Ground stress prediction method and device based on random forest, electronic equipment and medium

Abstract

The invention provides a crustal stress prediction method and device based on a random forest, electronic equipment and a medium, and relates to the field of geophysical exploration, and the method comprises the steps: obtaining seismic data of a to-be-measured region; processing the seismic data, and determining Young modulus and curvature attributes; and processing the Young modulus, the curvature attribute and the product of the Young modulus and the curvature attribute of the to-be-measured area by adopting the trained crustal stress prediction model based on the random forest to obtain the target crustal stress of the to-be-measured area. According to the method, a random forest (RF) model taking curvature attributes, Young moduli and products of the curvature attributes and the Young moduli as training samples is constructed and is used for predicting crustal stress. The model shows extremely high prediction precision, the square correlation coefficient between an actual value and a predicted value reaches 0.95, and the crustal stress prediction accuracy is improved while the crustal stress prediction efficiency is guaranteed.

Description

Technical Field

[0001] The present application relates to the field of geophysical exploration, and more specifically, to a method, device, electronic equipment and medium for predicting ground stress based on random forests. Background Art

[0002] As global energy demand continues to grow, traditional oil and gas resources are gradually being depleted. This challenge has prompted the global energy industry to accelerate its transformation to the development of unconventional oil and gas resources, among which shale gas has received special attention due to its huge development potential and abundant resource reserves. The development of unconventional oil and gas resources is not only an important part of my country's energy security strategy, but also plays a key role in the sustainable development of the economy. Traditional geostress prediction methods often have limited accuracy and it is difficult to capture the nonlinear relationship between complex geological factors and geostress. In the application of machine learning in geostress prediction methods, current methods also have limitations, such as difficulty in providing continuous stress distribution between wells. Summary of the invention

[0003] The purpose of the embodiments of the present application is to provide a method, device, electronic device and medium for predicting ground stress based on random forest, so as to solve the above-mentioned problems existing in the prior art and improve the prediction accuracy of ground stress.

[0004] In a first aspect, a method for predicting ground stress based on random forest is provided, and the method may include:

[0005] Obtaining seismic data of the area to be measured;

[0006] Processing the seismic data to determine Young's modulus and curvature attributes; the Young's modulus represents the elasticity value of the underground medium in the area to be measured; the curvature attribute represents the deformation and bending degree value of the underground structure in the area to be measured;

[0007] The trained random forest-based geostress prediction model is used to process the Young's modulus, the curvature attribute, and the product of the Young's modulus and the curvature attribute of the area to be measured to obtain the target geostress of the area to be measured.

[0008] In one possible implementation, determining Young's modulus includes:

[0009] The seismic data are processed by using pre-stack simultaneous inversion technology to obtain Young's modulus.

[0010] In a possible implementation, the training process of the geostress prediction model includes:

[0011] Acquire historical seismic data and corresponding multiple historical well logging data in the area to be tested;

[0012] Process the historical seismic data to obtain the historical Young's modulus and historical curvature attributes corresponding to each point in the gather corresponding to the historical seismic data;

[0013] For the historical Young's modulus and historical curvature attribute of any point, the historical Young's modulus, the historical curvature attribute and the product of the historical Young's modulus and the historical curvature attribute are used as training samples;

[0014] Based on multiple groups of training samples and training labels determined by corresponding historical logging data, the configured random forest-based training model is trained to obtain a trained geostress prediction model.

[0015] In a possible implementation, based on multiple sets of training samples and training labels determined by corresponding well logging data, the configured random forest-based training model is trained, including:

[0016] For any training sample corresponding to any historical earthquake data, the configured random forest-based training model is trained according to the training sample and the corresponding training label to obtain a decision tree;

[0017] Based on the obtained multiple decision trees, the initial geostress prediction model is determined.

[0018] In a possible implementation, after determining the initial geostress prediction model, the method further includes:

[0019] The performance of the initial geostress prediction model is tested through the test set:

[0020] If the test result of the performance test meets the preset test result, the initial geostress prediction model is determined as the geostress prediction model.

[0021] In a possible implementation, the performance test of the initial geostress prediction model is performed using a test set, including:

[0022] The square correlation coefficient and / or the mean absolute percentage error are used to process the predicted values ​​obtained by processing the test samples in the test set using the initial geostress prediction model and the test labels corresponding to the test samples to obtain the test results.

[0023] In a possible implementation, obtaining the target geostress of the area to be measured includes:

[0024] By using any decision tree in the geostress prediction model, the Young's modulus, the curvature attribute and the product of the Young's modulus and the curvature attribute of the area to be measured are processed to obtain the initial geostress;

[0025] The target geostress is obtained by calculating the average value of the multiple initial geostresses.

[0026] In a second aspect, a random forest-based ground stress prediction device is provided, which may include:

[0027] An acquisition unit, used for acquiring seismic data of a region to be measured;

[0028] A processing unit, used to process the seismic data to determine Young's modulus and curvature attribute; the Young's modulus represents the elastic value of the underground medium in the area to be measured; the curvature attribute represents the deformation and bending degree value of the underground structure in the area to be measured;

[0029] And, using a trained random forest-based geostress prediction model, the Young's modulus, the curvature attribute and the product of the Young's modulus and the curvature attribute of the area to be measured are processed to obtain the target geostress of the area to be measured.

[0030] In a third aspect, an electronic device is provided, the electronic device comprising a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus;

[0031] Memory, used to store computer programs;

[0032] The processor is used to implement any method step described in the first aspect when executing the program stored in the memory.

[0033] In a fourth aspect, a computer-readable storage medium is provided, wherein a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, any method step described in the first aspect is implemented.

[0034] The present application provides a method for predicting geostress based on random forests, the method comprising: obtaining seismic data of the area to be measured; processing the seismic data to determine the Young's modulus and curvature attributes; using a trained geostress prediction model based on random forests to process the Young's modulus, curvature attributes and the product of the Young's modulus and the curvature attributes of the area to be measured, and obtaining the target geostress of the area to be measured. The present application constructs a random forest (RF) model with curvature attributes, Young's modulus and their product as training samples for predicting geostress. The model shows extremely high prediction accuracy, and the square correlation coefficient between the actual value and the predicted value reaches 0.95. The present application improves the accuracy of geostress prediction while ensuring the efficiency of predicting geostress. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0036] Figure 1 A system architecture diagram for a random forest-based ground stress prediction method provided in an embodiment of the present application;

[0037] Figure 2 A schematic diagram of a flow chart of a method for predicting ground stress based on random forests provided in an embodiment of the present application;

[0038] Figure 3 A schematic diagram of a cross-sectional diagram provided for an embodiment of the present application;

[0039] Figure 4 A schematic diagram of the structure of a random forest-based ground stress prediction device provided in an embodiment of the present application;

[0040] Figure 5 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments of the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0042] For ease of understanding, the terms involved in the embodiments of the present application are explained below:

[0043] Seismic data: refers to the information obtained through seismic exploration technology, which can specifically include: reflection seismic data, refraction seismic data, vertical profile seismic data, microseismic monitoring data, seismic wave data and passive source seismic data, etc.

[0044] Young's modulus (Elastic Modulus or Young's Modulus): It is a basic parameter in material mechanics, which describes the material's ability to resist stretching or compression under external forces within the elastic range. In seismic research, the speed of seismic waves (including P waves and S waves) depends on the elastic modulus (including Young's modulus) of the medium and its density. Generally speaking, the higher the Young's modulus, the faster the seismic wave propagates in the medium. Therefore, by measuring the seismic wave velocity, the distribution of Young's modulus of underground rocks can be indirectly inferred.

[0045] Curvature attribute: In seismic exploration, curvature analysis is a technique used to extract information about the morphology of geological interfaces, which can better understand the details of underground structures. The curvature attribute refers to the degree of curvature of the seismic reflection layer in space. It can reveal the geometric changes of the strata and is very useful for identifying faults, folds and other structural features.

[0046] The random forest-based ground stress prediction method provided in the embodiment of the present application can be applied to Figure 1 In the system architecture shown in Figure 1 As shown, the system may include: a server and a terminal. The server may be a physical server, or a server cluster or distributed system composed of multiple physical servers, or a cloud server that provides cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDN), and basic cloud computing services such as big data and artificial intelligence platforms. The terminal may be a user equipment (UE) such as a mobile phone, smart phone, laptop, digital broadcast receiver, personal digital assistant (PDA), tablet computer (PAD), handheld device, vehicle-mounted device, wearable device, computing device or other processing device connected to a wireless modem, mobile station (MS), mobile terminal, etc. The terminal and the server may be directly or indirectly connected via wired or wireless communication, which is not limited in this application.

[0047] The terminal is used to receive the seismic data of the area to be measured input by the user and send the seismic data to the server.

[0048] The server is used to obtain the seismic data sent by the terminal to execute a random forest-based ground stress prediction method provided in this application.

[0049] As global energy demand continues to grow, traditional oil and gas resources are gradually being depleted. This challenge has prompted the global energy industry to accelerate its transformation to the development of unconventional oil and gas resources, among which shale gas has received special attention due to its huge development potential and abundant resource reserves. The development of unconventional oil and gas resources is not only an important part of my country's energy security strategy, but also plays a key role in the sustainable development of the economy. Traditional geostress prediction methods often find it difficult to capture the nonlinear relationship between complex geological factors and geostress. At present, machine learning also has limitations in the application of geostress prediction methods, and it is difficult to provide continuous stress distribution between wells.

[0050] Therefore, the present application provides a ground stress prediction method based on random forest to solve the above-mentioned problems existing in the prior art and improve the prediction accuracy of ground stress.

[0051] The preferred embodiments of the present application are described below in conjunction with the drawings in the specification. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present application, and are not used to limit the present application. In addition, the embodiments and features in the embodiments of the present application may be combined with each other if there is no conflict.

[0052] Figure 2 A schematic diagram of a flow chart of a method for predicting ground stress based on random forests provided in an embodiment of the present application. Figure 2 As shown, the method may include:

[0053] Step S210: Process the acquired seismic data to determine Young's modulus and curvature attributes.

[0054] Specifically, the pre-stack simultaneous inversion technique is used to calculate the Young's modulus from the seismic waves in the seismic data, and the curvature attributes are extracted from the seismic data.

[0055] Pre-stack simultaneous inversion technology is that the seismic waves received by the ground detectors will change with the change of the offset after passing through the complex stratum structure and rocks with different physical properties. In short, the amplitude of the seismic reflection wave changes with the change of the offset, which is the basis for seismic inversion to obtain rock formation characteristics.

[0056] It should be noted that seismic data is a set of time series data, and the seismic data is preprocessed, such as denoising and filtering, to improve the quality of the seismic data.

[0057] The Fatti equation is a mathematical equation that describes the propagation and reflection of seismic waves between different media inside the earth and is used to calculate the reflection and transmission coefficients of seismic waves between two media.

[0058] When seismic waves are incident on an elastic interface, there is a certain relationship between the amplitude energy, reflection coefficient and formation parameters of the seismic waves. The Aki-Richards equation is a linearization of the Zoeppirtz equation, expressing the P-wave and S-wave velocities and densities in separate terms, so as to better perform inversion analysis.

[0059] The Aki-Richards equation is:

[0060] The Fatti equation is rewritten based on the Aki-Richards equation. It can control the inversion noise level and improve the resolution. It makes the variables more correlated and the algorithm more stable.

[0061] The Fatti equation is:

[0062] R pp (θ)=C1R p +C2R s +C3R D

[0063] Among them, R pp (θ) is the longitudinal wave reflection coefficient, which changes with the incident angle θ; C i and R j are the first influence coefficient and the second influence coefficient respectively.

[0064]

[0065]

[0066] Among them, γ is the ratio of shear wave velocity to longitudinal wave velocity, ΔVp is the change in longitudinal wave velocity, Vp is the longitudinal wave velocity, Vs is the shear wave velocity, ΔVs is the change in shear wave velocity, ρ is the density, and Δρ is the change in density.

[0067] First, we start from the low-frequency model and use the Fatti equation for inversion. The low-frequency model includes longitudinal wave impedance, shear wave impedance, and density model. Based on the Fatti equation, we can get R p , R s , R D , given the incident angle θ and the value of γ, the three coefficients c1, c2, and c3 can be obtained. Then the reflection coefficient corresponding to the angle θ can be obtained. By convolving the wavelet and the reflection coefficient, the forward synthesized track gather can be obtained. The initial model is corrected by calculating the residuals of the synthesized track gather and the actual track gather. Continuously iterate to obtain the final inversion result. Combine the obtained inversion results with the existing geological data (such as logging data downhole), and establish a mathematical model between Young's modulus and other parameters (such as density and wave velocity).

[0068] The prestack simultaneous inversion technique is used to estimate the Young's modulus based on parameters such as the ratio of longitudinal and transverse wave velocities and density.

[0069] And, smoothing the seismic reflection wave data.

[0070] Calculate the gradient of the reflecting interface in the area to be measured.

[0071] The curvature is calculated using the gradient information, that is, the curvature attribute is obtained.

[0072] Step S220: Using the trained random forest-based geostress prediction model, the Young's modulus, the curvature attribute, and the product of the Young's modulus and the curvature attribute of the area to be measured are processed to obtain the target geostress of the area to be measured.

[0073] Before executing step S220, the training process of the geostress prediction model may include:

[0074] Acquire historical seismic data and multiple historical well logging data in the area to be tested;

[0075] The historical seismic data in the area to be measured are processed to obtain the historical Young's modulus and historical curvature attribute corresponding to each point in the gather corresponding to the historical seismic data; the processing process is the same as step S210. It should be noted that each point in the gather is the intersection of the horizontal and vertical axes in the coordinate system corresponding to the area to be measured after being divided according to a certain horizontal and vertical spacing, and each intersection is called a point; therefore, the historical seismic data is a gather, and the gather includes multiple points, and each point corresponds to a historical Young's modulus and a historical curvature attribute.

[0076] It should be noted that the area to be tested mentioned in the model training process can be at least one of an area that is exactly the same as the area to be tested in the actual application, an area that partially overlaps with the area to be tested, an area adjacent to the area to be tested, and an area with the same geological characteristics as the area to be tested.

[0077] For the historical Young's modulus and historical curvature attributes of any point in the gather corresponding to the historical seismic data, the historical Young's modulus, the historical curvature attribute, the product of the historical Young's modulus and the historical curvature attribute, and the historical geostress determined by the historical logging data corresponding to the historical seismic data, form a historical data set.

[0078] The product of the curvature attribute and Young's modulus approximately represents the formation stress.

[0079] The historical data set (multiple historical data sets) corresponding to each point is divided into a test set and a training set according to a certain ratio.

[0080] The historical Young's modulus, historical curvature attribute, and the product of the historical Young's modulus and the historical curvature attribute in each historical data set corresponding to the training set are used as training samples; that is, the training sample set is obtained: D b = BootstraSample(D), where b represents b sub-sample sets; the corresponding historical geostress is used as the training label. Among them, any sub-sample set can extract each data in the training sample set according to the characteristics of the training label, that is, the sub-sample set consists of at least one training sample.

[0081] Similarly, the historical Young's modulus, historical curvature attribute, and the product of historical Young's modulus and historical curvature attribute in each historical data set corresponding to the test set are used as test samples; and the corresponding historical geostress is used as a test label.

[0082] Afterwards, based on multiple sub-sample sets and corresponding training labels, the configured random forest-based model to be trained is trained to obtain a trained initial geostress prediction model including multiple decision trees.

[0083] Specifically, for any sub-sample set, the sub-sample set and the corresponding training labels are processed to obtain a decision tree.

[0084] According to the obtained multiple decision trees, an initial geostress prediction model is determined. That is, the trained initial geostress prediction model may include b decision trees.

[0085] It should be noted that if the same situation exists in multiple sub-sample sets and the corresponding sample labels are the same, only one set of the same sub-sample sets and corresponding sample labels is retained. At this time, the number of decision trees generated is less than the number of sub-sample sets; if the same situation exists in multiple sub-sample sets and the corresponding sample labels are different, at this time, the time features (or other features) of the same sub-sample sets are extracted to generate a new decision tree.

[0086] In some embodiments, the performance of the initial geostress prediction model is tested using a test set:

[0087] If the test result meets the preset test result, the initial geostress prediction model is determined as the geostress prediction model.

[0088] If the test result does not meet the preset test result, the b decision trees in the initial geostress prediction model are retrained until the test result meets the preset test result.

[0089] Furthermore, the method of performing performance testing on the initial geostress prediction model through the test set may include:

[0090] The square correlation coefficient and / or mean absolute percentage error are used to process the predicted values ​​obtained by processing the test samples in the test set using the initial geostress prediction model and the test labels corresponding to the test samples to obtain the test results.

[0091] The calculation formula of the square correlation coefficient R is as follows:

[0092]

[0093] The calculation formula of mean absolute percentage error AAPE is as follows:

[0094]

[0095] Among them, y iactual is the test label, y ipredicted is the predicted value, N is the number of test sets involved in the test process, is the average value of the test labels, is the average of the predicted values.

[0096] Furthermore, the preset test results are generally set standard square correlation coefficients and standard mean absolute percentage errors. When using one method (square correlation coefficient or mean absolute percentage error) to test the performance of the initial geostress prediction model, if the obtained square correlation coefficient or mean absolute percentage error reaches the corresponding standard square correlation coefficient or standard mean absolute percentage error, it indicates that the test result meets the preset test result, otherwise it indicates that the test result does not meet the preset test result.

[0097] When the performance of the initial geostress prediction model is tested using two methods (square correlation coefficient or mean absolute percentage error), when the obtained square correlation coefficient and mean absolute percentage error reach the corresponding standard square correlation coefficient and standard mean absolute percentage error, it indicates that the test results meet the preset test results, otherwise it indicates that the test results do not meet the preset test results.

[0098] In some embodiments, in combination Figure 3 As shown, the test accuracy of the initial geostress prediction model on the test set can be displayed by drawing a cross-plot, analyzing the difference between the test labels and the predicted values, and ensuring the consistency of the data distribution.

[0099] Step S220 may specifically include:

[0100] Through each decision tree in the trained geostress prediction model, the Young's modulus, curvature attribute and the product of the Young's modulus and curvature attribute of the measured area are processed to obtain the initial geostress;

[0101] The target geostress is obtained by calculating the average value of the multiple initial geostresses.

[0102] The process can be expressed as:

[0103]

[0104] Among them, T b (x) is the initial geostress of the Young's modulus, curvature attribute and the product of Young's modulus and curvature attribute of the b decision trees for the area to be tested, is the target ground stress.

[0105] In some embodiments, when multiple seismic data of the area to be tested are obtained and the data vary with the spatial position, the seismic data at different spatial positions are labeled according to the spatial position, and the labeled seismic data are processed to obtain the labeled Young's modulus and curvature attributes; the labeled Young's modulus and curvature attributes are input into a trained geostress prediction model to obtain a continuous target geostress, thereby determining the distribution of the target geostress, that is, providing a continuous stress distribution between wells.

[0106] This method can use the predicted target geostress to guide the hydraulic fracturing development of shale reservoirs, evaluate the areas in the formation that are prone to cracks, and optimize the mining plan. In other words, hydraulic fracturing is the main means of increasing production in shale gas development. Geostress prediction can help determine the most suitable fracturing area and fracturing parameters, optimize fracturing design, and improve fracturing effects, thereby increasing the production capacity of shale gas wells, and further improve the efficiency and safety of resource extraction.

[0107] The present application provides a method for predicting geostress based on random forests, the method comprising: obtaining seismic data of the area to be measured; processing the seismic data to determine the Young's modulus and curvature attributes; using a trained geostress prediction model based on random forests to process the Young's modulus, curvature attributes and the product of the Young's modulus and the curvature attributes of the area to be measured, and obtaining the target geostress of the area to be measured. The present application constructs a random forest (RF) model with curvature attributes, Young's modulus and their product as training samples for predicting geostress. The model shows extremely high prediction accuracy, and the square correlation coefficient between the actual value and the predicted value reaches 0.95. The present application improves the accuracy of geostress prediction while ensuring the efficiency of predicting geostress.

[0108] Corresponding to the above method, the embodiment of the present application also provides a ground stress prediction device based on random forest, such as Figure 4 As shown, the device comprises:

[0109] An acquisition unit 410 is used to acquire seismic data of a region to be measured;

[0110] The processing unit 420 is used to process the seismic data to determine Young's modulus and curvature attributes; the Young's modulus represents the elastic value of the underground medium in the area to be measured; the curvature attribute represents the deformation and bending degree of the underground structure in the area to be measured;

[0111] And, using a trained random forest-based geostress prediction model, the Young's modulus, the curvature attribute and the product of the Young's modulus and the curvature attribute of the area to be measured are processed to obtain the target geostress of the area to be measured.

[0112] The functions of each functional unit of a random forest-based geostress prediction device provided in the above-mentioned embodiment of the present application can be realized through the above-mentioned method steps. Therefore, the specific working process and beneficial effects of each unit in a random forest-based geostress prediction device provided in the embodiment of the present application will not be repeated here.

[0113] The present application also provides an electronic device, such as Figure 5 As shown, it includes a processor 510 , a communication interface 520 , a memory 530 and a communication bus 540 , wherein the processor 510 , the communication interface 520 , and the memory 530 communicate with each other via the communication bus 540 .

[0114] Memory 530, for storing computer programs;

[0115] The processor 510 is used to execute the program stored in the memory 530 to implement the following steps:

[0116] Obtaining seismic data of the area to be measured;

[0117] Processing the seismic data to determine Young's modulus and curvature attributes; the Young's modulus represents the elasticity value of the underground medium in the area to be measured; the curvature attribute represents the deformation and bending degree value of the underground structure in the area to be measured;

[0118] The trained random forest-based geostress prediction model is used to process the Young's modulus, the curvature attribute, and the product of the Young's modulus and the curvature attribute of the area to be measured to obtain the target geostress of the area to be measured.

[0119] The communication bus mentioned above can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. The communication bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one thick line is used in the figure, but it does not mean that there is only one bus or one type of bus.

[0120] The communication interface is used for communication between the above electronic device and other devices.

[0121] The memory may include a random access memory (RAM) or a non-volatile memory (NVM), such as at least one disk memory. Optionally, the memory may also be at least one storage device located away from the aforementioned processor.

[0122] The above-mentioned processor can be a general-purpose processor, including a central processing unit (CPU), a network processor (NP), etc.; it can also be a digital signal processor (DSP), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components.

[0123] The implementation methods and beneficial effects of the components of the electronic device in the above embodiments to solve the problems can be seen in Figure 2 The various steps in the illustrated embodiment are implemented, therefore, the specific working process and beneficial effects of the electronic device provided by the embodiment of the present application are not repeated here.

[0124] In another embodiment provided in the present application, a computer-readable storage medium is also provided, in which instructions are stored. When the computer-readable storage medium is run on a computer, the computer executes a random forest-based geostress prediction method described in any of the above embodiments.

[0125] In another embodiment provided in the present application, a computer program product comprising instructions is also provided. When the computer program product is run on a computer, the computer executes a random forest-based geostress prediction method described in any one of the above embodiments.

[0126] Those skilled in the art will appreciate that the embodiments in the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt a complete hardware embodiment, a complete software embodiment, or a form of an embodiment combining software and hardware. Moreover, the present application may adopt a form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0127] The embodiments of the present application are described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0128] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0129] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0130] Unless otherwise defined, the technical terms or scientific terms used in this application should be understood by people with ordinary skills in the field to which the present invention belongs. "First", "second" and similar words used in this application do not indicate any order, quantity or importance, but are only used to distinguish different components. "Include" or "comprise" and similar words mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect", "couple" or "connected" and similar words are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

[0131] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they are aware of the basic creative concepts. Therefore, the present application embodiments are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application embodiments.

[0132] Obviously, those skilled in the art can make various changes and modifications to the embodiments in the present application without departing from the spirit and scope of the embodiments in the present application. Thus, if these modifications and variations of the embodiments in the present application are within the scope of the embodiments in the present application and their equivalents, the embodiments in the present application are also intended to include these modifications and variations.

Claims

1. A method for predicting ground stress based on random forest, characterized in that: The method comprises: Obtaining seismic data of the area to be measured; Processing the seismic data to determine Young's modulus and curvature attributes; the Young's modulus represents the elasticity value of the underground medium in the area to be measured; the curvature attribute represents the deformation and bending degree value of the underground structure in the area to be measured; The trained random forest-based geostress prediction model is used to process the Young's modulus, the curvature attribute, and the product of the Young's modulus and the curvature attribute of the area to be measured to obtain the target geostress of the area to be measured.

2. The method according to claim 1, characterized in that Determine Young's modulus, including: The seismic data are processed by using pre-stack simultaneous inversion technology to obtain Young's modulus.

3. The method according to claim 1, characterized in that The training process of the geostress prediction model includes: Acquire historical seismic data and corresponding multiple historical well logging data in the area to be tested; Process the historical seismic data to obtain the historical Young's modulus and historical curvature attributes corresponding to each point in the gather corresponding to the historical seismic data; For the historical Young's modulus and historical curvature attribute of any point, the historical Young's modulus, the historical curvature attribute and the product of the historical Young's modulus and the historical curvature attribute are used as training samples; Based on multiple groups of training samples and training labels determined by corresponding historical logging data, the configured random forest-based training model is trained to obtain a trained geostress prediction model.

4. The method according to claim 3, characterized in that Based on multiple sets of training samples and training labels determined by corresponding logging data, the configured random forest-based training model is trained, including: For any training sample corresponding to any historical earthquake data, the configured random forest-based training model is trained according to the training sample and the corresponding training label to obtain a decision tree; Based on the obtained multiple decision trees, the initial geostress prediction model is determined.

5. The method according to claim 4, characterized in that After determining the initial geostress prediction model, the method further includes: The performance of the initial geostress prediction model is tested through the test set: If the test result of the performance test meets the preset test result, the initial geostress prediction model is determined as the geostress prediction model.

6. The method according to claim 5, characterized in that The performance test of the initial geostress prediction model is performed through a test set, including: The square correlation coefficient and / or the mean absolute percentage error are used to process the predicted values ​​obtained by processing the test samples in the test set using the initial geostress prediction model and the test labels corresponding to the test samples to obtain the test results.

7. The method according to claim 4, characterized in that Obtaining the target geostress of the area to be measured includes: By using any decision tree in the geostress prediction model, the Young's modulus, the curvature attribute and the product of the Young's modulus and the curvature attribute of the measured area are processed to obtain the initial geostress; The target geostress is obtained by calculating the average value of the multiple initial geostresses.

8. A ground stress prediction device based on random forest, characterized in that: The device comprises: An acquisition unit, used for acquiring seismic data of a region to be measured; A processing unit, used to process the seismic data to determine Young's modulus and curvature attribute; the Young's modulus represents the elastic value of the underground medium in the area to be measured; the curvature attribute represents the deformation and bending degree value of the underground structure in the area to be measured; And, using a trained random forest-based geostress prediction model, the Young's modulus, the curvature attribute and the product of the Young's modulus and the curvature attribute of the area to be measured are processed to obtain the target geostress of the area to be measured.

9. An electronic device, characterized in that: The electronic device comprises a processor, a communication interface, a memory and a communication bus, wherein the processor, the communication interface and the memory communicate with each other via the communication bus; Memory, used to store computer programs; A processor, for implementing the method steps described in any one of claims 1 to 7 when executing a program stored in a memory.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method steps described in any one of claims 1 to 7 are implemented.

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