CONTROLLING RESISTANCE IN WELL SYSTEM

Description

TECHNICAL FIELD

Some implementations relate to operations for hydraulic fracturing in a well. More specifically, some implementations relate to controlling resistance in a well system.

BACKGROUND

The hydraulic fracture treatment process for a stage may be determined by a pre-job design that may attempt to optimize certain performance metrics. One such metric may be the designed stage resistance at the end of the hydraulic fracture treatment. During the treatment process, stage resistivity may not be trending correctly to achieve the desired pressure at the end of the treatment. This may lead to poor fracture treatment efficiency where the performance metrics are not achieved before running out of resources. Alternatively, the hydraulic fracture treatment may be successful, but not optimized with respect to time and cost.

BRIEF DESCRIPTION OF THE DRAWINGS

Implementations of the disclosure may be better understood by referencing the accompanying drawings.

FIG. 1 is a graph showing curves representing pressure and flow of a water hammer.

FIG. 2 is a diagram illustrating example operations for supervised training.

FIG. 3 is diagram illustrating an example neural network that may be used in conjunction with some implementations.

FIG. 4 is a block diagram illustrating a computer system that may be utilized with some implementations.

FIG. 5 is a flow diagram illustrating operations for determining factor vectors during one or more hydraulic fracture treatments.

FIG. 6 is an illustration depicting an example multi-well system, according to some implementations.

FIG. 7 is a flow diagram showing operations for determining a new factor vector for a first hydraulic fracture treatment in a wellbore.

DESCRIPTION OF IMPLEMENTATIONS

The description that follows may include example systems, methods, techniques, and program flows that embody implementations of the disclosure. However, this disclosure may be practiced without these specific details. For clarity, some well-known instruction instances, protocols, structures, and techniques may not be shown in detail.

Overview

Efficiency of a hydraulic fracture treatment process may be improved by periodically monitoring the resistance during the hydraulic fracture treatment and adjusting treatment factors to optimally drive the resistance to the desired value. These factors may include breakdown sequence, proppant pumping scheme, rate/proppant cycling sequences, diverter drop information or any application of formation conditioning agents, remaining resources (e.g. fluid/proppant volume), and any other suitable aspect of the hydraulic fracture treatment process. In this disclosure, a factor vector refers to one or more factors. The factors are described in more detail below.

Some implementations may monitor (periodically, continuously, or otherwise) flow resistance of a wellbore or boundary (also referred to herein as “resistance”) throughout a hydraulic fracture treatment. During the hydraulic fracture treatment, some implementations may attempt to achieve or maintain a target resistance (i.e., a desired resistance for the treatment) in the well. To achieve or maintain the target resistance, some implementations may compute the current resistance from the amplitude and decay rate of water hammers generated from rapid drops in pressure and flow rate. Some implementations utilize the current resistance to select different factor vectors that have a likelihood of achieving (or maintaining) the target resistance over the course of the hydraulic fracture treatment. For example, a controller may select a factor vector for a first time interval of the hydraulic treatment. Depending on the current resistance, the controller may modify the factor vector (or select a completely different factor vector) for the next time interval of the hydraulic fracture treatment. Periodic updates to the factor vector may be repeated until the target resistance is achieved (or is likely). Although factor vectors may be updated during a hydraulic fracture treatment, some implementations also may operate across treatments by using a previous treatment's factor vector and resistance to determine a later treatment's factor vector. This approach of optimizing the factor vector in response to conditions (such as current resistance) during intervals of the hydraulic fracture treatment may increase production and reduce waste of fluid, proppant, and other material pumped for the each well.

Some implementations may train a learning machine (such as a neural network or other suitable machine learning model) to predict a factor vector that is likely to achieve or maintain a specified target resistance for the next time interval of the hydraulic fracture treatment. The learning machine may make the prediction based on the current factor vector, current resistance, and target resistance. Additional details about training the learning machine are described below.

Example Implementations

Some implementations periodically compute resistance during the hydraulic fracture treatment from the amplitude and decay rate of a pressure water hammer measured at the well head. FIG. 1 is a graph showing curves representing pressure and flow of a water hammer. In FIG. 1, the graph 100 includes a plot of pressure and a plot of flow rate for a water hammer during a hydraulic fracture treatment. During hydraulic fracture treatments, some implementations use the water hammer to compute the resistance of the boundary. The rising and falling edges 102 of the pressure plot may constitute a boundary condition with which some implementations compute resistance based on the water hammer. The pressure pulses of the water hammer may occur one or more times during one or more hydraulic fracture treatments.

The wellbore can be modeled using the following equations:

C ⁢ ∂ H ∂ t + ∂ Q ∂ x = 0 ( Equation ⁢ 1 ) I ⁢ ∂ H ∂ t + ∂ H ∂ x + R ⁢ Q = 0 ( Equation ⁢ 1 ) C = g ⁢ A a 2 , I = 1 g ⁢ A , R = f ⁢ ❘ "\[LeftBracketingBar]" Q ❘ "\[RightBracketingBar]" 2 ⁢ g ⁢ D ⁢ A 2 ( Equation ⁢ 3 )

In Equations 1-3, C is the capacitance, H is the head, t is the time, Q is the flowrate, x is the spatial dimension (measured depth), I is the inductance, R is the resistance, g is gravity, f is the friction coefficient, D is diameter, and A is the area of cross section. In some implementations, values for one or more of the variables noted herein may be measured or derived from one or more sensors in the well or at the surface.

The resistance of the boundary can be modeled by

Δ ⁢ H = R * ⁢ Q ( Equation ⁢ 4 )

The parameters R, C, and I associated with the wellbore may be known, and all the boundary conditions may be known. Thus, the only unknown, R*, may be computed using an inversion process by using the measured pressure pulse signature (104). In the example here,

R i * ⁢ and ⁢ R i + 1 *

may be computed via inversion process.

Many sets of sample data

{ F ⁢ V i , R i * } ⁢ and ⁢ { FV i + 1 , R i + 1 * }

may obtained, where FV_i may represent the factor vector that was in-use when

R i *

was computed. FV_i+1 may represent the factor vector of the next interval of the treatment. Each factor vector may include all the treatment data up to the instance of measurement (pressure, rate, chemicals, proppant concentration, etc.), wellbore geometry detail, perforation design and fluid properties, etc.

Some implementations train a machine learning model to learn from data patterns and to make decisions without knowledge of the explicit relationships. FIG. 2 is a diagram illustrating example operations for supervised training. Training samples from a training data set may be used to train the machine learning model 204. Each training sample may include the following features: current factor vector (FV_i), current computed resistance

( R i * ) ,

resistance for the next interval

( R i + 1 * ) ,

and the next factor vector (FV_i+1). Using the current factor vector (FV_i), current resistance

( R i * ) ,

and the next resistance

( R i + 1 * ) ,

the machine learning model can predict the next factor vector (predicted FV_i+1) that is likely to achieve the next resistance

( R i + 1 * ) .

The predicted next factor (predicted FV_i+1) is compared to the known next factor vector from the sample data (FV_i+1). If the predicted factor next factor vector does not match the known next factor vector, the machine learning model may be updated (such as by updating, weights, biases, activation functions, etc.). This process may repeat for all sample in the training data set and for multiple training data sets.

After the machine learning model is trained, it may be inverted to determine next factor vectors that may achieve a target resistance for a hydraulic fracture treatment. The target resistance is a desired resistance for the hydraulic fracture treatment. The target resistance may come from different sources—for example, operators or system components may desire to have the target resistance be a certain fraction of design perforation resistance. The design perforation resistance can be calculated using:

ℛ 𝒯 = 16 ⁢ ρ ⁢ Q N p 2 ⁢ π 2 ⁢ h 4 ( Equation ⁢ 5 )

where rate Q, number of perforations N_p, discharge coefficient C, and hydraulic perforation diameter h=d√{square root over (C)}, where d is a perforation diameter. For example, operators or system components may desire to have target resistance to be 90% of . Other sources of target resistance may involve using correlation between resistance and cluster flow distribution metric, such as uniformity index. There may be any suitable source of target resistance.

In some implementations, the machine learning model may be implemented via a neural network. The neural network may be configured to learn a function that transforms input data into meaningful predictions or classifications about factor vectors of a hydraulic fracture treatment. The function may be defined by aspects of the neural network such as weights, biases, activation functions, and other functionality of the neural network. FIG. 3 is diagram illustrating an example neural network that may be used in conjunction with some implementations. The neural network 302 may include a plurality of neurons 304. The neural network 302 also may include an input layer having any suitable number of neurons 304 (supporting any suitable number of features). The input layer may intake information (sometimes referred to as features) indicating a current resistance, the designed target resistance for the hydraulic fracture treatment, and the current factor vector. The neural network 302 also may include an output layer that predicts an optimal factor vector for the next time interval of the hydraulic fracture treatment based on the information fed into the input layer-that is, based on the current resistance, target resistance, and current factor vector. The output layer may include any suitable number of neurons. The neural network may be embodied in the factor vector unit 300 via computer-executable instructions, hardware, circuitry, and/or other logic for performing the functionality described herein.

In some implementations, the factor vector unit 300 may be integrated into a computer system. FIG. 4 is a block diagram illustrating a computer system that may be utilized with some implementations. In FIG. 4, a computer system 400 may include one or more processors 402 connected to a system bus 404. The system bus 404 may be connected to memory 408 and a network interface 405. The memory 408 may include any suitable memory random access memory (RAM), non-volatile memory (e.g., magnetic memory device), and/or any device for storing information and instructions executable by the processor(s) 402. The network interface 405 may provide connectivity to any suitable network, such as a wired network, wireless network, satellite network, etc.

The computer system 400 may include additional peripheral devices. For example, the computer system 400 may include multiple external multiple processors. In some implementations, any of the components can be integrated or subdivided.

The computer system 400 also may include a factor vector unit 300. The factor vector unit 300 may implement the methods and operations described herein. The factor vector unit 300 may include the neural network 302 (as described herein). The factor vector unit 300 may include any suitable instructions, media, circuitry, and/or logic for performing the operations described herein. In some implementations, the computer system 400 may be included in the well system (such as the well system described with reference to FIG. 6) and may cooperate with other components and/or systems to perform the functionality described herein.

The computer system 400 also may include a resistance unit 412 configured to perform operations for determining resistance during a well treatment. The resistance unit 412 may determine the resistance (such as a current resistance) by utilizing one or more of the equations described herein. The resistance unit 412 also may perform computations for determining design resistance (see also Equation 5).

The computer system 400 also may include a fracturing controller 410 configured to perform operations for controlling fracturing operations in a well. The fracturing controller 410 may respond to output from the factor vector unit 300. For example, the fracturing controller 410 may implement a new factor vector during a fracturing treatment, where the factor vector unit 300 unit outputs the new factor vector.

Although the components are shown separately, any of the components of the computer system 400 may be further combined or subdivided. For example, the factor vector unit 300 and fracturing controller 410 may be combined into a single component or subdivided into three or more components. Any component of the computer system 400 can be implemented as hardware, firmware, and/or machine-readable media including computer-executable instructions for performing the operations described herein. For example, some implementations include one or more non-transitory machine-readable media including computer-executable instructions including program code configured to perform functionality described herein. Machine-readable media includes any mechanism that provides (e.g., stores and/or transmits) information in a form readable by a machine (e.g., a computer system). For example, tangible machine-readable media includes read only memory (ROM), random access memory (RAM), magnetic disk storage media, optical storage media, flash memory machines, etc. Machine-readable media also includes any media suitable for transmitting software over a network.

As noted, some implementations determine a current resistance of a hydraulic fracture treatment. These implementations may utilize the current resistance to determine a next factor vector that is likely to achieve a target resistance during hydraulic fracture treatments. FIG. 5 is a flow diagram illustrating operations for determining factor vectors during one or more hydraulic fracture treatments. In FIG. 5, the flow 500 begins at block 502, where the fracturing controller 410 may initialize a factor vector for a hydraulic fracture treatment and commence the hydraulic fracture treatment.

At block 504, the factor vector unit 300 may record one or more water hammer pressure pulses. In some implementations, the factor vector unit 300 records at least two water hammer pressure pulses before moving to block 506. FIG. 1 shows an example water hammer pressure pulse.

At block 506, the resistance unit 412 may determine, based on one or more water hammer pressure pulses, target and current resistances for the current factor vector. In some implementations, the target resistance may be determined earlier as part of the design process for the hydraulic fracture treatment. However, in some implementations, the resistance unit 412 may determine the target resistance based on well conditions, factors in the factor vector, and/or other data. As for the current resistance, the resistance unit 412 may utilize one or more the equations described herein to determine the current resistance in the well based on the first water hammer pressure pulse.

At block 508, the factor vector unit 300 may obtain the current factor vector, current resistance, and target resistance. At this point, the factor vector unit 300 is already trained (as described above) and capable of predicting a new factor vector during the hydraulic fracture treatment, where the new factor vector is likely to achieve the target resistance.

At block 510, the factor vector unit 300 may predict a new factor vector and continue the hydraulic fracture treatment using the new factor vector.

At block 512, the resistance unit 412 may obtain one or more water hammer pressure pulses.

At block 514, the resistance unit 412 may determine a current resistance based on the water hammer pressure pulse(s).

At block 516, the resistance unit 412 may compare the current resistance with the target resistance. If the current resistance is above a resistance threshold (such as the current resistance is unacceptable) (see block 518), operations continue at block 506. By continuing at block 506, the fracturing controller 410 may find a new factor vector that achieves a current resistance closer to the target resistance. If the current resistance is below a resistance threshold (such as the current resistance is acceptable) (see block 518), operations continue at block 520.

At block 520, the fracturing controller 410 may determine that current resistance was achieved or at least was acceptable (such as below the resistance threshold).

At block 522, the fracturing controller 410 may end the hydraulic fracture treatment and move to the next stage in the hydraulic fracturing process. In some implementations, operational flow may proceed back to 502 for a new stage of the hydraulic fracture treatment. If the process proceeds at block 502 for a new stage of the hydraulic fracture treatment, the factor vector unit 300 may determine an initial factor vector for the next stage based on the last values for current resistance, current factor vector, and target resistance. Hence, some implementations may predict factor vectors across stages of the hydraulic fracture treatment.

In some implementations, a downhole operation or attribute in the wellbore may be modified or updated based one or more new factor vectors. For example, in response to the factor vector unit 300 determining a new factor vector based on current and target resistances, the fracturing controller 310 may perform (and/or direct to be performed) a change to a downhole operation or attribute. The downhole operation or attribute may be part of a hydraulic fracturing treatment or other fracturing operation. For example, physical attributes in a borehole may be set based on a new factor vectors generated by the factor vector unit 300. Examples of such attributes may include depth, composition of the proppant used for fracturing, composition of the fracturing fluid used for fracturing, the pump rate for fracturing, etc. In some embodiments, the fracturing controller 310 may alter (or cause to be altered) one or more attributes in the borehole. As a result, there is modification of depth, composition of the proppant used for fracturing, composition of the fracturing fluid used for fracturing, the pump rate for fracturing, and/or other suitable attributes in the wellbore.

As noted above, factors may include breakdown sequence, proppant pumping scheme, rate/proppant cycling sequences, diverter drop information or any application of formation conditioning agents, remaining resources (e.g. fluid/proppant volume), and any other suitable aspect of the hydraulic fracture treatment process. Also as noted, a factor vector refers to one or more factors. In some implementations a breakdown sequence relates to the initial phase of the fracturing process where a fracture is created in the rock formation. The breakdown sequence may entail: 1) injecting fracturing fluid into the wellbore to increase wellbore pressure; 2) increasing the wellbore pressure until reaching the pressure required to initiate a fracture in the rock formation; 3) fracturing the rock formation as the wellbore pressure exceeds the breakdown pressure causing the rock formation to fracture. 4) propagating the fracture by continuing to inject the fracturing fluid causing the fracture to propagate. The breakdown sequence may create new pathways in the rock formation, enabling the extraction of oil or gas. The breakdown sequence can be influenced by various factors, including the properties of the rock formation and the characteristics of the fracturing fluid.

In some implementations a proppant pumping scheme relates to proppant distribution inside the fracture and may influence conductivity and the production rate. A proppant pumping scheme may entail an initial injection of fracturing fluid, without proppants to create the fractures. Next, the proppant pumping scheme may entail a proppant injection. After the fractures are created, the proppant may be mixed with the fracturing fluid and injected into the wellbore. The proppants may be carried by the fracturing fluid into the fractures. Over time, the size, type and concentration of proppant may vary according to a proppant schedule. A proppant schedule may start with smaller, lighter proppants and gradually transition to larger, heavier proppants. After the proppants are placed in the fractures, clean fluid may be pumped into the wellbore to push the proppant further into the fractures. The goal of a proppant pumping scheme may be to achieve a uniform proppant concentration and to maximize hydrocarbon production. The exact scheme can vary depending on the specifics of the reservoir and the goals of the operation.

In some implementations, the diverter drop information relates to use of diverters. which are materials used to guide the fracturing fluid into specific fractures while avoiding interference with other wells. Diverters may be dropped in relatively small volumes during short breaks in pumping of proppant within each stage. The particles may follow the fluid flow into the dominant opening and form a plug that is strong enough to stand up to the hydraulic pressure. Afterward, they break may down into a benign liquid.

Formation conditioning agents may be used in various ways in hydraulic fracturing. For instance, in sandstone or shale formations, proppants may be injected to hold fractures open. In carbonate formations, acid may be pumped into the fractures to etch the formation, creating artificial roughness. Conditioning agents may include soundless cracking demolition agents (SCDAs) to initiate radial fractures in a predrilled host rock, followed by hydraulic stimulation to extend the fractures. These agents help condition the formation to enhance the effectiveness of the hydraulic fracturing process.

Example Environment

The computer system 400 may be part of a larger system for drilling and fracturing well. FIG. 6 is an illustration depicting an example multi-well system, according to some implementations. In particular, FIG. 6 is a schematic of a multi-well system 600 that includes a wellbore 602 and a wellbore 608 in a subsurface formation 601. The wellbore 602 includes casing 606 and a number of perforations 690A-690H being made in the casing 606 at different depths to allow reservoir fluids (i.e., oil, water, and gas) from the subsurface formation 601 to flow into the wellbore 602. Similarly, the wellbore 608 includes casing 610 and a number of perforations 680A-680H being made in the casing 610 to allow reservoir fluids (i.e., oil, water, and gas) from the subsurface formation 601 to flow into the wellbore 608. During hydraulic fracturing operations of the wellbores 602 608, fracturing fluid, with or without sand, may be pumped into the subsurface formation 601, via the perforations 690A-690H and perforations 680A-680H, to hydraulically fracture the rock such that reservoir fluid may flow into the wellbore 602, 608, respectfully.

In some implementations, one or more sensors may be positioned in a wellbore to obtain measurements while an offset well is being hydraulically fractured. For example, the wellbore 602 may include a fiber optic cable 620 to obtain strain measurements, temperature measurements, derived pressure measurements (from strain measurements), etc. of the subsurface formation 601 while the wellbore 608 is being hydraulically fractured. The fiber optic cable 620 may extend from the wellhead 614 on the surface 611 to the subsurface along the wellbore 602. The fiber optic cable 620 may be cemented in place in the annular space between the casing 606 of the wellbores 602 and the subsurface formation 601. The fiber optic cable 620 may be clamped to the outside of the casing 606 during deployment and protected by centralizers and cross coupling clamps. The fiber optic cables 620 may be included with coiled tubing, wireline, loose fiber using coiled tubing, or gravity deployed fiber coils that unwind the fiber as the coils are moved in the wellbore 602. The fiber optic cable 620 also may be deployed with pumped down coils and/or self-propelled containers. Additional deployment options for the fiber optic cable 620 can include coil tubing and wireline deployed coils where the fiber optic cables 620 are anchored at the toe of the wellbore. In such implementations the fiber optic cable 620 can be deployed when the wireline or coiled tubing is removed from the well. The fiber optic cable 620 may house one or more optical fibers, and the optical fibers may be single mode fibers, multi-mode fibers, or a combination of single mode and multi-mode optical fibers. The distribution of sensors shown in FIG. 6 is for example purposes only. Any suitable sensor deployment may be used.

The fiber optic cable 620 may be used for distributed sensing where acoustic, vibration, strain, and temperature measurements may be collected downhole in the wellbores 602. The measurements may be collected at various positions distributed along the fiber optic cable 620. For example, data may be collected every 1-3 ft along the full length of the fiber optic cable 620 downhole along the horizontal section of the wellbore. Fiber optic interrogation unit 622 of the wellbore 602 may be located on the surface 611 of the multi-well system 600. The fiber optic interrogation units 622 may be directly coupled to the fiber optic cables 620. Alternatively, the fiber optic interrogation units 622 may be coupled to a fiber stretcher module, wherein the fiber stretcher module is coupled to the fiber optic cable 620. The fiber optic interrogation unit 622 may receive measurement values taken and/or transmitted along the length of the fiber optic cable 620 such as acoustic, temperature, strain, etc. The fiber optic interrogation unit 622 may be electrically connected to a digitizer to convert optically transmitted measurements into digitized measurements.

The fiber optic interrogation unit 622 may operate using various sensing principles including but not limited to amplitude-based sensing systems like DTS, DAS, Low Frequency Distributed Acoustic Sensing (LFDAS), Distributed Vibration Sensing (DVS), and Distributed Strain Sensing (DSS). For example, the DTS system may be based on Raman and/or Brillouin scattering. A DAS system may be a phase sensing-based system based on interferometric sensing using homodyne or heterodyne techniques where the system may sense phase or intensity changes due to constructive or destructive interference. The DAS system may also be based on Rayleigh scattering and in particular coherent Rayleigh scattering. A DSS system may be a strain sensing system using dynamic strain measurements based on interferometric sensors or static strain sensing measurements using Brillouin scattering. DAS systems based on Rayleigh scattering may also be used to detect dynamic strain events. Temperature effects may in some cases be subtracted from both static and/or dynamic strain events, and temperature profiles may be measured using Raman based systems and/or Brillouin based systems capable of differentiating between strain and temperature, and/or any other optical and/or electronic temperature sensors, and/or any other optical and/or electronic temperature sensors, and/or estimated thermal events.

In some implementations, the fiber optic interrogation unit 622 may measure changes in optical fiber properties between two points in an optical fiber at any given point, and these two measurement points move along the optical sensing fiber as light travels along the optical fiber. Changes in optical properties may be induced by strain, vibration, acoustic signals and/or temperature as a result of the fluid flow. Phase and intensity based interferometric sensing systems are sensitive to temperature and mechanical, as well as acoustically induced, vibrations. DAS data can be converted from time series data to frequency domain data using Fast Fourier Transforms (FFT) and other transforms, like wavelet transforms, also may be used to generate different representations of the data. Various frequency ranges can be used for different purposes and where low frequency signal changes may be attributed to formation strain changes or fluid movement and other frequency ranges may be indicative of fluid movement. Various techniques may be applied to generate indicators of events related to the generation and/or expansion of shear induced fracture fields during hydraulic fracturing operations. Although FIG. 6 depicts the fiber optic cable 620 in the wellbore 602, a fiber optic cable 620 may also be positioned in the wellbore 608 to obtain measurements when the wellbore 602 is hydraulically fractured.

The wellbore 602 may also include pressure sensors, such as externally ported pressure sensors 630, 632, to measure the formation pressure while the offset wellbore 608 is hydraulically fractured. Although FIG. 6 depicts the externally ported pressure sensors 630, 632 at the heel and toe of the wellbore 602, respectively, the externally ported pressure sensors 630, 632 may be positioned at any suitable location in the wellbore 602. Although FIG. 6 depicts the externally ported pressure sensors 630, 632 external to the casing 606 of the wellbore 602, externally ported pressure sensors 630, 632 may also be positioned in the wellbore 608 to obtain measurements when the wellbore 602 is hydraulically fractured.

During the hydraulic fracturing operations of wellbore 602 and/or wellbore 608, shear induced fracturing fields may be generated and/or dilated. For example, the shear induced fracturing fields comprising Mode 2 and/or Mode 3 failures may form between clusters of a stage, between stages of a wellbore, between clusters and/or stages of offset wellbores, etc. In some implementations, the fiber optic cable 620 and/or the externally ported pressure sensors 630, 632 may obtain measurements of the subsurface formation 601 to detect and/or monitor the subsurface formation 601 and the shear induced fracture fields.

A computer 670 may be communicatively coupled to the fiber optic interrogation units 622, externally ported pressure sensors 630, 632, and other sensors in the multi-well system 600. The computer 670 may include a signal processor to perform various signal processing operations on signals captured by the fiber optic interrogation units 622, externally ported pressure sensors 630, 632, and/or other components of the multi-well system 600. The computer 670 may have one or more processors and a memory device to analyze the measurements and graphically represent analysis results on a display device. The computer 670 may include one or more of the components described with reference to FIGS. 3 and 4. The computer 670 may be configured to predict factor vectors during a hydraulic fracturing treatment (as described herein). Although FIG. 6 depicts a system with multiple wellbores, embodiments described herein may also be applicable to other systems such as a single well system, multiple pads, etc.

FIG. 7 is a flow diagram showing operations for determining a new factor vector for a first hydraulic fracture treatment in a wellbore. At block 702, a resistance unit may determine, based on one or more first pressure pulses, a first current resistance in the well during the first hydraulic fracture treatment. At block 804, a factor vector unit may determine a second factor vector based, at least in part, on a first factor vector and the first current resistance and a target resistance. At block 806, a fracturing controller may implement the second factor vector in the wellbore. At block 808, the resistance unit may determine, based on one or more second pressure pulses, a second current resistance in the well during the first hydraulic fracture treatment. At block 810, the factor vector unit may determine whether the second current resistance is above a resistance threshold. At block 812, in response to the second current resistance being above the resistance threshold, the factor vector unit may determine a third factor vector.

FIGS. 1-7 and the operations described herein are examples meant to aid in understanding example implementations and should not be used to limit the potential implementations or limit the scope of the claims. None of the implementations described herein may be performed exclusively in the human mind nor exclusively using pencil and paper. None of the implementations described herein may be performed without computerized components such as those described herein. Some implementations may perform additional operations, fewer operations, operations in parallel or in a different order, and some operations differently. Some implementations may perform the operations with different components.

As used herein, a phrase referring to “at least one of” a list of items refers to any combination of those items, including single members. As an example, “at least one of: a, b, or c” is intended to cover: a, b, c, a-b, a-c, b-c, and a-b-c.

The various illustrative logics, logical blocks, modules, circuits, and algorithm processes described in connection with the implementations disclosed herein may be implemented as electronic hardware, computer software, or combinations of both. The interchangeability of hardware and software has been described in terms of functionality, and illustrated in the various illustrative components, blocks, modules, circuits and processes described throughout. Whether such functionality is implemented in hardware or software depends upon the particular application and design constraints imposed on the overall system.

The hardware and data processing apparatus used to implement the various illustrative logics, logical blocks, modules and circuits described in connection with the implementations disclosed herein may be implemented or performed with a general purpose single- or multi-chip processor, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof designed to perform the functions described herein. A general-purpose processor may be a microprocessor or any conventional processor, controller, microcontroller, or state machine. A processor also may be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. In some implementations, particular processes and methods may be performed by circuitry that is specific to a given function.

In one or more implementations, the functions described may be implemented in hardware, digital electronic circuitry, computer software, firmware, including the structures disclosed in this specification and their structural equivalents thereof, or in any combination thereof. Implementations of the subject matter described in this specification also may be implemented as one or more computer programs, such as one or more modules of computer program instructions stored on a computer storage media for execution by, or to control the operation of, a computing device.

If implemented in software, the functions may be stored on or transmitted over as one or more instructions or code on a computer-readable medium. The processes of a method or algorithm disclosed herein may be implemented in a processor-executable instructions which may reside on a computer-readable medium. Computer-readable media includes both computer storage media and communication media including any medium that may be enabled to transfer a computer program from one place to another. Storage media may be any available media that may be accessed by a computer. By way of example, and not limitation, such computer-readable media may include RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium that may be used to store desired program code in the form of instructions or data structures and that may be accessed by a computer. Also, any connection may be properly termed a computer-readable medium. Disk and disc, as used herein, includes compact disc (CD), laser disc, optical disc, digital versatile disc (DVD), floppy disk, and Blu-Ray™ disc where disks usually reproduce data magnetically, while discs reproduce data optically with lasers. Combinations also may be included within the scope of computer-readable media. Additionally, the operations of a method or algorithm may reside as one or any combination or set of codes and instructions on a machine readable medium and computer-readable medium, which may be incorporated into a computer program product.

Various modifications to the implementations described in this disclosure may be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other implementations without departing from the spirit or scope of this disclosure. Thus, the claims are not intended to be limited to the implementations shown herein but are to be accorded the widest scope consistent with this disclosure, the principles and the novel features disclosed herein.

Certain features that are described in this specification in the context of separate implementations also may be implemented in combination in a single implementation. Conversely, various features that are described in the context of a single implementation also may be implemented in multiple implementations separately or in any suitable subcombination. Moreover, although features may be described as acting in certain combinations and even initially claimed as such, one or more features from a claimed combination may in some cases be excised from the combination, and the claimed combination may be directed to a subcombination or variation of a subcombination.

Similarly, while operations are depicted in the drawings in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order, or that all illustrated operations be performed, to achieve desirable results. Further, the drawings may schematically depict one more example process in the form of a flow diagram. However, some operations may be omitted and/or other operations that are not depicted may be incorporated in the example processes that are schematically illustrated. For example, one or more additional operations may be performed before, after, simultaneously, or between any of the illustrated operations. In certain circumstances, multitasking and parallel processing may be advantageous. Moreover, the separation of various system components in the implementations described should not be understood as requiring such separation in all implementations, and the described program components and systems may be integrated together in a single software product or packaged into multiple software products. Additionally, other implementations are within the scope of the following claims. In some cases, the actions recited in the claims may be performed in a different order and still achieve desirable results.

Example Clauses

Some implementations may include the following clauses.

Clause 1: A method for determining a new factor vector for a first hydraulic fracture treatment in a wellbore, the method comprising: determining, based on one or more first pressure pulses, a first current resistance in the well during the first hydraulic fracture treatment; determining a second factor vector based, at least in part, on a first factor vector and the first current resistance and a target resistance; implementing the second factor vector in the wellbore; determining, based on one or more second pressure pulses, a second current resistance in the well during the first hydraulic fracture treatment; determining whether the second current resistance is above a resistance threshold; and in response to the second current resistance being above the resistance threshold, determining a third factor vector.

Clause 2: The method of clause 1 further comprising: in response to the second current resistance being below a threshold resistance, continuing with the first hydraulic fracture treatment in the wellbore.

Clause 3: The method of any one or more of clauses 1-2 further comprising: determining a fourth factor vector for a second hydraulic fracture treatment based on a third current resistance that was determined during the first hydraulic fracture treatment, a fourth current resistance that was determined during the first hydraulic fracture treatment, and a target resistance for the second hydraulic fracture treatment.

Clause 4: The method of any one or more of clauses 1-3 further comprising: obtaining training samples each including a first current resistant sample, a first factor vector sample, a second current resistance sample, and a second factor vector sample; training, with the training samples, a machine learning model to output a predicted factor vector that matches the second factor vector sample based on the first current resistance sample, the first factor vector sample, and the second current resistance sample.

Clause 5: The method of any one or more of clauses 1-4, wherein the determining the second factor vector occurs after the training and includes an inversion of the machine learning model by which the machine learning model outputs the second factor vector based on the first factor vector, first current resistance, and the second current resistance.

Clause 6: The method of any one or more of clauses 1-5, wherein the determining whether the second current resistance is above a resistance threshold includes: determining a difference between the second current resistance and the target resistance; in response to the difference being above a given resistance value, determining the second current resistance is above the resistance threshold; and in response to the difference being below a given resistance value, determining the second current resistance is below the resistance threshold.

Clause 7: The method of any one or more of clauses 1-6, wherein the target resistance is based, at least in part, on a design perforation resistance.

Clause 8: The method of any one or more of clauses 1-7 further comprising: implementing the first factor vector during the first hydraulic fracture treatment in the wellbore.

Clause 9: One or more non-transitory computer-readable mediums including instructions that, when executed by a processor, perform operations for determining a new factor vector for a first hydraulic fracture treatment in a wellbore, the instructions comprising: instructions to determine, based on one or more first pressure pulses, a first current resistance in the well during the first hydraulic fracture treatment; instructions to determine a second factor vector based, at least in part, on a first factor vector and the first current resistance and a target resistance; instructions to implement the second factor vector in the wellbore; instructions to determine, based on one or more second pressure pulses, a second current resistance in the well during the first hydraulic fracture treatment; instructions to determine whether the second current resistance is above a resistance threshold; and instructions to, in response to the second current resistance being above the resistance threshold, determine a third factor vector.

Clause 10: The one or more machine-readable mediums of clause 9 further comprising: instructions to, in response to the second current resistance being below a threshold resistance, continue with the first hydraulic fracture treatment in the wellbore.

Clause 11: The one or more machine-readable mediums of any one or more of clauses 9-10, the instructions further comprising: instructions to determine a fourth factor vector for a second hydraulic fracture treatment based on a third current resistance that was determined during the first hydraulic fracture treatment, a fourth current resistance that was determined during the first hydraulic fracture treatment, and a target resistance for the second hydraulic fracture treatment.

Clause 12: The one or more machine-readable mediums of any one or more of clauses 9-11, the instructions further comprising: instructions to obtain training samples each including a first current resistant sample, a first factor vector sample, a second current resistance sample, and a second factor vector sample; instructions to train, with the training samples, a machine learning model to output a predicted factor vector that matches the second factor vector sample based on the first current resistance sample, the first factor vector sample, and the second current resistance sample.

Clause 13: The one or more machine-readable mediums of any one or more of clauses 9-12, wherein the determination of the second factor vector to occur after the instructions to train and include an inversion of the machine learning model by which the machine learning model to output the second factor vector based on the first factor vector, first current resistance, and the second current resistance.

Clause 14: The one or more machine-readable mediums of any one or more of clauses 9-13, wherein the instructions to determine whether the second current resistance is above a resistance threshold includes: instructions to determine a difference between the second current resistance and the target resistance; instructions to, in response to the difference being above a given resistance value, determine the second current resistance is above the resistance threshold; and instructions to, in response to the difference being below a given resistance value, determine the second current resistance is below the resistance threshold.

Clause 15: The one or more machine-readable mediums of any one or more of clauses 9-14, wherein the target resistance is based, at least in part, on a design perforation resistance.

Clause 16: The one or more machine-readable mediums of any one or more of clauses 9-15, the instructions further comprising: instructions to implement the first factor vector during the first hydraulic fracture treatment in the wellbore.

Clause 17: A computer system comprising: a processor; one or more non-transitory computer-readable mediums including instructions that, when executed by the processor, perform operations for determining a new factor vector for a first hydraulic fracture treatment in a wellbore, the instructions comprising: instructions to determine, based on one or more first pressure pulses, a first current resistance in the well during the first hydraulic fracture treatment; instructions to determine a second factor vector based, at least in part, on a first factor vector and the first current resistance and a target resistance; instructions to implement the second factor vector in the wellbore; instructions to determine, based on one or more second pressure pulses, a second current resistance in the well during the first hydraulic fracture treatment; instructions to determine whether the second current resistance is above a resistance threshold; and instructions to, in response to the second current resistance being above the resistance threshold, determine a third factor vector.

Clause 18: The computer system of clause 17, the instructions further comprising: instructions to, in response to the second current resistance being below a threshold resistance, continue with the first hydraulic fracture treatment in the wellbore.

Clause 19: The computer system of any one or more of clauses 17-18, the instructions further comprising: instructions to determine a fourth factor vector for a second hydraulic fracture treatment based on a third current resistance that was determined during the first hydraulic fracture treatment, a fourth current resistance that was determined during the first hydraulic fracture treatment, and a target resistance for the second hydraulic fracture treatment.

Clause 20: The computer system of any one or more of clauses 17-19, the instructions further comprising: instructions to obtain training samples each including a first current resistant sample, a first factor vector sample, a second current resistance sample, and a second factor vector sample; instructions to train, with the training samples, a machine learning model to output a predicted factor vector that matches the second factor vector sample based on the first current resistance sample, the first factor vector sample, and the second current resistance sample.