TECHNIQUES FOR GENERATING AND/OR TRAINING A MODEL FOR PREDICTING PARAMETERS ASSOCIATED WITH WELL OPERATIONS

Description

CROSS-REFERENCE TO RELATED APPLICATION

The present disclosure is related to, and claims priority to, U.S. Provisional Patent Application Ser. No. 63/718,824, titled “Techniques for Generating and/or Training a Model for Predicting Parameters Associated with Well Operations,” which was filed on Nov. 11, 2024, and which is herein incorporated by reference in its entirety for all purposes.

BACKGROUND

The present disclosure generally relates to systems and methods for generating and/or training models with downhole data associated with a first well operation in order to determine downhole parameters of subsequent well operations using only surface data.

This section is intended to introduce the reader to various aspects of art that may be related to various aspects of the present techniques, which are described and/or claimed below. This discussion is believed to be helpful in providing the reader with background information to facilitate a better understanding of the various aspects of the present disclosure. Accordingly, it should be understood that these statements are to be read in this light, and not as an admission of any kind.

Coiled tubing is a technology that has been expanding its range of applications since its introduction to the oil industry in the 1960s. Its ability to pass through completion tubulars, as well as the wide array of tools and technologies that can be used in conjunction with it, make it a very versatile technology.

A typical coiled tubing apparatus includes surface pumping facilities, a coiled tubing string mounted on a reel, a method to convey the coiled tubing into and out of the wellbore, such as an injector head or the like, and surface control apparatus at the wellhead. Coiled tubing has been utilized for performing well treatment and/or well intervention operations in existing wellbores such as, but not limited to, hydraulic fracturing operations, matrix acidizing operations, milling operations, perforating operations, cleanout operations, coiled tubing drilling operations, nitrogen kick-off operations, fishing operations, zonal isolation operations, and so forth.

Coiled tubing operations can be relatively complex operations that, in order to successfully accomplish their goals, need to account for various factors for success that include, but are not limited to, wellbore hydraulics, movement of the coiled tubing, reservoir flow and coupling between the wellbore and the reservoir, nitrified fluids injection, solid transport, phase changes, and temperature evolution and distribution along the wellbore.

It remains desirable to provide improvements in oilfield surface equipment and/or downhole assemblies and methods of using such equipment or assemblies such as, but not limited to, methods for optimizing coiled tubing operations.

SUMMARY

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 determining or limiting the scope of the claimed subject matter as set forth in the claims.

In certain embodiments, a tangible, non-transitory computer readable medium includes instructions, that when executed by one or more processors, cause the one or more processors to perform operations comprising: receiving downhole data associated with downhole parameters of a first well operation from one or more sources; generating and/or training a tubing force model (TFM) based on the downhole data associated with the first well operation; monitoring one or more surface parameters associated with a second well operation; calculating and/or estimating weight on bit (WOB) based on the TFM and the one or more surface parameters associated with the second well operation; calculating and/or estimating a downhole parameter for the second well operation based on the calculated and/or estimated WOB; and controlling an operating parameter of the second well operation based at least in part on calculated and/or estimated downhole parameter.

In other embodiments, a method includes receiving downhole data associated with downhole parameters of a first well operation from one or more sources. The method also includes generating and/or training a TFM based on the downhole data associated with the first well operation. The method further includes monitoring one or more surface parameters associated with a second well operation. In addition, the method includes calculating and/or estimating WOB based on the TFM and the one or more surface parameters associated with the second well operation. The method also includes calculating and/or estimating a downhole parameter for the second well operation based on the calculated and/or estimated WOB. The method further includes controlling an operating parameter of the second well operation based at least in part on calculated and/or estimated downhole parameter.

In yet other embodiments, a system includes one or more processors and a memory, including instructions, that when executed by the one or more processors, cause the one or more processors to perform operations comprising: receiving downhole data associated with downhole parameters of a first well operation from one or more sources; generating and/or training a TFM based on the downhole data associated with the first well operation; monitoring one or more surface parameters associated with a second well operation; calculating and/or estimating WOB based on the TFM and the one or more surface parameters associated with the second well operation; calculating and/or estimating a downhole parameter for the second well operation based on the calculated and/or estimated WOB; and controlling an operating parameter of the second well operation based at least in part on calculated and/or estimated downhole parameter.

Various refinements of the features noted above may be undertaken in relation to various aspects of the present disclosure. Further features may also be incorporated in these various aspects as well. These refinements and additional features may exist individually or in any combination. For instance, various features discussed below in relation to one or more of the illustrated embodiments may be incorporated into any of the above-described aspects of the present disclosure alone or in any combination. The brief summary presented above is intended to familiarize the reader with certain aspects and contexts of embodiments of the present disclosure without limitation to the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

The subject disclosure is further described in the following detailed description, and the accompanying drawings and schematics of non-limiting embodiments of the subject disclosure. The features depicted in the figures are not necessarily shown to scale. Certain features of the embodiments may be shown exaggerated in scale or in somewhat schematic form, and some details of elements may not be shown in the interest of clarity and conciseness. These and other features, aspects, and advantages of the present disclosure will become better understood when the following detailed description is read with reference to the accompanying drawings in which like characters represent like parts throughout the drawings, wherein:

FIG. 1 illustrates a schematic diagram of a well system, in accordance with embodiments of the present disclosure;

FIG. 2 illustrates a well control system including a surface processing system to control the well system of FIG. 1, in accordance with embodiments of the present disclosure;

FIG. 3 illustrates an example procedure that may be followed during a first coiled tubing (CT) run, in accordance with embodiments of the present disclosure;

FIG. 4 is a graph of a match between a tubing forces module (TFM) and measured CT weight at the surface, in accordance with embodiments of the present disclosure;

FIG. 5 illustrates an exemplary method of generating a pseudo-gradient channel based on pressure measurement from a downhole tool and true vertical depths (TVD), in accordance with embodiments of the present disclosure;

FIG. 6 illustrates an exemplary method of fine-tuning inputs in the TFM using a data-informed pseudo-gradient, in accordance with embodiments of the present disclosure;

FIG. 7 illustrates a match between TFM and measured CT weight after improving inputs based on the data-informed pseudo-gradient of FIG. 6, in accordance with embodiments of the present disclosure;

FIG. 8 illustrates a method of TFM simulation of CT weight slackoff required to generate a downhole weight on bit (WOB), in accordance with embodiments of the present disclosure;

FIG. 9A illustrates a relationship between downhole torque and downhole thrust while milling a downhole plug, in accordance with embodiments of the present disclosure;

FIG. 9B illustrates a relationship between downhole thrust and circulation pressure while milling a downhole plug, in accordance with embodiments of the present disclosure;

FIG. 9C illustrates a relationship between downhole torque and circulation pressure while milling a downhole plug, in accordance with embodiments of the present disclosure;

FIG. 10 illustrates a time-gated acquisition of milling, in accordance with embodiments of the present disclosure; and

FIG. 11 is a method of generating and/or training a TFM model based on downhole measurement data and estimating one or more parameters associated with one or more coiled tubing operations in real time, in accordance with embodiments of the present disclosure.

DETAILED DESCRIPTION

Certain embodiments commensurate in scope with the present disclosure are summarized below. These embodiments are not intended to limit the scope of the disclosure, but rather these embodiments are intended only to provide a brief summary of certain disclosed embodiments. Indeed, the present disclosure may encompass a variety of forms that may be similar to or different from the embodiments set forth below.

As used herein, the term “coupled” or “coupled to” may indicate establishing either a direct or indirect connection (e.g., where the connection may not include or include intermediate or intervening components between those coupled), and is not limited to either unless expressly referenced as such. The term “set” may refer to one or more items. Wherever possible, like or identical reference numerals are used in the figures to identify common or the same elements. The figures are not necessarily to scale and certain features and certain views of the figures may be shown exaggerated in scale for purposes of clarification.

As used herein, the terms “inner” and “outer”; “up” and “down”; “upper” and “lower”; “upward” and “downward”; “above” and “below”; “inward” and “outward”; and other like terms as used herein refer to relative positions to one another and are not intended to denote a particular direction or spatial orientation. The terms “couple,” “coupled,” “connect,” “connection,” “connected,” “in connection with,” and “connecting” refer to “in direct connection with” or “in connection with via one or more intermediate elements or members.”

In addition, as used herein, the terms “real time”, “real-time”, or “substantially real time” may be used interchangeably and are intended to described operations (e.g., computing operations) that are performed without any human-perceivable interruption between operations. For example, as used herein, data relating to the systems described herein may be collected, transmitted, and/or used in control computations in “substantially real time” such that data readings, data transfers, and/or data processing steps occur once every second, once every 0.1 second, once every 0.01 second, or even more frequent, during operations of the systems (e.g., while the systems are operating). In addition, as used herein, the terms “automatic” and “automated” are intended to describe operations that are performed or caused to be performed, for example, by a processing system (i.e., solely by the processing system, without human intervention).

In addition, as used herein, the term “approximately equal to” may be used to mean values that are relatively close to each other (e.g., within 5%, within 2%, within 1%, within 0.5%, or even closer, of each other). Similarly, as used herein, the terms “constant” or “relatively constant” or “substantially constant” may be used to refer to values that are held relatively close to a desired value (e.g., within 5%, within 2%, within 1%, within 0.5%, or even closer, of each other).

Furthermore, when introducing elements of various embodiments of the present disclosure, the articles “a,” “an,” and “the” are intended to mean that there are one or more of the elements. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that there may be additional elements other than the listed elements. Additionally, it should be understood that references to “one embodiment,” “an embodiment,” or “some embodiments” of the present disclosure are not intended to be interpreted as excluding the existence of additional embodiments that also incorporate the recited features. Furthermore, the phrase A “based on” B is intended to mean that A is at least partially based on B. Moreover, unless expressly stated otherwise, the term “of” is intended to be inclusive (e.g., logical OR) and not exclusive (e.g., logical XOR). In other words, the phrase A “of” B is intended to mean A, B, or both A and B.

Certain terms are used throughout the description and claims to refer to particular features or components. As one skilled in the art will appreciate, different persons may refer to the same feature or component by different names. This document does not intend to distinguish between components or features that differ in name, but not function.

As mentioned above, certain embodiments of the present disclosure are directed to generating and/or training a model of downhole measurements for coiled tubing surface data. After the model is generated and trained, a computing system may use the model to display one or more parameters associated with one or more coiled tubing operations in real time. For example, such parameters may include a downhole weight on bit (WOB) measurement, a torque measurement, or both.

In conventional techniques that utilize a downhole tool string lacking telemetry, operators run an operation based on only surface measurements without any actual measurements of what is occurring downhole. This requires an extreme amount of skill and experience to perform an effective operation. By tying data from previous runs together and using relationships, such as torque and thrust from previous runs, the model described herein can update continuously based on target depths and be utilized by a computing system to provide real-time downhole measurement-based estimates to an operator.

In certain embodiments, a milling operation can be automated based on the model. For example, a computing system may use the model to predict one or more optimum downhole parameters and control one or more operations of surface equipment based on the predicted optimum downhole parameters. Accordingly, such techniques would reduce or remove the training and/or experience required for operators to perform such operations. Additionally, such techniques can increase tool string utilization. For example, downhole tool strings can be used to gather calibration information on initial runs to validate a model. When the model has been validated, subsequent runs can be performed without telemetry. The downhole tool strings may then be used at another location to gather data, thereby increasing operational efficiency.

With the foregoing in mind, FIG. 1 illustrates a schematic diagram of an example well system 10. As illustrated, in certain embodiments, a downhole tool string 12 may be run into a wellbore 14 that traverses a hydrocarbon-bearing formation 16 (i.e., reservoir). While certain elements of the well system 10 are illustrated in FIG. 1, other elements of the well system 10 (e.g., blow-out preventers, wellhead “tree”, etc.) may be omitted for clarity of illustration. In certain embodiments, the well system 10 includes an interconnection of pipes, including vertical and/or horizontal casings 18, coiled tubing 20, and so forth, that connect to a surface facility 22 at the surface 24 of the well system 10. In certain embodiments, the coiled tubing 20 extends inside the casing 18 and terminates at a tubing head (not shown) at or near the surface 24. In addition, in certain embodiments, the casing 18 contacts the wellbore 14 and terminates at a casing head (not shown) at or near the surface 24. Although described herein as using coiled tubing 20 as the means of conveyance of the downhole tool string 12, in other embodiments, other means of conveyance such as other types of cables (e.g., wireline cables) may be used by the well system 10.

In certain embodiments, a bottom hole assembly (“BHA”) 26 may be run inside the casing 18 by the coiled tubing 20. As illustrated in FIG. 1, in certain embodiments, the BHA 26 may include a downhole motor 28 that operates to rotate a drilling/milling bit 30 (e.g., during drilling/milling operations) or other downhole well tools. In certain embodiments, the downhole motor 28 may be driven by hydraulic forces carried in fluid supplied from the surface 24 of the well system 10. In certain embodiments, the BHA 26 may be connected to the coiled tubing 20, which is used to run the BHA 26 to a desired location within the wellbore 14. It is also contemplated that, in certain embodiments, the rotary motion of the drilling/milling bit 30 may be driven by rotation of the coiled tubing 20 effectuated by a rotary table or other surface-located rotary actuator. In such embodiments, the downhole motor 28 may be omitted.

In certain embodiments, the coiled tubing 20 may also be used to deliver fluid 32 to the drilling/milling bit 30 through an interior of the coiled tubing 20 to aid in the drilling/milling process and carry cuttings and possibly other fluid or solid components in return fluid 34 that flows up the annulus between the coiled tubing 20 and the casing 18 (or via a return flow path provided by the coiled tubing 20, in certain embodiments) for return to the surface facility 22. It is also contemplated that the return fluid 34 may include remnant proppant (e.g., sand) or possibly rock fragments that result from a hydraulic fracturing application, and flow within the well system 10. Under certain conditions, fracturing fluid and possibly hydrocarbons (oil and/or gas), proppants and possibly rock fragments may flow from the fractured formation 16 through perforations in a newly opened interval and back to the surface 24 of the well system 10 as part of the return fluid 34. In certain embodiments, the BHA 26 may be supplemented behind the rotary drill by an isolation device such as, for example, an inflatable packer that may be activated to isolate the zone below or above it and enable local pressure tests. In addition, in certain embodiments, the BHA 26 may include a tractor system that is capable of improving reach and WOB of the BHA 26 during coiled tubing (CT) operations.

As such, in certain embodiments, the well system 10 may include a downhole well tool 36 that is moved along the wellbore 14 via the coiled tubing 20. In certain embodiments, the downhole well tool 36 may include a variety of drilling/milling tools coupled with the coiled tubing 20. In the illustrated embodiment, the downhole well tool 36 includes the drilling/milling bit 30, which may be powered by the downhole motor 28 (e.g., a positive displacement motor (PDM), or other hydraulic motor) of the BHA 26. In certain embodiments, the wellbore 14 may be an openhole wellbore or a cased wellbore defined by the casing 18. In addition, in certain embodiments, the wellbore 14 may be vertical or horizontal or inclined. It should be noted the downhole well tool 36 may be part of various types of BHAs 26 coupled to the coiled tubing 20.

As also illustrated in FIG. 1, in certain embodiments, the well system 10 may include a downhole sensor package 38 having multiple downhole sensors 40. In certain embodiments, the sensor package 38 may be mounted along the downhole tool string 12, although certain downhole sensors 40 may be positioned at other downhole locations in other embodiments. In addition, in certain embodiments, downhole sensors 40 disposed on the coiled tubing 20 may be configured to detect downhole flow rates, downhole temperatures, and downhole pressures, and so forth, in the wellbore 14. In addition, in certain embodiments, downhole sensors 40 disposed on the casing 18 may be configured to detect downhole temperatures, downhole pressures, axial load (or “weight”) and torque applied on the drilling/milling bit 30, casing collar locators (CCLs), resistivity, and so forth, in the wellbore 14.

In certain embodiments, data from the downhole sensors 40 may be relayed uphole to a surface processing system 42 (e.g., a computer-based processing system) disposed at the surface 24 and/or other suitable location of the well system 10. In certain embodiments, the data may be relayed uphole in substantially real time (e.g., relayed while it is detected by the downhole sensors 40 during operation of the downhole well tool 36) via a wired or wireless telemetric control line 44, and this real-time data may be referred to as edge data. In certain embodiments, the telemetric control line 44 may be in the form of an electrical line, fiber-optic line, or other suitable control line for transmitting data signals. In certain embodiments, the telemetric control line 44 may be routed along an interior of the coiled tubing 20, within a wall of the coiled tubing 20, or along an exterior of the coiled tubing 20. In addition, as described in greater detail herein, additional data (e.g., surface data) may be supplied by surface sensors 46 and/or stored in a memory location 48. By way of example, historical data and other useful data may be stored in the memory location 48 such as a cloud storage 50.

As illustrated, in certain embodiments, the coiled tubing 20 may be deployed from a CT reel 55 of a CT unit 52 and delivered downhole via an injector head 54. In certain embodiments, the injector head 54 may be controlled to slack off or pick up the coiled tubing 20 so as to control the tubing string weight and, thus, the WOB acting on the drilling/milling bit 30 (or the downhole well tool 36). In certain embodiments, the downhole well tool 36 may be moved along the wellbore 14 via the coiled tubing 20 under control of the injector head 54 so as to apply a desired tubing weight and, thus, to achieve a desired rate of penetration (ROP) as the drilling/milling bit 30 is operated. Depending on the specifics of a given application, various types of data may be collected downhole, and transmitted to the surface processing system 42 in substantially real time to facilitate improved operation of the downhole well tool 36. For example, as described in greater detail herein, the data may be used to fully or partially automate downhole operations, to optimize the downhole operations, and/or to provide more accurate predictions regarding components or aspects of the downhole operations.

In certain embodiments, fluid 32 may be delivered downhole under pressure from a pump unit 56. In certain embodiments, the fluid 32 may be delivered by the pump unit 56 through the downhole motor 28 to power the downhole motor 28 and, thus, the drilling/milling bit 30. In certain embodiments, the return fluid 34 is returned uphole, and this flow back of the return fluid 34 is controlled by suitable flowback equipment 58. In certain embodiments, the flowback equipment 58 may include chokes and other components/equipment used to control flow back of the return fluid 34 in a variety of applications, including well treatment applications.

As described in greater detail herein, the CT unit 52, the injector head 54, the pump unit 56, and the flowback equipment 58 may include advanced surface sensors 46, actuators, and local controllers, such as PLCs, which may cooperate together to provide sensor data to receive control signals from, and generate local control signals based on communications with, respectively, the surface processing system 42. In certain embodiments, as described in greater detail herein, the surface sensors 46 may include flow rate, pressure, and fluid rheology sensors 46, among other types of sensors. In addition, as described in greater detail herein, the actuators may include actuators for pump and choke control of the pump unit 56 and the flowback equipment 58, respectively, among other types of actuators.

In certain embodiments, surface sensors 46 of the CT unit 52 may be configured to detect positions of the coiled tubing 20, weights of the coiled tubing 20, and so forth. In addition, in certain embodiments, surface sensors 46 of the injector head 54 may be configured to detect wellhead pressure, and so forth. In addition, in certain embodiments, surface sensors 46 of the pump unit 56 may be configured to detect pump pressures, pump flow rates, and so forth. In addition, in certain embodiments, surface sensors 46 of the flowback equipment 58 may be configured to detect fluids production rates, solids production rates, and so forth.

FIG. 2 illustrates a well control system 60 that may include the surface processing system 42 to control the well system 10 described herein. In certain embodiments, the surface processing system 42 may include one or more analysis modules 62 (e.g., a program of computer-executable instructions and associated data) that may be configured to perform various functions of the embodiments described herein. In certain embodiments, to perform these various functions, the one or more analysis modules 62 may execute on one or more processors 64 of the surface processing system 42, which may be connected to one or more storage media 66 of the surface processing system 42. Indeed, in certain embodiments, the one or more analysis modules 62 may be stored in the one or more storage media 66.

In certain embodiments, the computer-executable instructions of the one or more analysis modules 62, when executed by the one or more processors 64, may cause the one or more processors 64 to generate one or more models (e.g., including the FM described in greater detail herein). Such models may be used by the surface processing system 42 to predict values of operational parameters that may or may not be measured (e.g., using gauges, sensors) during well operations.

In certain embodiments, the one or more processors 64 may include a microprocessor, a microcontroller, a processor module or subsystem, a programmable integrated circuit, a programmable gate array, a digital signal processor (DSP), or another control or computing device. In certain embodiments, the one or more processors 64 may include machine learning and/or artificial intelligence (AI) based processors. In certain embodiments, the one or more storage media 66 may be implemented as one or more non-transitory computer-readable or machine-readable storage media. In certain embodiments, the one or more storage media 66 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); or other types of storage devices. Note that the computer-executable instructions and associated data of the analysis module(s) 62 may be provided on one computer-readable or machine-readable storage medium of the storage media 66, or alternatively, may be provided on multiple computer-readable or machine-readable storage media distributed in a large system having possibly plural nodes. Such computer-readable or machine-readable storage medium or media are considered to be part of an article (or article of manufacture), which may refer to any manufactured single component or multiple components. In certain embodiments, the one or more storage media 66 may be located either in the machine running the machine-readable instructions or may be located at a remote site from which machine-readable instructions may be downloaded over a network for execution.

In certain embodiments, the processor(s) 64 may be connected to a network interface 68 of the surface processing system 42 to allow the surface processing system 42 to communicate with the multiple downhole sensors 40 and surface sensors 46 described herein, as well as communicate with the actuators 70 and/or PLCs 72 of the surface equipment 74 (e.g., the CT unit 52, the injector head 54, the pump unit 56, the flowback equipment 58, and so forth) and of the downhole equipment 76 (e.g., the BHA 26, the downhole motor 28, the drilling/milling bit 30, the downhole well tool 36, and so forth) for the purpose of controlling operation of the well system 10, as described in greater detail herein. In certain embodiments, the network interface 68 may also facilitate the surface processing system 42 to communicate data to the cloud storage 50 (or other wired and/or wireless communication network) to, for example, archive the data or to enable external computing systems 78 to access the data and/or to remotely interact with the surface processing system 42.

It should be appreciated that the well control system 60 illustrated in FIG. 2 is only one example of a well control system, and that the well control system 60 may have more or fewer components than shown, may combine additional components not depicted in the embodiment of FIG. 2, and/or the well control system 60 may have a different configuration or arrangement of the components depicted in FIG. 2. In addition, the various components illustrated in FIG. 2 may be implemented in hardware, software, or a combination of both hardware and software, including one or more signal processing and/or application specific integrated circuits. Furthermore, the operations of the well control system 60 as described herein may be implemented by running one or more functional modules in an information processing apparatus such as application specific chips, such as application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), programmable logic devices (PLDs), systems on a chip (SOCs), or other appropriate devices. These modules, combinations of these modules, and/or their combination with hardware are all included within the scope of the embodiments described herein.

As discussed above, certain embodiments of the present disclosure are directed to generating and/or training a model of downhole measurements for CT surface data. After the model is generated and trained, a computing system (e.g., the surface processing system 42, the surface processing system 42 and one or more external computing systems 78 working together, and so forth) may use the model to display one or more parameters associated with one or more CT operations in real time. In certain embodiments, the model can be generated and/or trained over a first CT run.

FIG. 3 illustrates an example procedure 80 that may be followed during the first CT run. In particular, as illustrated, the procedure 80 may include the steps of: (1A) gathering measurement data, such as thrust, torque, pressure, and/or the like, (1B) using the measurement data to fine-tune a model, and (1C) generating a thrust/torque curve. In certain embodiments, the model is a WOB tubing forces module (TFM). The trained model can then be used to generate downhole torque, WOB, or the like, based on surface measurements only for a second CT run (or any subsequent CT runs). For example, an operator may first pick a target depth for the TFM, such as a desired milling depth for a plug, restriction, or the like. For a given loss (delta) of CT weight (measured slack off) at the current CT depth, TFM can be used iteratively to estimate downhole WOB. The calculated downhole WOB can then be plugged into the torque/thrust curve to estimate the downhole torque during milling. This relationship can then be used iteratively when engaging an object of minimal length to provide an accurate torque measurement without the need for any downhole sensors 40. When the downhole object is removed during milling, a new target depth can be used in the TFM model to repeat the algorithm described above. Accordingly, such embodiments of the present disclosure improve data measurement and acquisition (e.g., downhole WOB, torque, and so forth) without the need of expensive downhole tool strings 12 (e.g., having downhole sensors 40). A software program can perform such embodiments in real time without the need for manual iterations that would update the TFM based on target depths. This can be supplemented by downhole data in previous runs or a data library to pull the information from similar well completions.

In step 1A of the procedure 80 illustrated in FIG. 3, the measurement data may be gathered using one or more data sources. For instance, the data sources may include software or devices configured to gather downhole data and/or surface data (e.g., including the downhole and/or the surface sensors 40, 46, import downhole data and/or surface data from another source, integrate downhole data and/or surface data, or a combination thereof. In certain embodiments, the gathered data may include surface data integrated with downhole data. For instance, a data source may integrate the surface data with the downhole data before the data is received from the data source. Alternatively, the surface data and the downhole data may be integrated after the data is received from the data source. For example, a memory tool (e.g., as part of a downhole well tool 36) may receive the surface data and the downhole data from one or more sources and include one or more workflows that integrate the surface data with the downhole data.

In Step 1B of the procedure 80 illustrated in FIG. 3, the gathered data from the first CT run can be used to improve a match of expected tubing forces simulated by a tubing forces module (TFM). FIG. 4 is a graph 82 of a match between a TFM and measured coiled tubing (CT) weight at the surface 24. FIG. 4 shows an initial match occurring between the measured CT weight during a job (CT WEIGHT) and the TFM's simulated run-in-hole (RIH) CT weight (curve 84) and pull-out-of-hole (POOH) CT weight (curve 86). There are simpler instances where the match is considered adequate based on assumed conditions at the surface 24, in the coiled tubing 20, and in the wellbore 14 (i.e., initial TFM inputs); and there are more complex instances where the match is considered relatively poor.

As shown in FIG. 4, the match is relatively good. In spite of having a good match, this case study shows how informed inputs, such as those supplied by downhole data on an initial run (e.g. first run including a first well operation, as described herein), such as the gathered data described above, can be used to improve the model without any forced curve matching. For instance, in certain embodiments, the fluid pseudo-gradient can be calculated by transforming time-gated data to depth-gated data, using a hole survey to relate measured depths (MD) to true vertical depths (TVD), and applying a conventional hydrostatic equation [e.g., Hydrostatic pressure (in psi)=0.052×PPG (in lbs/gal)×TVD (in ft)] to estimate a pseudo-gradient. FIG. 5 shows an exemplary method 88 for executing this calculation, wherein the pseudo-gradient is calculated based on recorded time-gated data that includes time-corrected downhole pressure from the memory tool, measured depth from the surface acquisition system, correlations of measured depth to true vertical depth, and conversation factors, in accordance with certain embodiments described herein.

FIG. 6 illustrates an exemplary method 90 for updating inputs of the TFM using the pseudo-gradient, in accordance with certain embodiments described herein. The expected outcome of updating the TFM inputs with better information is a more accurate prediction of the tubing forces downhole. In this case, the match improved from the illustrated example in FIG. 4 to a superior match is illustrated in the graph 92 of FIG. 7. In certain embodiments, surface and downhole data can be leveraged to provide a more accurate prediction of tubing forces downhole. Surface and downhole data can also be leveraged to provide a more accurate prediction of fluids, flow rates, and pressures downhole. In general, surface and downhole data can be leveraged to improve simulations.

Downhole data gathered from a memory of downhole well tool 36 is often inaccessible, but tends to be more economically viable than real-time data from telemetry-enabled tools 36. Thus, downhole data gathered from downhole well tools 36 can be used to improve models, such as a TFM and, therefore, produce more accurate predictions in subsequent runs. By improving the accuracy of subsequent runs, intervention may be executed more efficiently, effectively, and safely.

As an example of the value of a more accurate simulation, consider TFM's ability to predict CT weight slackoff required to generate WOB downhole. FIG. 8 illustrates a method 94 of TFM simulation of CT weight slackoff required to generate a downhole WOB. Step 1C of the procedure 80 illustrated in FIG. 3 generally requires the availability of downhole torque and thrust (sometimes referred to as axial loading, or tension/compression) data. After the first run, downhole torque, downhole thrust, and surface circulating pressure data may be analyzed at intervals of interest (e.g., during the milling of a specific mechanical plug) to extract one or more relationships between those parameters. For example, FIGS. 9A, 9B, and 9C illustrate relationships 96, 98, 100 between (a) downhole torque and downhole thrust (relationship 96), (b) downhole thrust and circulation pressure (relationship 98), and (c) downhole torque and circulation pressure (relationship 100), for example, while milling a downhole plug. Each sample set includes a respective linear regression. However, other relationships may exist (e.g., non-linear, polynomial, piecewise) or other methods may exist for defining these relationships (e.g., transfer function, neural networks). Similarly, a relationship may exist between other parameters including pump rate, circulation pressure, CT weight, thrust, and torque, or the like.

After having defined relationships, such as those illustrated in FIGS. 9A, 9B, and 9C, one or more conversions may be made in post-job and in substantially real time during subsequent runs in the same or comparable wells. Table 1 provides examples of such conversions based on the relationships 96, 98, 100 presented in FIGS. 9A, 9B, and 9C. Calculations of such conversions can be performed using the function that relates one channel to the other. As discussed above, the exemplary relationships 96, 98, 100 illustrated in FIGS. 9A, 9B, and 9C are established through a linear regression.

On its own, the relationship between DH Thrust and DH Torque (as per Conversion #1 in Table 1) is not particularly useful by itself, but coupled with a surface measurement that is related to a downhole parameter, it can prove somewhat powerful. To illustrate, consider the time-gated acquisition data 102 illustrated in FIG. 10. FIG. 10 includes a CT depth channel 104, a CT weight channel 106, a circulating pressure channel 108, a DH thrust channel 110, and a DH torque channel 112. CT depth 104, CT weight 106, and circulating pressure 108 are surface measurements, whereas DH thrust 110 and DH torque 112 are downhole measurements. The dashed rectangle 114 marks the time interval that produces the cross-plot relationships 96, 98, 100 illustrated in FIGS. 9A, 9B, and 9C.

Consider the hypothetical situation where there is no downhole data. In such instances, circulation pressure 108 can be used to convert and/or estimate DH thrust 110 using Conversion #2 in Table 1. Then, estimated DH thrust 110 can be used to convert and/or estimate DH torque 112:

For this example, the linear regressions for the relationships 96, 98, 100 in FIGS. 9A, 9B, and 9C were produced based on the milling period in the dashed rectangle 114 illustrated in FIG. 10.

At time B (in FIG. 10):

• • (1) The coiled tubing 20 is beginning to RIH to initiate milling, but there is no milling yet;
  • (2) The measured circulating pressure 108 is 3148 psi;
  • (3) Per Conversion #2 (in Table 1), there is 698 lbf of estimated DH thrust 110. Note that by convention in this tool, positive thrust indicates a tensile load. The DH thrust 110 measured by the memory tool is 573 lbf.
  • (4) Per Conversion #1 (in Table 1) and reusing 698 lbf of estimated DH thrust 110, there is −44 ft-lbf of DH torque 112 (i.e., there is no milling going on). The DH torque 112 measured by the memory tool is −41 ft-lbf.

At time C (in FIG. 10):

• • (1) The coiled tubing 20 is engaging the plug and actively milling.
  • (2) The measured circulating pressure 108 is 3313 psi.
  • (3) Per Conversion #2 (in Table 1), there is −363 lbf of estimated DH thrust 110. Note that by convention in this tool, negative thrust indicates a compressive load (i.e., pressing against the plug with the BHA 26). The DH thrust 110 measured by the memory tool is −316 lbf.
  • (4) Per Conversion #1 (in Table 1) and reusing −363 lbf of estimated DH thrust 110, there is 10 ft-lbf of DH torque 112 (i.e., there is some milling going on). The DH torque 112 measured by the memory tool is 8 ft-lbf.

The relationship between circulating pressure 108 and DH torque 112 (Conversion #3 in Table 1) can be used to reduce the number of conversions. However, in practice, the workflow may not benefit from this reduced number of conversions. Instead, a more likely workflow 116 is illustrated in FIG. 11. As illustrated, during a first run 118, a memory tool (e.g., as part of a downhole well tool 36) may be run into hole (RIH) to a particular CT depth 104, for example, into a wellbore 14 (step 120) and a milling operation may be performed (step 122). Then, memory data from the run may be extracted (step 124) and then used to improve the TFM model (step 126).

Then, during a subsequent run 128 (e.g., a second run including a second well operation, as described herein), a downhole well tool 36 may be run into hole (RIH) to a particular CT depth 104, for example, into a wellbore 14 without a memory tool (step 130). Then, CT weight 106 may be monitored (step 132) and circulation pressure 108 may be monitored (step 134). Then, the monitored CT weight 106 may be used to estimate WOB based on the TFM prediction (step 136) and the monitored circulation pressure 108 may be used to estimate WOB based on the circulation pressure relationship (step 138). Then, the two estimations of WOB may be averaged or otherwise processed relative to each other (step 140) and used to estimate DH torque 112 based on a torque-thrust (WOB) relationship (step 142). It will be appreciated that the workflow 116 may be iteratively continued by continuously monitoring the parameters (step 144). Furthermore, in certain embodiments, during this iterative process, one or more operating parameters of the subsequent run 128 may be controlled based on the estimated WOB values.

As such, with respect to FIG. 11:

• • 1. During an initial run 118, torque and thrust data will be recorded with a memory tool (e.g., as part of a downhole well tool 36) or with real-time telemetry.
  • 2. Torque and thrust data will be analyzed and torque-thrust relationships will be developed that are unique to a particular downhole motor 28 and milling bit 30, milling fluid, milling target (e.g., mechanical plug, cement, or the like), and downhole environment (e.g., downhole pressures, fluids), among other operational considerations.
  • 3. The TFM model accuracy may be improved as per the example illustrated in FIG. 7.
  • 4. As a result of the improved TFM model, a more accurate prediction can be generated of WOB based on CT weight 106, as per the example in FIG. 8.
  • 5. Then, during a subsequent run 128, where there is no downhole data collected, the CT operator may rely on:
    • a. The CT weight 106 and the TFM to predict an estimated WOB.
    • b. The circulation pressure conversion to predict an estimated DH thrust 110 (i.e., WOB) as per the Conversion #2 in Table 1.
  • 6. Based on the two estimated WOB values, a certainty of WOB may be calculated. The operator may choose to use the minimum, maximum, or average value of those estimated WOB values.
  • 7. The WOB value (whether the minimum, maximum, or average value) may be converted to an estimated DH torque 112 using the Conversion #1 in Table 1.

Returning to FIG. 8, the WOB measured by the memory tool is fed back into the TFM inputs (as a desired WOB), and the TFM output predicts a CT weight slackoff of 906 lbf. However, to enable this workflow 116, as proposed in FIG. 11, the TFM enables the reverse TFM workflow: the user plugs in the measured CT weight slackoff, and the TFM outputs the expected WOB downhole. Further, the workflow 116 may eliminate the effort of manually having to calculate the CT weight slackoff and, instead, have the TFM module (or software) use the TFM service in real-time to provide an estimate of WOB (or tensile load) that updates in substantially real time.

Although the first run 118 and the second run 128 are described herein with references to a series of steps of the workflow 116, it should be understood that, in certain embodiments, the first run 118 and the second run 128 may include more steps of the workflow 116 than the referenced steps or less steps than the referenced steps. For instance, one or more of the referenced steps may be split into two or more steps. Additionally, or alternatively, two or more of the referenced steps may be consolidated into a single step.

In addition, although the embodiments described herein are primarily focused on calculating and/or estimating WOB for a first well operation based on a TFM and one or more surface parameters, and then calculating and/or estimating a downhole parameter for a second, subsequent well operation, in other embodiments, instead of (or in addition to) WOB, pull on bit (POB), torque, and or a downhole pressure may be calculated and/or estimated for the first well operation based on the TFM and the one or more surface parameters, and these calculated and/or estimated values may instead be used to estimate the downhole parameter for the second, subsequent well operation.

The foregoing description, for purpose of explanation, has been described with reference to specific embodiments. However, the illustrative discussions above are not intended to be exhaustive or to limit the disclosure to the precise forms disclosed. Many modifications and variations are possible in view of the above teachings. Moreover, the order in which the elements of the methods described herein are illustrated and described may be re-arranged, and/or two or more elements may occur simultaneously. The embodiments were chosen and described in order to best explain the principles of the disclosure and its practical applications, to thereby enable others skilled in the art to best utilize the disclosure and various embodiments with various modifications as are suited to the particular use contemplated.

Finally, the techniques presented and claimed herein are referenced and applied to material objects and concrete examples of a practical nature that demonstrably improve the present technical field and, as such, are not abstract, intangible or purely theoretical. Further, if any claims appended to the end of this specification contain one or more elements designated as “means for [perform]ing [a function] . . . ” or “step for [perform]ing [a function] . . . ”, it is intended that such elements are to be interpreted under 35 U.S.C. § 112(f). However, for any claims containing elements designated in any other manner, it is intended that such elements are not to be interpreted under 35 U.S.C. § 112(f).