may be built using a computational framework with a library, a toolbox, etc., such as, for example, those of the MATLAB framework (MathWorks, Inc., Natick, Massachusetts). The MATLAB framework includes a toolbox that provides supervised and unsupervised machine learning algorithms, including support vector machines (SVMs), boosted and bagged decision trees, k-nearest neighbor (KNN), k-means, k-medoids, hierarchical clustering, Gaussian mixture models, and hidden Markov models. Another MATLAB framework toolbox is the Deep Learning Toolbox (DLT), which provides a framework for designing and implementing deep neural networks with algorithms, pretrained models, and apps. The DLT provides convolutional neural networks (ConvNets, CNNs) and long short-term memory (LSTM) networks to perform classification and regression on image, time-series, and text data. The DLT includes features to build network architectures such as generative adversarial networks (GANs) and Siamese networks using custom training loops, shared weights, and automatic differentiation. The DLT provides for model exchange various other frameworks.

As an example, the TENSORFLOW framework (Google LLC, Mountain View, CA) may be implemented, which is an open-source software library for dataflow programming that includes a symbolic math library, which may be implemented for machine learning applications that may include neural networks. As an example, the CAFFE framework may be implemented, which is a DL framework developed by Berkeley AI Research (BAIR) (University of California, Berkeley, California). As another example, consider the SCIKIT platform (e.g., scikit-learn), which utilizes the PYTHON programming language. As an example, a framework such as the APOLLO AI framework may be utilized (APOLLO. AI GmbH, Germany). As an example, a framework such as the PYTORCH framework may be utilized (Facebook AI Research Lab (FAIR), Facebook, Inc., Menlo Park, California).

As an example, a training method may include various actions that may operate on a dataset to train an ML model. As an example, a dataset may be split into training data and test data where test data may provide for evaluation. A method may include cross-validation of parameters and best parameters, which may be provided for model training.

The TENSORFLOW framework may run on multiple CPUs and GPUs (with optional CUDA (NVIDIA Corp., Santa Clara, California) and SYCL (The Khronos Group Inc., Beaverton, Oregon) extensions for general-purpose computing on graphics processing units (GPUs)). TENSORFLOW is available on 64-bit LINUX, MACOS (Apple Inc., Cupertino, California), WINDOWS (Microsoft Corp., Redmond, Washington), and mobile computing platforms including ANDROID (Google LLC, Mountain View, California) and IOS (Apple Inc.) operating system-based platforms.

TENSORFLOW computations may be expressed as stateful dataflow graphs; noting that the name TENSORFLOW derives from the operations that such neural networks perform on multidimensional data arrays. Such arrays may be referred to as “tensors”.

As an example, a device may utilize TENSORFLOW LITE (TFL) or another type of lightweight framework. For example, consider a gateway that may be in the field (e.g., on-site) and that may utilize the TFL and/or one or more other types of lightweight frameworks. The TFL framework is a set of tools that enables on-device machine learning where models may run on mobile, embedded, and IoT devices. The TFL framework is optimized for on-device machine learning, by addressing latency (no round-trip to a server), privacy (no personal data leaves the device), connectivity (Internet connectivity is demanded), size (reduced model and binary size) and power consumption (e.g., efficient inference and a lack of network connections). The TFL framework offers multiple platform support, covering ANDROID and iOS devices, embedded LINUX, and microcontrollers. The TFL framework offers diverse language support includes JAVA, SWIFT, Objective-C, C++, and PYTHON. The TFL framework may provide high performance via hardware acceleration and model optimization.

FIG. 13 shows an example of a method 1300 that includes a reception block 1310 for receiving resistivity data acquired by a downhole tool of a drillstring disposed at least in part in a borehole in a subsurface environment, where the borehole includes a downhole end, and where the borehole defines a borehole axis; a reception block 1320 for receiving seismic data for the subsurface environment, where the seismic data include spatial locations; and generation block 1330 for generating a displacement field using the resistivity data and the seismic data, wherein the displacement field specifies displacement values with respect to the spatial locations of the seismic data. As shown, the method 1300 may include a control block 1340 for controlling the drillstring to drill further into the subsurface environment to lengthen the borehole. As explained, a displacement field may be utilized for one or more purposes. As an example, a displacement field may be specified with respect to positions of data, processed data, a model, one or more properties, etc.

The method 1300 of FIG. 13 is shown as including various computer-readable storage medium (CRM) blocks 1311, 1321, 1331, and 1341 that may include processor-executable instructions that may instruct a computing system, which may be a control system, to perform one or more of the actions described with respect to the method 1300.

As shown in the example of FIG. 13, the system 1390 may include one or more computers 1392 that include one or more processors 1393, memory 1394 operatively coupled to at least one of the one or more processors 1393, instructions 1396 that may be, for example, stored in the memory 1394, and one or more interfaces 1395 (e.g., one or more network interfaces and/or other interfaces). As an example, the system 1390 may include one or more processor-readable media that include processor-executable instructions executable by at least one of the one or more processors 1393 to cause the system 1390 to perform actions such as, for example, one or more actions of the method 1300. As an example, the instructions 1396 may include instructions of one or more of the CRM blocks 1311, 1321, 1331, and 1341. The memory 1394 may be or include the one or more processor-readable media where the processor-executable instructions may be or include instructions. As an example, a processor-readable medium may be a computer-readable storage medium that is non-transitory that is not a signal and that is not a carrier wave.

As an example, the system 1390 may include subsystems. For example, the system 1390 may include a plurality of subsystems that may operate using equipment that is distributed where a subsystem may be referred to as being a system. For example, consider a downhole tool system and a surface system. As an example, operations of the blocks 1310, 1320, and 1330 of the method 1300 may be performed using a downhole tool system. The method 1300 may be implemented using, for example, a downhole system and/or a surface system, which may be a cloud-based or cloud-coupled system.

As an example, a method may include receiving resistivity data acquired by a downhole tool of a drillstring disposed at least in part in a borehole in a subsurface environment, where the borehole includes a downhole end, and where the borehole defines a borehole axis; receiving seismic data for the subsurface environment, where the seismic data include spatial locations; and generating a displacement field using the resistivity data and the seismic data, where the displacement field specifies displacement values with respect to the spatial locations of the seismic data. In such an example, generating the displacement field may include generating synthetic seismic data for a portion of the subsurface environment using the resistivity data and comparing spatial locations of the synthetic seismic data to the spatial locations of the seismic data. In such an example, comparing may include matching. As an example, one or more types of matching may be utilized. As an example, non-rigid matching may be utilized. As an example, comparing may be a type of assessing where, for example, an assessment may consider one or more dimensions. For example, consider 1D, 2D, 3D, etc. In such an approach, a displacement field may be a 1D, 2D, or 3D displacement field.

As an example, spatial locations may be direct or indirect. For example, direct may be coordinates in space such as, for example, in a Cartesian coordinate system, a cylindrical coordinate system, a spherical coordinate system, etc. As an example, spatial locations may be indirect, for example, consider time as a proxy for distance. As explained, seismic data may be acquired using known spatial locations of one or more sources and one or more receivers where such data are acquired with respect to time where time is a proxy for a spatial dimension such as, for example, depth.

As an example, a method may be performed using one or more domains. For example, consider one or more of spatial, temporal, and frequency domains. As an example, an assessment may be performed in one or more domains where a displacement field may be generated in one or more domains.

As an example, a method may include one or more of updating spatial locations of seismic data using a displacement field, adjusting one or more structural features of a structural model using a displacement field (e.g., where the one or more structural features may include at least one surface in a subsurface environment), and adjusting one or more physical properties using a displacement field.

As explained, a method may include controlling one or more field operations using a displacement field. As an example, consider at least one structural feature located in a region of a subsurface environment beyond a downhole end of a borehole being adjusted by a displacement field. In such an example, a method may include steering a drillstring to lengthen the borehole based at least in part on such adjusting. As an example, a structural feature may depend on one or more physical properties. In such an example, adjusting one or more physical properties may result in adjusting a structural feature. For example, consider a structural feature defined in part by porosity where a change in porosity (e.g., an adjustment to porosity) results in a spatial adjustment of the structural feature.

As an example, a method may include generating a displacement field in a manner that occurs responsive to receiving resistivity data. For example, consider a method where receiving the resistivity data occurs in real-time. In such an example, real-time may account for transmission of resistivity data from a downhole tool to surface, hence, some latency may exist (e.g., for mud-pulse telemetry, etc.).

As an example, a method may include determining uncertainty of a displacement field. In such an example, uncertainty may be determined using one or more statistical techniques. In such an example, a control scheme may operate using uncertainty, for example, to adjust a level of automation based on uncertainty where low uncertainty may allow for more automation and high uncertainty may allow for less automation and more intervention and/or attention by a human (e.g., a driller). As an example, to reduce uncertainty, a control scheme may instruct one or more sensors of a downhole tool to acquire data in a particular manner, for example, with an adjusted sampling rate, an increase in digital bits, etc. As an example, if ROP and/or other drillstring movement may be contributed to an increased uncertainty, a control scheme may call for adjusting drilling, drillstring movement, etc. As an example, a method may include a human-in-the-loop (HITL), which may be for purposes of oversight (e.g., safety, regulations, etc.) and/or for control.

As an example, a method may include determining uncertainty of a displacement field where, for example, the uncertainty of a displacement field and that of an interpreted resistivity model may be processed together to define uncertainty for one or more purposes. For example, a method may include adjusting one or more surfaces based at least in part on uncertainty, which may be a combined uncertainty (e.g., from one or more sources, etc.).

As an example, drilling may be controlled in a manner based at least in part on uncertainty. For example, consider reducing rate of penetration (ROP) based at least in part on uncertainty of one or more of a displacement field, a forward prediction, application of a displacement field, etc.

As an example, a method may include predicting resistivity data in a region along a borehole axis extending beyond a downhole end of a borehole using at least a portion of a displacement field. In such an example, the method may include determining uncertainty of the predicted resistivity. As an example, predicted resistivity may be provided in the form of a resistivity image, which may be rendered to a display such as, for example, a display in a driller's cabin at a site such that a driller may view the resistivity image and control directional drilling based at least in part on the resistivity image. As explained, drilling may be performed in an automated, semi-automated, manual or other manner. As an example, drilling may be improved through utilization of one or more displacement fields.

As an example, a method may include steering a drillstring to lengthen a borehole based at least in part on predicted resistivity. As explained, a displacement field may be or include an acoustic impedance displacement field. As an example, acoustic impedance may be indicative of one or more types of physical properties of a formation or formations. For example, consider a reservoir being bound by an adjacent layer where acoustic impedance may differ for the reservoir and the adjacent layer due to one or more physical properties (e.g., porosity, density, fluid type, fluid composition, etc.).

As an example, a displacement field, generated via generation of synthetic seismic data for a portion of a subsurface environment using resistivity data and a comparison of spatial locations of the synthetic seismic data to spatial locations of the seismic data, may be applied to acoustic impedance. In such an example, a displacement of acoustic properties may be a type of spatial displacement. As an example, a method may provide for adjusting acoustic impedance data, Poisson's ratio data, density data, etc. As an example, such data types may be rock property data types that characterize rock.

As explained, a method may improve rock characterization in a region adjacent to a borehole, which may extend a distance from the borehole in one or more directions. Such an approach may improve directional drilling, which may involve geosteering. For example, consider an ability to directionally drill in an improved manner that may reduce risks of exiting a pay zone, risks of compromising borehole integrity, risks of decreasing reservoir contact, etc. As explained, a method may provide for seeing ahead of an end of a borehole by a number of meters such that drilling may be improved (e.g., a better borehole, a better borehole trajectory, lesser time to drill, increased ROP, etc.).

As an example, a seismic data may be or include synthetic seismic data. For example, given a model of a subsurface environment, synthetic seismic data may be generated. In such an example, the model may be a velocity model and/or one or more other types of models. As an example, a model may be a layer cake type of model where layers are specified along with one or more physical properties as to the layers. In such an example, the one or more physical properties may determine how acoustic energy travels, reflects, absorbs, etc., in the layers. As an example, a model may provide for generation of synthetic resistivity data.

As an example, a displacement field may cover a region of a subsurface environment that includes multiple boreholes. In such an example, a method may include steering multiple drillstrings in the multiple boreholes using at least a displacement field.

As an example, a method may include generating a number of displacement fields and stitching the displacement fields together. As an example, a method may include generating synthetic seismic data (e.g., reflectivity data) from resistivity data acquired in multiple boreholes and utilizing such synthetic data to generate a displacement field that may encompass one or more of the multiple boreholes.

As an example, a method may include extrapolating a displacement field to cover a larger region of a subsurface environment. In such an example, the larger region may be larger than a region of resistivity data.

As an example, a method may include generating an interpreted resistivity model using at least a displacement field. In such an example, the interpreted resistivity model may delineate resistivity zones in a subsurface region. As an example, an interpreted resistivity model may define formation strata in a subsurface region. As an example, an interpreted resistivity model may define properties in a subsurface region. As an example, an interpreted resistivity model may define structural geometry laterally varying along at least a portion of a borehole in a subsurface region. As an example, a method may include re-generating a displacement field using at least an interpreted resistivity model.

As an example, a method may include generating a subsurface model using at least a displacement field and re-generating the displacement field using at least the subsurface model. In such an example, re-generating may include generating synthetic seismic data using the surface model. As an example, a subsurface model may cover a region of a subsurface environment that is not covered by seismic data. In such an example, the region that is not covered may include internal seismic data voids or internal noisy seismic data. As an example, a region that is not covered may be exterior to a region covered by seismic data.

As an example, resistivity data may include one or more of acquired resistivity data and interpreted resistivity data. As an example, seismic data may include one or more of acquired seismic data and synthetic seismic data. In such an example, resistivity data may include one or more of acquired resistivity data and interpreted resistivity data.

As an example, a system may include a processor; memory accessible to the processor; and processor-executable instructions stored in the memory and executable by the processor to instruct the system to: receive resistivity data acquired by a downhole tool of a drillstring disposed at least in part in a borehole in a subsurface environment, where the borehole includes a downhole end, and where the borehole defines a borehole axis; receive seismic data for the subsurface environment, where the seismic data include spatial locations; and generate a displacement field using the resistivity data and the seismic data, where the displacement field specifies displacement values with respect to the spatial locations of the seismic data.

As an example, one or more non-transitory computer-readable storage media may include processor-executable instructions executable to instruct a processor to: receive resistivity data acquired by a downhole tool of a drillstring disposed at least in part in a borehole in a subsurface environment, where the borehole includes a downhole end, and where the borehole defines a borehole axis; receive seismic data for the subsurface environment, where the seismic data include spatial locations; and generate a displacement field using the resistivity data and the seismic data, where the displacement field specifies displacement values with respect to the spatial locations of the seismic data.

As an example, one or more computer-readable storage media may include processor-executable instructions to instruct a computing system to perform one or more methods. In such an example, the one or more computer-readable storage media may be a program product (e.g., a computer program product, a computer system program product, etc.).

In some embodiments, a method or methods may be executed by a computing system. FIG. 14 shows an example of a system 1400 that may include one or more computing systems 1401-1, 1401-2, 1401-3 and 1401-4, which may be operatively coupled via one or more networks 1409, which may include wired and/or wireless networks.

As an example, a system may include an individual computer system or an arrangement of distributed computer systems. In the example of FIG. 14, the computer system 1401-1 may include one or more sets of instructions 1402, which may be or include processor-executable instructions, for example, executable to perform various tasks (e.g., receiving information, requesting information, processing information, simulation, outputting information, etc.).

As an example, a set of instructions may be executed independently, or in coordination with, one or more processors 1404, which is (or are) operatively coupled to one or more storage media 1406 (e.g., via wire, wirelessly, etc.). As an example, one or more of the one or more processors 1404 may be operatively coupled to at least one of one or more network interface 1407. In such an example, the computer system 1401-1 may transmit and/or receive information, for example, via the one or more networks 1409 (e.g., consider one or more of the Internet, a private network, a cellular network, a satellite network, etc.). As shown, one or more other components 1408 may be included.

As an example, the computer system 1401-1 may receive from and/or transmit information to one or more other devices, which may be or include, for example, one or more of the computer systems 1401-2, etc. A device may be located in a physical location that differs from that of the computer system 1401-1. As an example, a location may be, for example, a processing facility location, a data center location (e.g., server farm, etc.), a rig location, a wellsite location, a downhole location, etc.

As an example, a processor may be or include a microprocessor, microcontroller, processor component or subsystem, programmable integrated circuit, programmable gate array, or another control or computing device.

As an example, the storage media 1406 may be implemented as one or more computer-readable or machine-readable storage media. As an example, storage may be distributed within and/or across multiple internal and/or external enclosures of a computing system and/or additional computing systems.

As an example, a storage medium or storage media may include one or more different forms of memory including semiconductor memory devices such as dynamic or static random access memories (DRAMs or SRAMs), erasable and programmable read-only memories (EPROMs), electrically erasable and programmable read-only memories (EEPROMs) and flash memories, magnetic disks such as fixed, floppy and removable disks, other magnetic media including tape, optical media such as compact disks (CDs) or digital video disks (DVDs), BLUERAY disks, or other types of optical storage, or other types of storage devices.

As an example, a storage medium or media may be located in a machine running machine-readable instructions, or located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

As an example, various components of a system such as, for example, a computer system, may be implemented in hardware, software, or a combination of both hardware and software (e.g., including firmware), including one or more signal processing and/or application specific integrated circuits.

As an example, a system may include a processing apparatus that may be or include a general-purpose processors or application specific chips (e.g., or chipsets), such as ASICs, FPGAs, PLDs, or other appropriate devices.

As an example, a device may be a mobile device that includes one or more network interfaces for communication of information. For example, a mobile device may include a wireless network interface (e.g., operable via IEEE 802.11, ETSI GSM, BLUETOOTH, satellite, etc.). As an example, a mobile device may include components such as a main processor, memory, a display, display graphics circuitry (e.g., optionally including touch and gesture circuitry), a SIM slot, audio/video circuitry, motion processing circuitry (e.g., accelerometer, gyroscope), wireless LAN circuitry, smart card circuitry, transmitter circuitry, GPS circuitry, and a battery. As an example, a mobile device may be configured as a cell phone, a tablet, etc. As an example, a method may be implemented (e.g., wholly or in part) using a mobile device. As an example, a system may include one or more mobile devices.

As an example, a system may be a distributed environment, for example, a so-called “cloud” environment where various devices, components, etc. interact for purposes of data storage, communications, computing, etc. As an example, a device or a system may include one or more components for communication of information via one or more of the Internet (e.g., where communication occurs via one or more Internet protocols), a cellular network, a satellite network, etc. As an example, a method may be implemented in a distributed environment (e.g., wholly or in part as a cloud-based service).

As an example, information may be input from a display (e.g., consider a touchscreen), output to a display or both. As an example, information may be output to a projector, a laser device, a printer, etc. such that the information may be viewed. As an example, information may be output stereographically or holographically. As to a printer, consider a 2D or a 3D printer. As an example, a 3D printer may include one or more substances that may be output to construct a 3D object. For example, data may be provided to a 3D printer to construct a 3D representation of a subterranean formation. As an example, layers may be constructed in 3D (e.g., horizons, etc.), geobodies constructed in 3D, etc. As an example, holes, fractures, etc., may be constructed in 3D (e.g., as positive structures, as negative structures, etc.).

Although only a few examples have been described in detail above, those skilled in the art will readily appreciate that many modifications are possible in the examples. Accordingly, all such modifications are intended to be included within the scope of this disclosure as defined in the following claims. In the claims, means-plus-function clauses are intended to cover the structures described herein as performing the recited function and not only structural equivalents, but also equivalent structures. Thus, although a nail and a screw may not be structural equivalents in that a nail employs a cylindrical surface to secure wooden parts together, whereas a screw employs a helical surface, in the environment of fastening wooden parts, a nail and a screw may be equivalent structures.