Crack parameter determination method and device based on distributed optical fibers

Abstract

The invention provides a crack parameter determination method and device based on distributed optical fibers. Obtaining a first waterfall plot corresponding to the target well and an initial crack influence parameter; performing crack propagation simulation processing according to the initial crack influence parameter, and constructing a second waterfall plot according to an optical fiber strain value in the crack propagation simulation process; under the condition that the first waterfall plot and the second waterfall plot are not matched, adjusting the initial crack influence parameter to obtain an adjusted crack influence parameter, and continuing to perform crack propagation simulation processing according to the adjusted crack influence parameter until the constructed second waterfall plot and the first waterfall plot are matched to obtain a target crack influence parameter; and according to the target crack influence parameter, determining a crack parameter corresponding to the target well. Compared with an existing method, the method has the advantages that the problem of multiplicity of solutions caused by construction of the waterfall plot only depending on the optical fiber strain value is avoided, the generated waterfall plot better conforms to the actual expansion characteristics of the crack, and meanwhile the determination precision of the crack parameters is improved.

Description

Technical Field

[0001] This specification belongs to the technical field of oil exploration, and particularly relates to a method and device for determining fracture parameters based on distributed optical fiber. Background Art

[0002] Currently, existing methods directly generate a waterfall diagram based on the optical fiber strain response mechanism and fit it with the on-site optical fiber waterfall diagram. However, such fitting has certain limitations, which may lead to the generated waterfall diagram not matching the actual evolution characteristics of the fracture, resulting in a large error between the inferred fracture parameters and the actual construction situation.

[0003] In response to the above problems, no effective solution has been proposed yet. Summary of the Invention

[0004] This specification provides a method and device for determining fracture parameters based on distributed optical fiber. First, the fracture propagation process is simulated using initial fracture influence parameters, and a second waterfall diagram is constructed in combination with optical fiber strain data, and then it is fitted and matched with the first waterfall diagram collected on-site for comparative analysis, so as to accurately determine the fracture parameters of the target well. Compared with the existing method, it avoids the multi-solution problem that may be caused by constructing the waterfall diagram only relying on the optical fiber strain value, makes the generated waterfall diagram more in line with the actual propagation characteristics of the fracture, and at the same time greatly improves the determination accuracy of the fracture parameters.

[0005] This specification provides a method for determining fracture parameters based on distributed optical fiber, including:

[0006] Obtain the first waterfall diagram corresponding to the target well and the initial fracture influence parameters;

[0007] Perform fracture propagation simulation processing according to the initial fracture influence parameters, and construct a second waterfall diagram according to the optical fiber strain values in the fracture propagation simulation process;

[0008] Detect whether the first waterfall diagram and the second waterfall diagram match;

[0009] In the case where the first waterfall diagram and the second waterfall diagram do not match, adjust the initial fracture influence parameters to obtain adjusted fracture influence parameters, and continue to perform fracture propagation simulation processing according to the adjusted fracture influence parameters until the constructed second waterfall diagram and the first waterfall diagram match to obtain the target fracture influence parameters;

[0010] Determine the fracture parameters corresponding to the target well according to the target fracture influence parameters.

[0011] In one embodiment, the obtaining the first waterfall diagram corresponding to the target well includes:

[0012] Obtain the acoustic wave signals corresponding to the target well through the distributed optical fiber at different positions in the target well, and construct the first waterfall diagram according to the acoustic wave signals.

[0013] In one embodiment, the constructing the first waterfall diagram according to the acoustic wave signals includes:

[0014] Perform noise reduction processing on the acoustic wave signals to obtain the noise-reduced acoustic wave signals;

[0015] Perform feature extraction processing on the noise-reduced acoustic wave signals to obtain key features, where the key features include but are not limited to frequency, amplitude, and phase;

[0016] Construct the first waterfall diagram according to the key features.

[0017] In one embodiment, the initial fracture influence parameters include but are not limited to stress difference parameters, rock physical property parameters, fracture geometry parameters, and injection fluid property parameters.

[0018] In one embodiment, the optical fiber strain value is determined according to the displacement of each measuring point of the optical fiber and the interval between the measuring points.

[0019] In one embodiment, the adjusting the initial fracture influence parameters to obtain the adjusted fracture influence parameters includes:

[0020] Use a preset prediction model to adjust the initial fracture influence parameters according to the initial fracture response parameters and the corresponding matching degrees to obtain the adjusted fracture influence parameters; wherein, the prediction model is a model constructed according to a preset machine learning algorithm.

[0021] In one embodiment, the method further includes:

[0022] Use a preset risk detection model to detect whether there is a fracture leakage risk in the target well according to the fracture parameters and the distributed optical fiber data of the target well, obtain a risk detection result, and determine an exploitation strategy for the target well according to the risk detection result.

[0023] This specification provides a device for determining fracture parameters based on distributed optical fiber, including:

[0024] A first determination module, configured to obtain a first waterfall diagram corresponding to a target well and initial fracture influence parameters;

[0025] A second determination module, configured to perform fracture propagation simulation processing according to the initial fracture influence parameters, and construct a second waterfall diagram according to the optical fiber strain value during the fracture propagation simulation process;

[0026] A matching degree judgment module, configured to detect whether the first waterfall diagram and the second waterfall diagram match;

[0027] An influence parameter determination module, configured to, when the first waterfall diagram and the second waterfall diagram do not match, perform adjustment processing on the initial crack influence parameter to obtain an adjusted crack influence parameter, and continue to perform crack propagation simulation processing according to the adjusted crack influence parameter until the constructed second waterfall diagram matches the first waterfall diagram, so as to obtain a target crack influence parameter;

[0028] A crack parameter determination module, configured to determine the crack parameters corresponding to the target well according to the target crack influence parameter.

[0029] This specification also provides an electronic device, including a processor and a memory for storing processor-executable instructions, where when the processor executes the instructions, a method for determining crack parameters based on distributed optical fiber is implemented.

[0030] This specification also provides a computer-readable storage medium, on which computer instructions are stored, and when the instructions are executed, a method for determining crack parameters based on distributed optical fiber is implemented.

[0031] Based on a method for determining crack parameters based on distributed optical fiber provided in this specification, obtain the first waterfall diagram corresponding to the target well and the initial crack influence parameter; perform crack propagation simulation processing according to the initial crack influence parameter, and construct a second waterfall diagram according to the optical fiber strain value in the crack propagation simulation process; detect whether the first waterfall diagram and the second waterfall diagram match; when the first waterfall diagram and the second waterfall diagram do not match, perform adjustment processing on the initial crack influence parameter to obtain an adjusted crack influence parameter, and continue to perform crack propagation simulation processing according to the adjusted crack influence parameter until the constructed second waterfall diagram matches the first waterfall diagram, so as to obtain a target crack influence parameter; determine the crack parameters corresponding to the target well according to the target crack influence parameter. In this way, first, the crack propagation process is simulated using the initial crack influence parameter, and a second waterfall diagram is constructed in combination with the optical fiber strain data, and then it is fitted and matched with the first waterfall diagram collected on site for comparative analysis, so as to accurately determine the crack parameters of the target well. Compared with the existing method, it avoids the multi-solution problem that may be caused by constructing the waterfall diagram only relying on the optical fiber strain value, makes the generated waterfall diagram more in line with the actual propagation characteristics of the crack, and at the same time greatly improves the determination accuracy of the crack parameters. Description of the Drawings

[0032] To more clearly illustrate the embodiments of this specification, the accompanying drawings required for the embodiments will be briefly introduced below. The accompanying drawings in the following description are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0033] Figure 1 It is a schematic flow chart of a method for determining crack parameters based on distributed optical fiber provided by an embodiment of this specification;

[0034] Figure 2 It is a schematic structural composition diagram of an electronic device provided by an embodiment of this specification;

[0035] Figure 3 It is a schematic structural composition diagram of a device for determining crack parameters based on distributed optical fiber provided by an embodiment of this specification;

[0036] Figure 4 It is a schematic diagram of data preprocessing provided by an embodiment of this specification;

[0037] Figure 5 It is a schematic diagram of inverted strain rate data provided by an embodiment of this specification;

[0038] Figure 6 It is a schematic diagram of inversion interpretation provided by an embodiment of this specification. Specific implementation manners

[0039] In order to enable those skilled in the art to better understand the technical solutions in this specification, the technical solutions in the embodiments of this specification will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of this specification. Obviously, the described embodiments are only a part of the embodiments of this specification, rather than all the embodiments. Based on the embodiments in this specification, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of this specification.

[0040] At present, unconventional oil and gas reservoirs are playing an increasingly important role in the oil and gas development industry. When facing unconventional reservoirs with low porosity, low permeability, tightness and strong heterogeneity, hydraulic fracturing reservoir stimulation technology is usually adopted to improve the seepage capacity of the rock formations in unconventional oil reservoirs. After the fracturing stimulation technology is implemented on an oil and gas well, the fracturing effect is usually evaluated to control and optimize the production of the fractured horizontal well and guide the subsequent fracturing design. Therefore, effective monitoring of the fracturing process is of great guiding significance for the optimization design of fracturing parameters of oil wells in low-permeability reservoirs, improving the reservoir stimulation effect, and the production dynamic adjustment and measure implementation during the later horizontal well development process. As a new type of fracturing monitoring and diagnosis technology at present, the distributed fiber optic sensing technology is widely used in the oil and gas development industry due to its unique properties. Distributed fiber optic fracturing monitoring is divided into the well itself and adjacent wells according to the fiber optic arrangement. Among them, adjacent well monitoring arranges the optical fiber along the monitoring well parallel to the fractured well. Adjacent well fiber optic low-frequency vibration monitoring realizes accurate fracturing monitoring by capturing the low-frequency vibration signals during the fracture formation process.

[0041] The existing technology obtains the oilfield fiber optic data from the field and constructs the Green's function by the three-dimensional displacement discontinuity method. First, the linear equations are solved by the least squares method, and then through the Markov chain Monte Carlo simulation, fracture width samples are generated from the target distribution of the adjacent well fiber optic low-frequency vibration monitoring strain data, and the uncertainty related to the inversion width is quantified. Simply put, the existing inversion method first obtains the fiber optic signal waterfall diagram of the adjacent well fiber optic low-frequency vibration monitoring at the oilfield site; secondly, based on the fiber optic strain response characteristics, the waterfall diagram of the adjacent well fiber optic low-frequency vibration monitoring is generated; by comparing the signal waterfall diagrams generated by the two, the latter's characteristics are continuously iteratively calculated to be close to the signal waterfall diagram at the oilfield site according to the existing differences. Finally, the fracture parameters corresponding to the result after the iterative calculation are compared and verified with the fracture parameters formed by the actual fracturing to further verify the accuracy of the model. However, the existing inversion method has certain limitations. Since the signal waterfall diagram is generated based on the fiber optic strain response characteristics, the fracture parameters corresponding to the picture often do not conform to the fracture propagation mechanism, resulting in a large error between the fracture parameters and the actual artificial fractures.

[0042] Aiming at the root cause of the above problems, this specification first simulates the fracture propagation process using the initial fracture influence parameters, constructs the second waterfall diagram in combination with the fiber optic strain data, and then performs fitting matching and comparative analysis with the first waterfall diagram collected on-site, so as to accurately determine the fracture parameters of the target well. Compared with the existing method, it avoids the multi-solution problem that may be caused by only relying on the fiber optic strain value to construct the waterfall diagram, makes the generated waterfall diagram more in line with the actual propagation characteristics of the fracture, and at the same time greatly improves the determination accuracy of the fracture parameters.

[0043] Refer to Figure 1As shown in the figure, the embodiment of the present specification provides a method for determining crack parameters based on distributed optical fiber, which is specifically applied to the server side. Specifically, the method may include the following:

[0044] S101: Obtain the first waterfall diagram corresponding to the target well and the initial crack influence parameters;

[0045] S102: Perform crack propagation simulation processing according to the initial crack influence parameters, and construct a second waterfall diagram according to the optical fiber strain values during the crack propagation simulation process;

[0046] S103: Detect whether the first waterfall diagram and the second waterfall diagram match;

[0047] S104: In the case where the first waterfall diagram and the second waterfall diagram do not match, perform adjustment processing on the initial crack influence parameters to obtain adjusted crack influence parameters, and continue to perform crack propagation simulation processing according to the adjusted crack influence parameters until the constructed second waterfall diagram and the first waterfall diagram match to obtain the target crack influence parameters;

[0048] S105: Determine the crack parameters corresponding to the target well according to the target crack influence parameters.

[0049] Among them, the above first waterfall diagram can be a visualization image based on distributed optical fiber monitoring data, used to display the changes of strain, vibration or acoustic wave signals along the wellbore or other monitoring areas over time and space. Its horizontal axis usually represents the well depth or the position of the sensing optical fiber, the vertical axis represents time, and the color or brightness represents the signal intensity or frequency characteristics.

[0050] The above initial crack influence parameters include but are not limited to stress difference parameters, rock physical property parameters, crack geometry parameters, and injection fluid property parameters.

[0051] In some embodiments, the obtaining of the first waterfall diagram corresponding to the target well may specifically include:

[0052] S1: Obtain the distributed optical fiber data of the target well, and perform noise reduction processing on the distributed optical fiber data to obtain the noise-reduced distributed optical fiber data;

[0053] S2: Perform time-frequency analysis on the noise-reduced distributed optical fiber data to extract key acoustic signal features;

[0054] S3: Map the key acoustic signal features according to the well depth and time dimensions to obtain a distribution diagram of acoustic wave signals changing with time;

[0055] S4: According to different time steps and the acoustic wave signal distribution map, use color gradient to visualize the change of acoustic wave signals over time, and determine the first waterfall plot corresponding to the target well.

[0056] Among them, the above time-frequency analysis can be wavelet transform or short-time Fourier transform.

[0057] In some embodiments, when specifically implementing the obtaining of the initial fracture influence parameters, it may include:

[0058] S1: Obtain the geological parameters of the target well; among them, the geological parameters include but are not limited to in-situ stress distribution, lithology characteristics, porosity, permeability, elastic modulus, and Poisson's ratio;

[0059] S2: Use acoustic logging to characterize the geological parameters to obtain the stress difference parameter and the rock physical property parameter;

[0060] S3: Use imaging logging to identify the natural fracture distribution of the target well, and combine historical fracturing construction data to determine the fracture geometric parameters;

[0061] S4: Based on the experimental monitoring data of the fracturing fluid, determine the viscosity, density, and filtration characteristics of the fracturing fluid, and combine the formation conditions to determine the injection fluid property parameters.

[0062] In some embodiments, when specifically implementing the fracture propagation simulation processing according to the initial fracture influence parameters and constructing a second waterfall plot according to the fiber strain values during the fracture propagation simulation process, it may include:

[0063] S1: Based on the initial fracture influence parameters, use the preset boundary element method to discretize the fracture surface and determine the displacement discontinuous boundary of the fracture surface;

[0064] S2: According to the displacement discontinuous boundary and the preset fracture mechanics criterion, determine the propagation path of the fracture surface;

[0065] S3: According to the propagation path and the fiber strain values during the fracture propagation simulation process, construct a second waterfall plot.

[0066] Among them, the above preset fracture mechanics criterion can be the maximum principal stress criterion or the energy release rate criterion.

[0067] In some embodiments, when specifically implementing the adjustment processing of the initial fracture influence parameters to obtain the adjusted fracture influence parameters, it may include:

[0068] S1: Calculate the matching degree between the first waterfall plot and the second waterfall plot;

[0069] S2: When the matching degree is lower than the preset matching degree threshold, use the preset gradient descent algorithm to iteratively adjust the initial fracture influence parameters until the matching degree reaches the preset matching degree threshold, and obtain the adjusted fracture influence parameters.

[0070] Specifically, the amplitude of the stress difference parameter, the elastic modulus or Poisson's ratio of the rock physical property parameter, the fracture geometric parameters (such as the initial fracture width, length, dip angle), and the injection fluid property parameters (such as viscosity, leakage coefficient) can be adjusted to obtain the adjusted fracture influence parameters.

[0071] In some embodiments, determining the fracture parameters corresponding to the target well according to the target fracture influence parameters may specifically include, in specific implementation:

[0072] S1: According to the target fracture influence parameters, establish a fracture propagation model, and use the preset boundary element method to calculate the stress field and displacement field during fracture propagation;

[0073] S2: According to the stress field and the displacement field, simulate the dynamic propagation of the fracture, and calculate the propagation path, shape of the fracture, and the displacement discontinuity characteristics on the fracture surface.

[0074] S3: According to the displacement discontinuity characteristics, determine the fracture parameters corresponding to the target well; wherein, the fracture parameters include but are not limited to fracture length, fracture width, and fracture height.

[0075] Among them, the above preset boundary element method can be the displacement discontinuity method.

[0076] Based on the above embodiments, first use the initial fracture influence parameters to simulate the fracture propagation process, construct the second waterfall diagram in combination with the fiber optic strain data, and then perform fitting matching and comparative analysis with the first waterfall diagram collected on site, so as to accurately determine the fracture parameters of the target well. Compared with the existing methods, it avoids the multi-solution problem that may be caused by only relying on the fiber optic strain value to construct the waterfall diagram, makes the generated waterfall diagram more in line with the actual propagation characteristics of the fracture, and at the same time greatly improves the determination accuracy of the fracture parameters.

[0077] In some embodiments, when obtaining the first waterfall diagram corresponding to the target well, the method may further include the following content in specific implementation:

[0078] Obtain the acoustic wave signals corresponding to the target well through the distributed optical fibers at different positions in the target well, and construct the first waterfall diagram according to the acoustic wave signals.

[0079] Based on the above embodiments, acoustic signals are obtained through distributed optical fibers at different positions in the target well, and a first waterfall plot is constructed based on the signals, which can accurately reflect the acoustic propagation characteristics and dynamic changes at each depth position in the wellbore.

[0080] In some embodiments, when specifically implementing the method of constructing the first waterfall plot according to the acoustic signals, the following may further be included:

[0081] S1: Perform noise reduction processing on the acoustic signals to obtain the noise-reduced acoustic signals;

[0082] S2: Perform feature extraction processing on the noise-reduced acoustic signals to obtain key features, where the key features include but are not limited to frequency, amplitude, and phase;

[0083] S3: Construct the first waterfall plot according to the key features.

[0084] Based on the above embodiments, by performing noise reduction processing, feature extraction on the acoustic signals, and constructing the first waterfall plot based on the key features, the signal quality can be effectively improved, the interference of environmental noise on data analysis can be reduced, and the accuracy and resolution of the waterfall plot can be improved.

[0085] In some embodiments, the initial fracture influence parameters include but are not limited to stress difference parameters, rock physical property parameters, fracture geometry parameters, and injection fluid property parameters.

[0086] Among them, the above stress difference parameters reflect the distribution of the original stress field in the formation in the target well area, including the minimum principal stress, the maximum principal stress, and the horizontal stress difference, which determine the fracture propagation trend. The above rock physical property parameters include elastic modulus, Poisson's ratio, permeability, and fracture toughness, etc., which affect the response characteristics of the rock to external forces, thus determining the opening and extension behavior of the fracture. The above fracture geometry parameters describe the initial state of the fracture, including fracture length, width, dip angle, and azimuth angle, providing initial boundary conditions for fracture propagation simulation. The above injection fluid property parameters cover fluid viscosity, flow rate, leakage coefficient, and sand-carrying capacity, which affect the support and diversion ability of the fracture and play an important role in the fracture morphology and evolution process.

[0087] In some embodiments, the optical fiber strain value is determined according to the displacement of each measurement point of the optical fiber and the interval between the measurement points.

[0088] Specifically, through distributed fiber optic sensing technology (such as distributed fiber optic Brillouin or Rayleigh scattering measurement technology), minute deformations of the optical fiber at different times and spatial positions are obtained. The displacement of the measurement point can be retrieved from the strain signal sensed by the optical fiber, and the interval between measurement points is the inherent layout parameter of the optical fiber or the measurement sampling interval. Combining this information, the strain distribution of the optical fiber at various positions inside the target well can be obtained through the strain calculation formula, i.e., strain = (displacement difference between adjacent measurement points) / (measurement point interval).

[0089] Based on the above embodiments, by determining the optical fiber strain value according to the displacement change of each measurement point of the optical fiber and the measurement point interval, the strain distribution of the formation around the wellbore can be accurately reflected, improving the perception accuracy of crack propagation and formation deformation. Compared with traditional point measurement sensors, distributed optical fibers can achieve continuous, real-time, and large-scale data acquisition, providing higher-resolution strain monitoring results.

[0090] In some embodiments, when adjusting and processing the initial crack influence parameters to obtain the adjusted crack influence parameters, the following content may also be included in the specific implementation of the method:

[0091] Using a preset prediction model, according to the initial crack response parameters and the corresponding matching degree, the initial crack influence parameters are adjusted and processed to obtain the adjusted crack influence parameters; wherein, the prediction model is a model constructed based on a preset machine learning algorithm.

[0092] Specifically, in the case where the matching degree between the first waterfall plot and the second waterfall plot is low, through an error backtracking mechanism, the differences between the crack simulation results and the actual optical fiber measurement results are analyzed, and the values of relevant crack influence parameters are adjusted to gradually converge to the optimal solution. This adjustment process can adopt optimization methods such as gradient descent, Bayesian optimization, or genetic algorithm to improve the adaptability of the preset prediction model to the crack propagation characteristics under complex formation conditions. In addition, during the continuous iteration of the preset prediction model, the parameter weights can be dynamically updated to make the crack propagation simulation more accurate.

[0093] In some embodiments, the following content may also be included in the specific implementation of the method:

[0094] Using a preset risk detection model, according to the crack parameters and the distributed optical fiber data of the target well, the target well is detected for the risk of crack leakage to obtain a risk detection result, and according to the risk detection result, a production strategy for the target well is determined.

[0095] Specifically, first, filter and extract features from the distributed optical fiber data of the target well, analyze abnormal strain, temperature changes, and acoustic signals, and identify possible leakage signs; then, perform a matching analysis between the identification results and the fracture parameters to determine the possibility and severity of fracture leakage, and finally generate a risk detection result.

[0096] Furthermore, according to the risk detection result, formulate an optimal production strategy for the target well to reduce the potential leakage risk and optimize wellbore stability. For the well sections detected with high leakage risk, measures such as adjusting fracturing parameters, reducing injection pressure, optimizing the position of packers, or implementing directional production can be taken to reduce the impact of leakage on reservoir recovery. In addition, if the detection result indicates that a complex seepage channel has been formed in the fracture network, staged plugging, adjusting the production rhythm, or strengthening the monitoring strategy can be adopted to improve the overall recovery rate and ensure the long-term stable operation of oil and gas wells. Through this closed-loop risk monitoring and adjustment mechanism, the safety and economy of fracturing operations can be effectively improved, and the intelligent level of oil and gas development can be enhanced.

[0097] As can be seen from the above, a method for determining fracture parameters based on distributed optical fiber provided by an embodiment of this specification obtains a first waterfall diagram corresponding to a target well and initial fracture influence parameters; performs fracture propagation simulation processing according to the initial fracture influence parameters, and constructs a second waterfall diagram according to the optical fiber strain values during the fracture propagation simulation process; detects whether the first waterfall diagram and the second waterfall diagram match; in the case where the first waterfall diagram and the second waterfall diagram do not match, perform adjustment processing on the initial fracture influence parameters to obtain adjusted fracture influence parameters, and continue to perform fracture propagation simulation processing according to the adjusted fracture influence parameters until the constructed second waterfall diagram matches the first waterfall diagram to obtain target fracture influence parameters; determine the fracture parameters corresponding to the target well according to the target fracture influence parameters. In this way, first, use the initial fracture influence parameters to simulate the fracture propagation process, combine the optical fiber strain data to construct a second waterfall diagram, and then perform fitting matching and comparative analysis with the first waterfall diagram collected on site, so as to accurately determine the fracture parameters of the target well. Compared with the existing methods, it avoids the multi-solution problem that may be caused by constructing the waterfall diagram only relying on the optical fiber strain value, makes the generated waterfall diagram more in line with the actual propagation characteristics of the fracture, and at the same time greatly improves the determination accuracy of the fracture parameters.

[0098] Refer to Figure 2 As shown, an embodiment of this specification also provides a specific electronic device, wherein the electronic device includes a network communication port 201, a processor 202, and a memory 203, and the above structures are connected by internal cables so that each structure can perform specific data interaction.

[0099] Among them, the network communication port 201 can specifically be used to obtain the first waterfall plot corresponding to the target well and the initial fracture influence parameters.

[0100] The processor 202 can specifically be used to perform fracture propagation simulation processing based on the initial fracture influence parameters, and construct a second waterfall plot according to the fiber optic strain values during the fracture propagation simulation process; detect whether the first waterfall plot and the second waterfall plot match; in the case where the first waterfall plot and the second waterfall plot do not match, perform adjustment processing on the initial fracture influence parameters to obtain adjusted fracture influence parameters, and continue to perform fracture propagation simulation processing according to the adjusted fracture influence parameters until the constructed second waterfall plot and the first waterfall plot match to obtain the target fracture influence parameters; determine the fracture parameters corresponding to the target well according to the target fracture influence parameters.

[0101] The memory 203 can specifically be used to store corresponding instruction programs.

[0102] Based on the above method, the relevant structural performance of the electronic device can be effectively utilized, the data processing speed of the electronic device can be improved, and the method for determining fracture parameters based on distributed optical fiber can be efficiently realized.

[0103] In this embodiment, the network communication port 201 can be bound to different communication protocols, so as to send or receive different data virtual ports. For example, the network communication port can be a port responsible for web data communication, or a port responsible for FTP data communication, or a port responsible for mail data communication. In addition, the network communication port can also be a physical communication interface or communication chip. For example, it can be a wireless mobile network communication chip, such as GSM, CDMA, etc.; it can also be a Wifi chip; it can also be a Bluetooth chip.

[0104] In this embodiment, the processor 202 can be implemented in any suitable manner. For example, the processor can take the form of, for example, a microprocessor or a processor and a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, application specific integrated circuit (ASIC), programmable logic controller, and embedded microcontroller, etc. This specification does not make a limitation.

[0105] In this embodiment, the memory 203 may include multiple levels. In a digital system, anything that can store binary data can be a memory; in an integrated circuit, a circuit with a storage function without a physical form is also called a memory, such as RAM, FIFO, etc.; in a system, a storage device with a physical form is also called a memory, such as a memory module, a TF card, etc.

[0106] This embodiment of the specification also provides a computer-readable storage medium based on the above method for determining crack parameters based on distributed optical fiber. Obtain a first waterfall diagram corresponding to a target well and initial crack influence parameters; perform crack propagation simulation processing according to the initial crack influence parameters, and construct a second waterfall diagram according to the optical fiber strain values during the crack propagation simulation process; detect whether the first waterfall diagram and the second waterfall diagram match; in the case where the first waterfall diagram and the second waterfall diagram do not match, perform adjustment processing on the initial crack influence parameters to obtain adjusted crack influence parameters, and continue to perform crack propagation simulation processing according to the adjusted crack influence parameters until the constructed second waterfall diagram matches the first waterfall diagram to obtain target crack influence parameters; determine the crack parameters corresponding to the target well according to the target crack influence parameters.

[0107] In this embodiment, the above storage medium includes but is not limited to random access memory (RAM), read-only memory (ROM), cache, hard disk drive (HDD), or memory card. The memory can be used to store computer program instructions. The network communication unit can be set according to the standards specified by the communication protocol and is used for the interface of network connection communication.

[0108] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer-readable storage medium can be compared and explained with other embodiments, and will not be elaborated here.

[0109] Refer to Figure 3 , at the software level, this embodiment of the specification also provides a device for determining crack parameters based on distributed optical fiber. The device may specifically include the following structural modules:

[0110] The first determination module 301 is used to obtain a first waterfall diagram corresponding to a target well and initial crack influence parameters;

[0111] The second determination module 302 is used to perform crack propagation simulation processing according to the initial crack influence parameters, and construct a second waterfall diagram according to the optical fiber strain values during the crack propagation simulation process;

[0112] A matching degree judgment module 303 is configured to detect whether the first waterfall diagram and the second waterfall diagram match;

[0113] An influence parameter determination module 304 is configured to, when the first waterfall diagram and the second waterfall diagram do not match, perform adjustment processing on the initial crack influence parameter to obtain an adjusted crack influence parameter, and continue to perform crack propagation simulation processing according to the adjusted crack influence parameter until the constructed second waterfall diagram matches the first waterfall diagram, so as to obtain a target crack influence parameter;

[0114] A crack parameter determination module 305 is configured to determine the crack parameters corresponding to the target well according to the target crack influence parameter.

[0115] In some embodiments, specifically, the first determination module 301 obtains the acoustic signal corresponding to the target well through distributed optical fibers at different positions in the target well, and the waterfall diagram construction module is configured to construct the first waterfall diagram according to the acoustic signal.

[0116] In some embodiments, specifically, the waterfall diagram construction module performs noise reduction processing on the acoustic signal to obtain a noise-reduced acoustic signal; performs feature extraction processing on the noise-reduced acoustic signal to obtain key features, where the key features include but are not limited to frequency, amplitude, and phase; constructs the first waterfall diagram according to the key features.

[0117] In some embodiments, specifically, the initial crack influence parameter includes but is not limited to a stress difference parameter, a rock physical property parameter, a crack geometry parameter, and an injected fluid property parameter.

[0118] In some embodiments, specifically, the optical fiber strain value is determined according to the displacement of each measurement point of the optical fiber and the interval between the measurement points.

[0119] In some embodiments, specifically, the influence parameter determination module 304 performs adjustment processing on the initial crack influence parameter by using a preset prediction model according to the initial crack response parameter and the corresponding matching degree to obtain the adjusted crack influence parameter; where the prediction model is a model constructed according to a preset machine learning algorithm.

[0120] In some embodiments, specifically, a preset risk detection model can also be used to detect whether there is a crack leakage risk in the target well according to the crack parameters and the distributed optical fiber data of the target well, obtain a risk detection result, and determine an exploitation strategy for the target well according to the risk detection result.

[0121] It should be noted that the units, devices, modules, etc. described in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. For the convenience of description, when describing the above devices, they are divided into various modules according to functions for separate description. Of course, when implementing this specification, the functions of each module can be implemented in the same or multiple software and / or hardware, or the modules that implement the same function can be realized by a combination of multiple sub-modules or sub-units, etc. The device embodiments described above are only illustrative. For example, the division of the units is only a logical function division, and there may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed coupling or direct coupling or communication connection between each other can be through some interfaces. The indirect coupling or communication connection of the devices or units can be in electrical, mechanical or other forms.

[0122] As can be seen from the above, based on a crack parameter determination device based on distributed optical fiber provided in the embodiments of this specification, first, the crack propagation process is simulated using the initial crack influence parameters, and a second waterfall diagram is constructed in combination with the optical fiber strain data, and then it is fitted, matched and compared with the first waterfall diagram collected on site, so as to accurately determine the crack parameters of the target well. Compared with the existing methods, it avoids the multi-solution problem that may be caused by constructing the waterfall diagram only relying on the optical fiber strain value, makes the generated waterfall diagram more in line with the actual propagation characteristics of the crack, and at the same time greatly improves the determination accuracy of the crack parameters.

[0123] In a specific scenario example, a method and device for determining crack parameters based on distributed optical fiber provided in this specification can be applied. First, the crack propagation process is simulated using the initial crack influence parameters, and a second waterfall diagram is constructed in combination with the optical fiber strain data, and then it is fitted, matched and compared with the first waterfall diagram collected on site, so as to accurately determine the crack parameters of the target well. Compared with the existing methods, it avoids the multi-solution problem that may be caused by constructing the waterfall diagram only relying on the optical fiber strain value, makes the generated waterfall diagram more in line with the actual propagation characteristics of the crack, and at the same time greatly improves the determination accuracy of the crack parameters. The specific implementation process can include the following content.

[0124] S1: Refer to Figure 4 As shown, obtain the original optical fiber data of the oilfield site, process the original data using filtering and noise reduction algorithms, remove the downhole interference vibration signals, and retain the effective information to obtain the acoustic vibration waterfall diagram (i.e., the first waterfall diagram);

[0125] S2: Establish a crack propagation model using the displacement discontinuous crack propagation calculation method;

[0126] Specifically, the above Displacement Discontinuity Method (DDM) is widely used in the oil and gas industry to describe the rock deformation during the hydraulic fracture propagation process. This method can not only calculate the displacement discontinuity surface and induced stress of a single fracture, but also calculate the three-dimensional displacement discontinuity surface and induced stress of multiple fractures. The boundary element method adopts the analytical solution of integrating the displacement discontinuity surface on the boundary element, which is easy to adapt to complex boundary geometries and is suitable for modeling fast stress change regions. The Displacement Discontinuity Method (DDM) is a special direct boundary element method, which is obtained based on the Somigliana formula.

[0127] S3: Refer to Figure 5 As shown, according to the established fracture propagation model, combined with the fiber optic strain response mechanism, extract the key signal features in the acoustic vibration waterfall plot data, such as frequency, amplitude, and phase;

[0128] Specifically, regarding the fiber optic displacement as the formation displacement, the fiber optic can only undergo axial deformation. The interval between each measuring point of the fiber optic is usually 5 - 10m, so the monitored strain and strain rate are the average values of the intervals. The number of fracture units at time t is m. The displacement of each unit can be calculated using the displacement discontinuity fundamental solution, and the superposition of all unit displacements can obtain the displacement of each measuring point of the fiber optic.

[0129]

[0130] In the formula, the specific forms of the kernel functions I1 and I2 are:

[0131]

[0132] In the formula, r and the operator || are respectively:

[0133]

[0134] In the formula, a = 0.5Δx, b = 0.5Δy.

[0135] Based on the central difference method, combined with the relationship between displacement and strain, the strain at the measuring point (axial direction) can be calculated from the displacement at the measuring point (axial direction), that is:

[0136]

[0137] The strain rate can be calculated based on the strain at adjacent times:

[0138]

[0139] When the displacement of the distributed optical fiber is continuous, the optical fiber displacement calculated from the measured point (axial) strain is the formation displacement; when the displacement of the distributed optical fiber is discontinuous (such as when a crack extends to the optical fiber), the displacement near the crack surface is discontinuous, and the optical fiber strain calculated from the optical fiber displacement at the discontinuous point is not equal to the formation strain, while the continuous part is still the formation strain.

[0140] S4: Refer to Figure 6 As shown, based on the results of the parameter sensitivity analysis, the results of the crack propagation model are used for data fitting to predict the physical parameters of the crack, such as crack width and crack height.

[0141] Specifically, the above parameter sensitivity analysis can be to identify the key parameters (i.e., initial crack influence parameters) that affect crack propagation and optical fiber strain response, such as in-situ stress difference, rock physical properties, crack geometric parameters, injected fluid properties, etc. By changing these key parameters, analyze their effects on crack morphology, propagation speed, and distributed optical fiber acoustic signal characteristics. According to the results of the sensitivity analysis, optimize the input parameters of the forward model to improve the prediction accuracy of the model for crack behavior.

[0142] Furthermore, an inversion model is constructed based on the crack parameter sensitivity analysis to describe the relationship between the distributed optical fiber acoustic signal and the crack parameters. Combining the crack formation law shown by the crack propagation, an optimization algorithm is used to fit the distributed optical fiber acoustic data to estimate the physical parameters of the crack, such as crack width and crack height. Based on the above method, the problem of non-uniqueness can be effectively solved, the fitting result is more consistent with the actual crack formation process, other fitting results that do not conform to the crack propagation mechanism law are excluded, the instability of the solution is reduced, the inversion parameters are gradually adjusted, and the model is continuously optimized.

[0143] Based on the above embodiments, compared with the existing method of directly generating a waterfall plot according to the optical fiber strain response mechanism and then fitting it with the on-site optical fiber waterfall plot, such a fitting may generate a result that is closer to the on-site waterfall plot, but because there is no crack propagation mechanism as a support, the generated waterfall plot will not conform to the crack formation law. In this solution, because the crack propagation model is used as the forward model, even if the fitting effect is relatively low, it is more in line with the crack propagation mechanism law of the hydraulic fracturing process.

[0144] Although this specification provides method operation steps as described in the embodiments or flowcharts, more or fewer operation steps may be included based on conventional or non-creative means. The order of steps listed in the embodiments is only one way among many execution orders of steps and does not represent the only execution order. When the actual device or client product is executed, it can be executed in the method order shown in the embodiments or the drawings or executed in parallel (for example, in an environment of parallel processors or multi-threaded processing, or even in a distributed data processing environment). The terms "comprising", "including" or any other variant thereof are intended to cover non-exclusive inclusion, such that a process, method, product or device comprising a series of elements not only includes those elements but also includes other elements not expressly listed, or also includes elements inherent to such process, method, product or device. Without further limitation, there is no exclusion of additional identical or equivalent elements in the process, method, product or device comprising the said elements. The words "first", "second", etc. are used to denote names and do not denote any particular order.

[0145] Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, the method steps can be logically programmed to enable the controller to implement the same functions in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. Therefore, such a controller can be regarded as a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0146] From the description of the above embodiments, those skilled in the art can clearly understand that this specification can be implemented by means of software plus a necessary general hardware platform. Based on such an understanding, the technical solution of this specification can essentially be embodied in the form of a software product, which can be stored in a storage medium such as ROM / RAM, magnetic disk, optical disc, etc., and includes several instructions for causing a computer device (which can be a personal computer, mobile terminal, server, or network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments of this specification.

[0147] Although this specification is depicted through embodiments, those of ordinary skill in the art know that this specification has many variations and changes without departing from the spirit of this specification, and it is hoped that the appended claims will cover these variations and changes without departing from the spirit of this specification.

Claims

1. A method for determining crack parameters based on distributed optical fiber, characterized in that, Including: Obtaining a first waterfall plot corresponding to a target well and initial fracture influence parameters; Performing fracture propagation simulation processing according to the initial fracture influence parameters, and constructing a second waterfall plot according to the fiber optic strain values during the fracture propagation simulation process; Detecting whether the first waterfall plot and the second waterfall plot match; In the case where the first waterfall plot and the second waterfall plot do not match, performing adjustment processing on the initial fracture influence parameters to obtain adjusted fracture influence parameters, and continuing to perform fracture propagation simulation processing according to the adjusted fracture influence parameters until the constructed second waterfall plot matches the first waterfall plot to obtain target fracture influence parameters; Determining fracture parameters corresponding to the target well according to the target fracture influence parameters.

2. The method according to claim 1, wherein The obtaining of the first waterfall plot corresponding to the target well includes: Obtaining acoustic signals corresponding to the target well through distributed optical fibers at different positions in the target well, and constructing the first waterfall plot according to the acoustic signals.

3. The method according to claim 2, wherein The constructing of the first waterfall plot according to the acoustic signals includes: Performing noise reduction processing on the acoustic signals to obtain noise-reduced acoustic signals; Performing feature extraction processing on the noise-reduced acoustic signals to obtain key features, where the key features include but are not limited to frequency, amplitude, and phase; Constructing the first waterfall plot according to the key features.

4. The method according to claim 3, characterized in that, The initial fracture influence parameters include but are not limited to stress difference parameters, rock physical property parameters, fracture geometry parameters, and injection fluid property parameters.

5. The method according to claim 4, characterized in that The fiber optic strain value is determined according to the displacement of each measurement point of the optical fiber and the interval between the measurement points.

6. The method according to claim 1, wherein The performing of adjustment processing on the initial fracture influence parameters to obtain adjusted fracture influence parameters includes: Using a preset prediction model to perform adjustment processing on the initial fracture influence parameters according to the initial fracture response parameters and the corresponding matching degree to obtain the adjusted fracture influence parameters; where the prediction model is a model constructed according to a preset machine learning algorithm.

7. The method according to claim 1, characterized in that The method further includes: Using a preset risk detection model to detect whether there is a fracture leakage risk in the target well according to the fracture parameters and the distributed optical fiber data of the target well, obtaining a risk detection result, and determining a mining strategy for the target well according to the risk detection result.

8. A device for determining crack parameters based on distributed optical fiber, characterized in that Including: A first determination module for obtaining a first waterfall plot corresponding to a target well and initial fracture influence parameters; A second determination module for performing fracture propagation simulation processing according to the initial fracture influence parameters and constructing a second waterfall plot according to the fiber optic strain values during the fracture propagation simulation process; A matching degree judgment module for detecting whether the first waterfall plot and the second waterfall plot match; An influence parameter determination module, configured to, when the first waterfall plot and the second waterfall plot do not match, perform an adjustment process on the initial crack influence parameter to obtain an adjusted crack influence parameter, and continue to perform a crack propagation simulation process based on the adjusted crack influence parameter until the constructed second waterfall plot matches the first waterfall plot, so as to obtain a target crack influence parameter; A crack parameter determination module, configured to determine the crack parameters corresponding to the target well according to the target crack influence parameter.

9. An electronic device, characterized in that, It includes a processor and a memory for storing instructions executable by the processor. When the processor executes the instructions, the steps of the distributed fiber-based crack parameter determination method according to any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that, Computer instructions are stored thereon. When the instructions are executed by a processor, the steps of the distributed fiber-based crack parameter determination method according to any one of claims 1 to 7 are implemented.

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