Method and device for determining crack information

Abstract

The invention provides a crack information determination method and device. Acquiring sound wave data of a target well in the target area by using a distributed optical fiber, and performing phase strain conversion processing on the sound wave data to obtain a first strain value; obtaining fracturing parameters of the target well, and determining first crack information of the target well by using a preset prediction model according to the fracturing parameters, a preset filtration coefficient and the initial crack position; determining a second strain value according to the first crack information and the optical fiber measuring point coordinate, and determining a model error of a preset prediction model according to the first strain value and the second strain value until the model error is smaller than a preset error threshold, thereby obtaining a target filtration coefficient and a target crack position; according to the fracturing parameters, the target filtration coefficient and the target fracture position, the target fracture information of the target well is determined, and according to the target fracture information and the target filtration coefficient of the target well, the fracturing strategy for the target well is determined, so that the fracturing strategy can be accurately and efficiently determined.

Description

Technical Field

[0001] This specification belongs to the technical field of oil extraction, and particularly relates to a method and device for determining fracture information. Background Art

[0002] Currently, for methods of inverting fracture information, there are often problems of multiple solutions. At the same time, during the fracturing process, not all of the fracturing fluid acts on fracture propagation, and a large part of it will be lost into the formation, which will further lead to problems of low accuracy and efficiency in inverting fracture information.

[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 information. By using a distributed optical fiber as a sensor, high-precision monitoring of the target well can be achieved, that is, high-precision acoustic wave data of the target well can be obtained by using the distributed optical fiber. Then, by using a preset prediction model, based on fracturing parameters, a preset fluid loss coefficient, and the initial fracture position, the target fracture information and the target fluid loss coefficient are quickly and accurately iteratively inverted. Furthermore, based on the target fracture information and the target fluid loss coefficient, a fracturing strategy for the target well is accurately and efficiently determined.

[0005] This specification provides a method for determining fracture information, including:

[0006] Using a distributed optical fiber to obtain acoustic wave data of a target well in a target area, and performing phase-strain conversion processing on the acoustic wave data to obtain a first strain value;

[0007] Obtaining the fracturing parameters of the target well, and using a preset prediction model to determine the first fracture information of the target well according to the fracturing parameters, the preset fluid loss coefficient, and the initial fracture position;

[0008] Determining a second strain value according to the first fracture information and the optical fiber measurement point coordinates, and determining the model error of the preset prediction model according to the first strain value and the second strain value. When the model error is not less than a preset error threshold, performing iterative update processing on the preset fluid loss coefficient and the initial fracture position until the model error is less than the preset error threshold to obtain a target fluid loss coefficient and a target fracture position;

[0009] Determining the target fracture information of the target well according to the fracturing parameters, the target fluid loss coefficient, and the target fracture position, and determining a fracturing strategy for the target well according to the target fracture information and the target fluid loss coefficient of the target well.

[0010] In one embodiment, the fracturing parameters include fracturing fluid displacement, planar Young's modulus, fracturing fluid viscosity, fracture height, time, and perforation location, and the first fracture information includes fracture half-length, maximum fracture width, and the fracture offset.

[0011] In one embodiment, determining the first fracture information of the target well according to the fracturing parameters, a preset filtration coefficient, and an initial fracture location includes:

[0012] Determine the fracture half-length according to the preset filtration coefficient, the fracturing fluid displacement, the planar Young's modulus, the fracturing fluid viscosity, the fracture height, and the time according to the following formula:

[0013]

[0014] where a opt is the fracture half-length, k is the filtration coefficient, Q is the fracturing fluid displacement, E' is the planar Young's modulus, μ is the fracturing fluid viscosity, H is the fracture height, and t is the time;

[0015] Determine the maximum fracture width according to the preset filtration coefficient, the fracturing fluid displacement, the planar Young's modulus, the fracturing fluid viscosity, the fracture height, and the time according to the following formula:

[0016]

[0017] where w opt is the maximum fracture width;

[0018] Determine the fracture offset according to the initial fracture location and the perforation location.

[0019] In one embodiment, performing phase-strain conversion processing on the acoustic wave data to obtain a first strain value includes:

[0020] Determine the first strain value according to the laser wavelength, optical phase difference in the acoustic wave data, and the fiber refractive index, scalar multiplication factor, and gauge length.

[0021] In one embodiment, determining a second strain value according to the first fracture information and the fiber measurement point coordinates includes:

[0022] Determine the fracture unit coordinates according to the fracture half-length and the fracture offset in the first fracture information;

[0023] Determine the fracture width distribution corresponding to the maximum fracture width according to the fracture unit coordinates and the maximum fracture width;

[0024] Determine the second strain value according to the slit width distribution corresponding to the maximum slit width, the optical fiber measurement point coordinates, and the Green's function matrix.

[0025] In one embodiment, the iterative update process for the preset filtration coefficient and the initial fracture position includes:

[0026] Use the preset nonlinear least squares method to perform an iterative update process on the preset filtration coefficient and the initial fracture position.

[0027] In one embodiment, the determining of the fracturing strategy for the target well according to the target fracture information of the target well and the target filtration coefficient includes:

[0028] When the target filtration coefficient is greater than the preset filtration threshold, determine the reduction amount of the fracturing fluid injection rate for the target well and the increase amount of the liquid viscosity for the target well according to the target fracture information of the target well;

[0029] Determine the fracturing strategy for the target well according to the reduction amount and the increase amount.

[0030] This specification provides a device for determining fracture information, including:

[0031] A data acquisition module, configured to use distributed optical fiber to acquire acoustic wave data of a target well in a target area, and perform phase-strain conversion processing on the acoustic wave data to obtain a first strain value;

[0032] A first information confirmation module, configured to acquire the fracturing parameters of the target well, and use a preset prediction model to determine the first fracture information of the target well according to the fracturing parameters, the preset filtration coefficient, and the initial fracture position;

[0033] A target information confirmation module, configured to determine a second strain value according to the first fracture information and the optical fiber measurement point coordinates, and determine the model error of the preset prediction model according to the first strain value and the second strain value. When the model error is not less than the preset error threshold, perform an iterative update process on the preset filtration coefficient and the initial fracture position until the model error is less than the preset error threshold to obtain the target filtration coefficient and the target fracture position;

[0034] A fracturing strategy confirmation module, configured to determine the target fracture information of the target well according to the fracturing parameters, the target filtration coefficient, and the target fracture position, and determine the fracturing strategy for the target well according to the target fracture information of the target well and the target filtration coefficient.

[0035] This specification also provides an electronic device, including a processor and a memory for storing instructions executable by the processor. When the processor executes the instructions, a method for determining crack information is implemented.

[0036] This specification also provides a computer-readable storage medium, on which computer instructions are stored. When the instructions are executed, a method for determining crack information is implemented.

[0037] Based on a method for determining crack information provided in this specification, using a distributed optical fiber, acoustic wave data of a target well in a target area is obtained, and the acoustic wave data is subjected to phase-strain conversion processing to obtain a first strain value; the fracturing parameters of the target well are obtained, and using a preset prediction model, according to the fracturing parameters, a preset fluid loss coefficient, and an initial crack position, the first crack information of the target well is determined; a second strain value is determined according to the first crack information and the fiber measurement point coordinates, and according to the first strain value and the second strain value, the model error of the preset prediction model is determined. And when the model error is not less than a preset error threshold, iterative update processing is performed on the preset fluid loss coefficient and the initial crack position until the model error is less than the preset error threshold, obtaining a target fluid loss coefficient and a target crack position; according to the fracturing parameters, the target fluid loss coefficient, and the target crack position, the target crack information of the target well is determined, and according to the target crack information of the target well and the target fluid loss coefficient, a fracturing strategy for the target well is determined. In this way, using a distributed optical fiber as a sensor, high-precision monitoring of the target well can be realized, that is, using a distributed optical fiber, acoustic wave data of the target well with higher accuracy can be obtained. Then, using a preset prediction model, based on the fracturing parameters, a preset fluid loss coefficient, and an initial crack position, the target crack information and the target fluid loss coefficient are quickly and accurately iteratively inverted, and then according to the target crack information and the target fluid loss coefficient, a fracturing strategy for the target well is accurately and efficiently determined. Description of the Drawings

[0038] To more clearly illustrate the embodiments of this specification, the drawings required for use in the embodiments will be briefly introduced below. The drawings described below are only some embodiments recorded in this specification. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0039] Figure 1 It is a flowchart of a method for determining crack information provided by an embodiment of this specification;

[0040] Figure 2 It is a schematic diagram of the structural composition of an electronic device provided by an embodiment of this specification;

[0041] Figure 3 It is a schematic structural composition diagram of a device for determining crack information provided by an embodiment of this specification;

[0042] Figure 4 It is a schematic overall flow diagram of a method for determining crack information provided by an embodiment of this specification. Specific implementation manners

[0043] 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.

[0044] As conventional oil and gas resources continue to decrease and people's demand for oil and gas continues to increase, the development of unconventional oil and gas has become increasingly important. Unconventional oil and gas are difficult to be economically and effectively exploited by traditional technical means and require large-scale hydraulic fracturing for development. During the hydraulic fracturing process, obtaining information on bottom-hole fracturing events and realizing bottom-hole fracturing event monitoring and evaluation are one of the key issues.

[0045] Commonly used fracturing monitoring means include microseismic, tracers, and cross-well monitoring, etc. Although each technology has been applied to fracturing on-site monitoring, each technology has certain limitations. The fiber optic sensing monitoring technology is a hydraulic fracturing monitoring technology developed in recent years. According to its technical principle, it can be mainly divided into two types: distributed fiber optic temperature sensing data (DTS) technology and distributed fiber optic acoustic data (DAS) technology. In particular, DAS data has advantages such as higher accuracy and signal intensity.

[0046] Currently, the main difficulty in solving the inversion model lies in the serious problem of multi-solution. At the same time, during the fracturing process, not all of the fracturing fluid acts on the crack propagation, and a large part of it will be lost into the formation, which is also an unsolved problem at present.

[0047] Aiming at the root causes of the above problems, this specification considers using distributed optical fiber as a sensor, which can realize high-precision monitoring of the target well, that is, using distributed optical fiber can obtain relatively accurate acoustic data of the target well. Then, using a preset prediction model, based on fracturing parameters, a preset fluid loss coefficient, and the initial crack position, the target crack information and the target fluid loss coefficient are iteratively inverted quickly and accurately. Furthermore, according to the target crack information and the target fluid loss coefficient, the fracturing strategy for the target well is determined accurately and efficiently.

[0048] Refer to Figure 1As shown in the figure, the embodiments of the present specification provide a method for determining crack information, which is specifically applied to the server side. Specifically, the method may include the following:

[0049] S101: Using a distributed optical fiber, obtain acoustic wave data of a target well in a target area, and perform phase-strain conversion processing on the acoustic wave data to obtain a first strain value;

[0050] S102: Obtain the fracturing parameters of the target well, and use a preset prediction model to determine the first crack information of the target well according to the fracturing parameters, a preset filtration coefficient, and an initial crack position;

[0051] S103: Determine a second strain value according to the first crack information and the optical fiber measurement point coordinates, and determine the model error of the preset prediction model according to the first strain value and the second strain value. When the model error is not less than a preset error threshold, perform iterative update processing on the preset filtration coefficient and the initial crack position until the model error is less than the preset error threshold to obtain a target filtration coefficient and a target crack position;

[0052] S104: Determine the target crack information of the target well according to the fracturing parameters, the target filtration coefficient, and the target crack position, and determine a fracturing strategy for the target well according to the target crack information of the target well and the target filtration coefficient.

[0053] Among them, the above-mentioned distributed optical fiber can be a sensing technology that obtains signals along the entire optical fiber, and can synchronously measure changes in physical quantities such as acoustic waves and temperature at multiple points on the optical fiber to form a continuous data distribution along the wellbore depth according to the measured data.

[0054] In some embodiments, the step of using a distributed optical fiber to obtain acoustic wave data of a target well in a target area may specifically include:

[0055] S1: According to the distributed optical fiber sensor arranged in the target well, monitor the change amount of the acoustic wave signal caused by fluid flow or vibration in the target area;

[0056] S2: Convert the change amount of the acoustic wave signal into corresponding acoustic wave data;

[0057] In some embodiments, when determining the model error of the preset prediction model according to the first strain value and the second strain value, specifically, the form of the sum of squared errors (SSE) may be adopted, and the squares of the differences between the first strain value and the second strain value are summed as a measure index of the model error.

[0058] Among them, the above-mentioned preset prediction model can be the (Perforation-Cluster-Kriger, PKN) model, which is a classic mathematical model for describing fracture propagation and fracturing fluid flow and is applicable to the case where the fracture length is greater than the fracture height.

[0059] In some embodiments, when the model error is not less than a preset error threshold, iterative update processing is performed on the preset filtration coefficient and the initial fracture position until the model error is less than the preset error threshold to obtain a target filtration coefficient and a target fracture position. Specifically, it may include:

[0060] When the model error is not less than a preset error threshold, using a preset iterative algorithm, iterative update processing is performed on the preset filtration coefficient and the initial fracture position until the model error is less than the preset error threshold to obtain a target filtration coefficient and a target fracture position; among them, the preset iterative algorithm can be determined based on a preset deep learning algorithm.

[0061] Specifically, the preset iterative algorithm may include but is not limited to genetic algorithms, particle swarm optimization algorithms, simulated annealing algorithms, and Kalman filters. In addition, with the goal of minimizing the model error, the preset filtration coefficient is gradually adjusted to optimize the volume loss of the fracturing fluid, and at the same time, the initial fracture position is corrected to more accurately reflect the actual distribution of fractures in the formation. Through each iteration, the theoretical strain value calculated by the model is compared with the actually observed strain value, and the parameters are adjusted according to the error value until the calculated value and the observed value are highly matched, ensuring that the theoretical model is closer to the actual geological and construction conditions.

[0062] Furthermore, when the model error converges below the preset error threshold, the iteration stops, and the obtained target filtration coefficient and target fracture position are used as the inversion results. These results not only accurately reflect the filtration behavior of the fracturing fluid but also provide the specific position of the fractures in the formation, providing an important basis for fracturing construction design and fracture monitoring. Through this optimization process, the accuracy and reliability of model prediction can be effectively improved, thus supporting subsequent construction optimization and production decision-making.

[0063] In some embodiments, determining the target fracture information of the target well according to the fracturing parameters, the target filtration coefficient, and the target fracture position may specifically include:

[0064] S1: According to the fracturing parameters and the target filtration coefficient, using a preset prediction model, determine the target fracture half-length and the target maximum fracture width of the target well;

[0065] S2: Determine the target fracture information of the target well according to the target fracture position, the target fracture half-length, and the target maximum fracture width.

[0066] Among them, the above target filtration coefficient can be obtained by calculating the ratio of the fracture volume of the target well to the volume of the fracturing fluid through the target fracture information of the target well.

[0067] Based on the above embodiments, by using a distributed optical fiber as a sensor, high-precision monitoring of the target well can be achieved, that is, high-precision acoustic wave data of the target well can be obtained by using the distributed optical fiber. Then, using a preset prediction model, based on the fracturing parameters, the preset filtration coefficient, and the initial fracture position, the target fracture information and the target filtration coefficient can be iteratively inverted quickly and accurately. Furthermore, according to the target fracture information and the target filtration coefficient, a fracturing strategy for the target well can be determined accurately and efficiently.

[0068] In some embodiments, the fracturing parameters include the fracturing fluid displacement, the plane Young's modulus, the fracturing fluid viscosity, the fracture height, the time, and the perforation position, and the first fracture information includes the fracture half-length, the maximum fracture width, and the fracture offset.

[0069] Among them, the above perforation position may refer to the hole position set on the wellbore wall for connecting the wellbore and the reservoir to achieve efficient fluid flow.

[0070] In some embodiments, when specifically implementing the method of determining the first fracture information of the target well according to the fracturing parameters, the preset filtration coefficient, and the initial fracture position, the following content may further be included:

[0071] S1: According to the preset filtration coefficient, the fracturing fluid displacement, the plane Young's modulus, the fracturing fluid viscosity, the fracture height, and the time, determine the fracture half-length according to the following formula:

[0072]

[0073] where a opt is the fracture half-length, k is the filtration coefficient, Q is the fracturing fluid displacement, E` is the plane Young's modulus, μ is the fracturing fluid viscosity, H is the fracture height, and t is the time;

[0074] S2: According to the preset filtration coefficient, the fracturing fluid displacement, the plane Young's modulus, the fracturing fluid viscosity, the fracture height, and the time, determine the maximum fracture width according to the following formula:

[0075]

[0076] where w optis the maximum slit width;

[0077] S3: Determine the fracture offset according to the initial fracture position and the perforation position.

[0078] Based on the above embodiments, determining the first fracture information of the target well through fracturing parameters, a preset filtration coefficient, and the initial fracture position can provide a reliable initial estimate for the optimization of fracture geometric parameters, thereby improving the efficiency and convergence rate of inversion calculation. At the same time, this preliminary information can quickly evaluate the formation's response to the fracturing operation, optimize the injection strategy of the fracturing fluid, reduce construction risks, and lay a foundation for the reliability of the final fracture model and the accuracy of the construction design, significantly enhancing the overall effect and economic benefits of the fracturing operation.

[0079] In some embodiments, when performing phase strain conversion processing on the acoustic wave data to obtain a first strain value, the method may further include the following when specifically implemented:

[0080] Determine the first strain value according to the laser wavelength, optical phase difference, fiber refractive index, scalar multiplication factor, and gauge length in the acoustic wave data.

[0081] In some embodiments, when determining the first strain value according to the laser wavelength, optical phase difference, fiber refractive index, scalar multiplication factor, and gauge length in the acoustic wave data, it can be obtained according to the following formula when specifically implemented:

[0082]

[0083] where ε is the first strain value, λ is the laser wavelength, is the optical phase difference, n c is the fiber refractive index, δ is the scalar multiplication factor, G L is the gauge length.

[0084] Based on the above embodiments, calculating the first strain value through the laser wavelength, optical phase difference, fiber refractive index, scalar multiplication factor, and gauge length in the acoustic wave data can accurately obtain the dynamic strain information of the formation near the fracture, reflecting the real formation response during the fracturing process. This method can improve the accuracy and resolution of strain measurement, provide reliable basic data for fracture parameter inversion, and simultaneously realize real-time monitoring of fracture propagation and fracturing fluid behavior, which helps to optimize the fracturing design, reduce construction risks, and improve the efficiency and effect of the fracturing operation.

[0085] In some embodiments, when determining the second strain value according to the first fracture information and the fiber measurement point coordinates, the method may further include the following when specifically implemented:

[0086] S1: Determine the crack unit coordinates according to the crack half-length and the crack offset in the first crack information;

[0087] S2: Determine the crack width distribution corresponding to the maximum crack width according to the crack unit coordinates and the maximum crack width;

[0088] S3: Determine the second strain value according to the crack width distribution corresponding to the maximum crack width, the optical fiber measurement point coordinates, and the Green's function matrix.

[0089] In some embodiments, when specifically implementing the determination of the second strain value according to the crack width distribution corresponding to the maximum crack width, the optical fiber measurement point coordinates, and the Green's function matrix, it can be obtained according to the following formula:

[0090]

[0091] where ε is the second strain value, x s,k is the optical fiber measurement point coordinate, y s,k is the optical fiber measurement point coordinate, G is the Green's function matrix, and w is the crack width distribution.

[0092] In some embodiments, when specifically implementing the iterative update process of the preset filtration coefficient and the initial crack position, the method may further include the following:

[0093] Use the preset nonlinear least squares method to perform iterative update processing on the preset filtration coefficient and the initial crack position.

[0094] Based on the above embodiments, in the process of performing iterative update processing on the preset filtration coefficient and the initial crack position, using the preset nonlinear least squares algorithm can effectively solve the possible multi-solution problem.

[0095] In some embodiments, when specifically implementing the determination of the fracturing strategy for the target well according to the target crack information and the target filtration coefficient of the target well, the method may further include the following:

[0096] S1: When the target filtration coefficient is greater than the preset filtration threshold, determine the reduction amount of the fracturing fluid injection rate for the target well and the increase amount of the liquid viscosity for the target well according to the target crack information of the target well;

[0097] S2: Determine the fracturing strategy for the target well according to the reduction amount and the increase amount.

[0098] Specifically, for example, assume that the target filtration coefficient of the target well is 0.25, while the preset filtration threshold is 0.2, indicating that there is a relatively large loss of fracturing fluid in the fracture. Based on the geometric information of the fracture and the filtration situation, it is decided to reduce the injection rate of the fracturing fluid from 50 L / min to 40 L / min to reduce the liquid loss rate. At the same time, increase the viscosity of the fracturing fluid from 10 cP to 20 cP to improve the sand-carrying capacity of the liquid and the fracture support effect. By adjusting the injection rate and the liquid viscosity, the fracture propagation can be better controlled and the fracturing efficiency can be improved.

[0099] Based on the above embodiments, the fracturing strategy optimization method based on the target filtration coefficient can effectively reduce the waste of fracturing fluid and reduce the negative impact of liquid loss on fracture support and propagation. At the same time, adjusting the liquid viscosity can enhance the fracture filling degree, improve the fracture stability, improve the construction efficiency and stimulation effect, so as to achieve the efficient utilization of resources and the maximization of economic benefits.

[0100] As can be seen from the above, a method for determining fracture information provided by an embodiment of this specification uses a distributed optical fiber to obtain acoustic wave data of a target well in a target area, and performs phase-strain conversion processing on the acoustic wave data to obtain a first strain value; obtains the fracturing parameters of the target well, and uses a preset prediction model to determine the first fracture information of the target well according to the fracturing parameters, the preset filtration coefficient, and the initial fracture position; determines a second strain value according to the first fracture information and the optical fiber measurement point coordinates, and determines the model error of the preset prediction model according to the first strain value and the second strain value, and when the model error is not less than a preset error threshold, performs iterative update processing on the preset filtration coefficient and the initial fracture position until the model error is less than the preset error threshold to obtain a target filtration coefficient and a target fracture position; determines the target fracture information of the target well according to the fracturing parameters, the target filtration coefficient, and the target fracture position, and determines a fracturing strategy for the target well according to the target fracture information of the target well and the target filtration coefficient. In this way, using a distributed optical fiber as a sensor can achieve high-precision monitoring of the target well, that is, using a distributed optical fiber can obtain relatively accurate acoustic wave data of the target well, and then, using a preset prediction model, based on fracturing parameters, a preset filtration coefficient, and an initial fracture position, quickly and accurately iteratively invert to obtain target fracture information and a target filtration coefficient, and further accurately and efficiently determine a fracturing strategy for the target well according to the target fracture information and the target filtration coefficient.

[0101] Refer to Figure 2As shown in the figure, the embodiments of the present specification also provide a specific electronic device. Among them, the electronic device includes a network communication port 201, a processor 202, and a memory 203. The above structures are connected by internal cables so that each structure can perform specific data interactions.

[0102] Among them, the network communication port 201 can specifically be used to obtain acoustic wave data of a target well in a target area by using distributed optical fiber, and perform phase strain conversion processing on the acoustic wave data to obtain a first strain value.

[0103] The processor 202 can specifically be used to obtain the fracturing parameters of the target well, and use a preset prediction model to determine the first fracture information of the target well according to the fracturing parameters, a preset filtrate loss coefficient, and an initial fracture position; determine a second strain value according to the first fracture information and the optical fiber measurement point coordinates, and determine the model error of the preset prediction model according to the first strain value and the second strain value, and when the model error is not less than a preset error threshold, perform iterative update processing on the preset filtrate loss coefficient and the initial fracture position until the model error is less than the preset error threshold to obtain a target filtrate loss coefficient and a target fracture position; determine the target fracture information of the target well according to the fracturing parameters, the target filtrate loss coefficient, and the target fracture position, and determine a fracturing strategy for the target well according to the target fracture information of the target well and the target filtrate loss coefficient.

[0104] The memory 203 can specifically be used to store corresponding instruction programs.

[0105] 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 information can be efficiently implemented.

[0106] In this embodiment, the network communication port 501 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.

[0107] 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, a computer-readable medium storing computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an Application Specific Integrated Circuit (ASIC), a programmable logic controller, and a form embedded microcontroller, etc. This specification does not make a limitation.

[0108] In this embodiment, the memory 203 can 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 stick, a TF card, etc.

[0109] The embodiment of this specification also provides a computer-readable storage medium based on the above method for determining a crack information. The computer-readable storage medium stores computer program instructions, which when executed, implement: using a distributed optical fiber to obtain acoustic wave data of a target well in a target area, and performing phase strain conversion processing on the acoustic wave data to obtain a first strain value; obtaining fracturing parameters of the target well, and using a preset prediction model, according to the fracturing parameters, a preset filtration coefficient, and an initial crack position, to determine first crack information of the target well; determining a second strain value according to the first crack information and fiber measurement point coordinates, and according to the first strain value and the second strain value, determining a model error of the preset prediction model, and when the model error is not less than a preset error threshold, performing iterative update processing on the preset filtration coefficient and the initial crack position until the model error is less than the preset error threshold, to obtain a target filtration coefficient and a target crack position; determining target crack information of the target well according to the fracturing parameters, the target filtration coefficient, and the target crack position, and determining a fracturing strategy for the target well according to the target crack information of the target well and the target filtration coefficient.

[0110] In this embodiment, the above storage medium includes but is not limited to a Random Access Memory (RAM), a Read-Only Memory (ROM), a Cache, a Hard Disk Drive (HDD), or a Memory Card. The memory can be used to store computer program instructions. The network communication unit can be set according to standards specified by a communication protocol and is used as an interface for network connection communication.

[0111] In this embodiment, the functions and effects specifically implemented by the program instructions stored in the computer-readable storage medium can be explained in comparison with other embodiments, and will not be elaborated here.

[0112] Refer to Figure 3 , at the software level, the embodiments of this specification also provide a device for determining fracture information, which may specifically include the following structural modules:

[0113] The data acquisition module 301 is configured to use distributed optical fiber to acquire acoustic wave data of a target well in a target area, and perform phase strain conversion processing on the acoustic wave data to obtain a first strain value;

[0114] The first information confirmation module 302 is configured to acquire the fracturing parameters of the target well, and use a preset prediction model to determine the first fracture information of the target well according to the fracturing parameters, a preset fluid loss coefficient, and an initial fracture position;

[0115] The target information confirmation module 303 is configured to determine a second strain value according to the first fracture information and the fiber measurement point coordinates, and determine the model error of the preset prediction model according to the first strain value and the second strain value. When the model error is not less than a preset error threshold, perform iterative update processing on the preset fluid loss coefficient and the initial fracture position until the model error is less than the preset error threshold to obtain a target fluid loss coefficient and a target fracture position;

[0116] The fracturing strategy confirmation module 304 is configured to determine the target fracture information of the target well according to the fracturing parameters, the target fluid loss coefficient, and the target fracture position, and determine a fracturing strategy for the target well according to the target fracture information of the target well and the target fluid loss coefficient.

[0117] In some embodiments, specifically in implementation, the fracturing parameters include fracturing fluid displacement, planar Young's modulus, fracturing fluid viscosity, fracture height, time, and perforation position, and the first fracture information includes fracture half-length, maximum fracture width, and fracture offset.

[0118] In some embodiments, specifically in implementation, the above first information confirmation module 302 determines the fracture half-length according to the preset fluid loss coefficient, the fracturing fluid displacement, the planar Young's modulus, the fracturing fluid viscosity, the fracture height, and the time according to the following formula:

[0119]

[0120] Where a optis the half-length of the crack, k is the filtration coefficient, Q is the displacement rate of the fracturing fluid, E` is the plane Young's modulus, μ is the viscosity of the fracturing fluid, H is the height of the crack, and t is the time;

[0121] Determine the maximum crack width according to the preset filtration coefficient, the displacement rate of the fracturing fluid, the plane Young's modulus, the viscosity of the fracturing fluid, the height of the crack, and the time, according to the following formula:

[0122]

[0123] where w opt is the maximum crack width;

[0124] Determine the crack offset according to the initial crack position and the perforation position.

[0125] In some embodiments, the above data acquisition module 301, specifically in implementation, determines the first strain value according to the laser wavelength, the optical phase difference in the acoustic wave data, as well as the fiber refractive index, the scalar multiplication factor, and the gauge length.

[0126] In some embodiments, the above target information confirmation module 303, specifically in implementation, determines the crack unit coordinates according to the half-length of the crack and the crack offset in the first crack information; determines the crack width distribution corresponding to the maximum crack width according to the crack unit coordinates and the maximum crack width; determines the second strain value according to the crack width distribution corresponding to the maximum crack width, the fiber measurement point coordinates, and the Green's function matrix.

[0127] In some embodiments, the above target information confirmation module 303, specifically in implementation, uses the preset nonlinear least squares method to perform iterative update processing on the preset filtration coefficient and the initial crack position.

[0128] In some embodiments, the above fracturing strategy confirmation module 304, specifically in implementation, when the target filtration coefficient is greater than the preset filtration threshold, determines the reduction amount of the fracturing fluid injection rate for the target well and the increase amount of the liquid viscosity for the target well according to the target crack information of the target well; determines the fracturing strategy for the target well according to the reduction amount and the increase amount.

[0129] It should be noted that the units, devices, modules, etc. illustrated 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 implementing the same function can be realized by the 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. In actual implementation, there may be other division methods. 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 couplings or direct couplings or communication connections with each other can be through some interfaces. The indirect couplings or communication connections of the devices or units can be in electrical, mechanical or other forms.

[0130] As can be seen from the above, based on a device for determining fracture information provided by an embodiment of this specification, by using a distributed optical fiber as a sensor, high-precision monitoring of a target well can be achieved. That is, by using the distributed optical fiber, acoustic wave data of the target well with relatively high precision can be obtained. Then, by using a preset prediction model, based on fracturing parameters, a preset fluid loss coefficient, and an initial fracture position, the target fracture information and the target fluid loss coefficient can be obtained through rapid and accurate iterative inversion. Furthermore, based on the target fracture information and the target fluid loss coefficient, a fracturing strategy for the target well can be accurately and efficiently determined.

[0131] In a specific scenario example, a method and device for determining fracture information provided by this specification can be applied. By using a distributed optical fiber as a sensor, high-precision monitoring of a target well can be achieved. That is, by using the distributed optical fiber, acoustic wave data of the target well with relatively high precision can be obtained. Then, by using a preset prediction model, based on fracturing parameters, a preset fluid loss coefficient, and an initial fracture position, the target fracture information and the target fluid loss coefficient can be obtained through rapid and accurate iterative inversion. Furthermore, based on the target fracture information and the target fluid loss coefficient, a fracturing strategy for the target well can be accurately and efficiently determined. The specific implementation process can include the following content.

[0132] In some embodiments, by using distributed optical fiber acoustic wave data (i.e., Distributed Acoustic Sensing, DAS data) to invert the fracture geometry model, the specific implementation is as follows:

[0133] Refer to Figure 4As shown, the distributed fiber optic acoustic data collected during 200 minutes of fracturing can be input first, and the distributed fiber optic acoustic data is converted into real strain values (i.e., the first strain values) through the phase-strain conversion formula. The fracturing parameters recorded during 200 minutes of fracturing are input, including the fracturing fluid displacement, planar Young's modulus, fracturing fluid viscosity, fracture height, time, perforation location. The corresponding fracture half-length and maximum fracture width are calculated through a preset filtration coefficient and a preset prediction model (i.e., the PKN model introducing the preset filtration coefficient). Among them, the preset filtration coefficient is taken to be close to the actual value, that is, between 0.1 and 0.3, and the fracture offset is determined according to the fracturing implementation plan. The three parameters of the above fracture half-length, maximum fracture width, and fracture offset (the first fracture information) are used as the inversion initial values to be input into the objective function, the fracture width distribution is calculated, and the simulated strain values (i.e., the second strain values) are calculated through coordinate transformation and the displacement discontinuity method. The real strain values (i.e., the first strain values) are input to compare with the simulated strain values (i.e., the second strain values), and the solution is obtained through iterative calculation using a preset non-linear least squares algorithm (Levenberg-Marquardt, LM). The fracture information corresponding to each group of data is calculated, and the corresponding fracture volume is calculated therefrom. By comparing the inverted fracture volume with the fracturing fluid injection volume, it can be found that the ratio of the inverted volume to the fracturing fluid volume is larger in the initial stage of fracturing, but this ratio is continuously decreasing as the fracturing continues. The ratio in the stable section is taken and the average value is calculated. The error between the average value and the filtration coefficient range is generally less than 15%, that is, the ratio of the fracture volume to the fracturing fluid volume can be considered as the target filtration coefficient varying with time. Finally, the target fracture information (i.e., the target fracture half-length, target maximum fracture width, target fracture location) and the target filtration coefficient that meet the real strain values are output.

[0134] 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 step sequences listed in the embodiments are only one way among numerous step execution sequences and do not represent the only execution sequence. When the actual device or client product is executed, it can be executed in the method sequence 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 term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, product or device comprising a series of elements not only includes those elements but also includes other elements not explicitly listed, or further includes elements inherent to such process, method, product or device. Without further limitations, there is no exclusion of additional identical or equivalent elements in the process, method, product or device comprising the said elements. The words such as first, second, etc. are used to represent names and do not represent any specific order.

[0135] 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 considered 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.

[0136] 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.

[0137] 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 information, characterized in that: include: Acquiring acoustic wave data of a target well in a target area by using a distributed optical fiber, and performing phase-strain conversion processing on the acoustic wave data to obtain a first strain value; Acquire the fracturing parameters of the target well, and determine the first fracture information of the target well according to the fracturing parameters, the preset fluid loss coefficient and the initial fracture position by using a preset prediction model; Determine a second strain value according to the first crack information and the optical fiber measuring point coordinates, determine a model error of the preset prediction model according to the first strain value and the second strain value, and iteratively update the preset filter loss coefficient and the initial crack position when the model error is not less than a preset error threshold, until the model error is less than the preset error threshold, and obtain a target filter loss coefficient and a target crack position; The target fracture information of the target well is determined according to the fracturing parameters, the target fluid loss coefficient and the target fracture position, and the fracturing strategy for the target well is determined according to the target fracture information and the target fluid loss coefficient of the target well.

2. The method according to claim 1, characterized in that The fracturing parameters include fracturing fluid displacement, plane Young's modulus, fracturing fluid viscosity, fracture height, time, and perforation position. The first fracture information includes fracture half-length, maximum fracture width, and the fracture offset.

3. The method according to claim 2, characterized in that Determining the first fracture information of the target well according to the fracturing parameters, the preset fluid loss coefficient and the initial fracture position includes: According to the preset fluid loss coefficient, the fracturing fluid displacement, the plane Young's modulus, the fracturing fluid viscosity, the fracture height and the time, the fracture half-length is determined according to the following formula: Among them, a opt is the half length of the fracture, k is the filtration coefficient, Q is the displacement of the fracturing fluid, E' is the plane Young's modulus, μ is the viscosity of the fracturing fluid, H is the fracture height, and t is the time; According to the preset fluid loss coefficient, the fracturing fluid displacement, the plane Young's modulus, the fracturing fluid viscosity, the fracture height and the time, the maximum fracture width is determined according to the following formula: Among them, w opt is the maximum seam width; The fracture offset is determined according to the initial fracture position and the perforation position.

4. The method according to claim 1, characterized in that: The performing phase strain conversion processing on the acoustic wave data to obtain a first strain value includes: The first strain value is determined according to the laser wavelength, optical phase difference, optical fiber refractive index, scalar multiplication factor and gauge length in the acoustic wave data.

5. The method according to claim 3, characterized in that: The determining the second strain value according to the first crack information and the optical fiber measuring point coordinates includes: Determining the coordinates of a crack unit according to the crack half-length and the crack offset in the first crack information; Determining a crack width distribution corresponding to the maximum crack width according to the crack unit coordinates and the maximum crack width; The second strain value is determined according to the slit width distribution corresponding to the maximum slit width, the optical fiber measuring point coordinates and the Green's function matrix.

6. The method according to claim 5, characterized in that The iterative updating process of the preset filtration coefficient and the initial crack position includes: The preset nonlinear least square method is used to iteratively update the preset filtration coefficient and the initial crack position.

7. The method according to claim 1, characterized in that Determining a fracturing strategy for the target well according to the target fracture information and the target fluid loss coefficient of the target well includes: When the target fluid loss coefficient is greater than a preset fluid loss threshold, determining a reduction in the fracturing fluid injection rate for the target well and an increase in the liquid viscosity for the target well according to target fracture information of the target well; A fracturing strategy for the target well is determined according to the reduction amount and the increase amount.

8. A device for determining crack information, characterized in that: include: A data acquisition module, used to acquire acoustic wave data of a target well in a target area by using distributed optical fibers, and perform phase-strain conversion processing on the acoustic wave data to obtain a first strain value; A first information confirmation module is used to obtain the fracturing parameters of the target well, and determine the first fracture information of the target well according to the fracturing parameters, the preset fluid loss coefficient and the initial fracture position by using a preset prediction model; a target information confirmation module, used to determine a second strain value according to the first crack information and the optical fiber measuring point coordinates, and determine a model error of the preset prediction model according to the first strain value and the second strain value, and iteratively update the preset filtration coefficient and the initial crack position when the model error is not less than a preset error threshold, until the model error is less than the preset error threshold, so as to obtain a target filtration coefficient and a target crack position; A fracturing strategy confirmation module is used to determine the target fracture information of the target well according to the fracturing parameters, the target fluid loss coefficient and the target fracture position, and to determine the fracturing strategy for the target well according to the target fracture information and the target fluid loss coefficient of the target well.

9. An electronic device, characterized in that: It comprises a processor and a memory for storing instructions executable by the processor, and when the processor executes the instructions, the steps of the method for determining crack information 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, and when the instructions are executed by a processor, the steps of the method for determining crack information according to any one of claims 1 to 7 are implemented.

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