Prediction method, device and equipment for bedrock buried hill inner crack and storage medium

Abstract

The invention discloses a prediction method and device for bedrock buried hill inner cracks, equipment and a storage medium. The method is characterized by comprising the following steps: acquiring predicted seismic data, and extracting a plurality of predicted seismic data volumes according to the predicted seismic data; obtaining predicted logging data, and generating a predicted crack curve according to the predicted logging data; and performing fracture prediction according to the predicted seismic data volume and the predicted fracture curve, and determining the fracture development degree of the target interval. According to the method, fine prediction of the bedrock buried hill interior crack is realized, the prediction efficiency of the bedrock buried hill interior crack is improved, the actual situation of the bedrock buried hill interior crack is better fitted through participation of actual production materials, and the prediction accuracy of favorable zones and crack development is improved.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum exploration, and in particular to a method, device, equipment and storage medium for predicting inner cracks of bedrock buried hills. Background Art

[0002] Bedrock buried hills are a combination of metamorphic rocks or igneous rocks of different metamorphic degrees below the normal sedimentary sequence in a sedimentary basin. Bedrock buried hills usually contain rich oil and gas resources, and the study of bedrock buried hill oil and gas reservoirs is of great value and significance. Buried hill oil and gas reservoirs show more complex geological structures, serious reservoir heterogeneity, and complex and variable reservoir-forming elements. The existing technology mainly studies buried hill oil and gas reservoirs with conventional geological methods or logging methods. Due to the complex distribution of fractures in bedrock buried hill oil and gas reservoirs, the research is difficult. The existing technology is cumbersome for predicting fractures in bedrock buried hill oil and gas reservoirs, and the prediction efficiency and accuracy are low. Summary of the invention

[0003] The present invention provides a method, device, electronic equipment and storage medium for predicting internal cracks in a bedrock buried hill, so as to achieve efficient and accurate prediction of internal cracks in a bedrock buried hill.

[0004] According to one aspect of the present invention, a method for predicting internal fractures in a bedrock buried hill is provided, comprising:

[0005] Acquiring predicted seismic data, and extracting a plurality of predicted seismic data volumes according to the predicted seismic data;

[0006] Acquiring predicted logging data, and generating a predicted fracture curve according to the predicted logging data;

[0007] Fracture prediction is performed based on the predicted seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment.

[0008] According to another aspect of the present invention, a device for predicting internal cracks in bedrock buried hills is provided, comprising:

[0009] An earthquake attribute extraction module, used for acquiring predicted earthquake data, and extracting a plurality of predicted earthquake data volumes according to the predicted earthquake data;

[0010] A fracture curve generating module, used for acquiring predicted well logging data and generating a predicted fracture curve according to the predicted well logging data;

[0011] The fracture prediction module is used to perform fracture prediction based on the predicted seismic data volume and the predicted fracture curve to determine the fracture development degree of the target layer segment.

[0012] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0013] at least one processor; and

[0014] a memory communicatively connected to the at least one processor; wherein,

[0015] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting internal fractures of bedrock buried hills described in any embodiment of the present invention.

[0016] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting internal fractures of bedrock buried hills described in any embodiment of the present invention when executed.

[0017] The technical solution of the embodiment of the present invention obtains predicted seismic data, extracts multiple predicted seismic data bodies according to the predicted seismic data, establishes corresponding seismic data bodies by obtaining seismic data corresponding to bedrock, and can effectively reflect seismic characteristics through actual seismic data, so that the prediction result is closer to reality; obtains predicted well logging data, generates predicted fracture curves according to the predicted well logging data, and the well logging curves generated by the well logging data can interpret the degree of fracture development and improve the accuracy of fracture prediction; performs fracture prediction according to the predicted seismic data body and the predicted fracture curve, determines the degree of fracture development in the target layer segment, and improves the accuracy of prediction of bedrock buried hill internal fractures through seismic data bodies and fracture curves, solves the technical problem of difficulty in predicting bedrock buried hill internal fractures in the prior art, improves the efficiency of predicting bedrock buried hill internal fractures, and through the participation of actual production materials, it is more in line with the actual situation of bedrock buried hill internal fractures, and improves the accuracy of predicting favorable zones and fracture development.

[0018] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0020] Figure 1 This is a flow chart of a method for predicting internal cracks in a bedrock buried hill provided in Example 1 of the present invention;

[0021] Figure 2 A plane display diagram of a PADD data volume provided by an embodiment of the present invention;

[0022] Figure 3 A plane display diagram of a coherent volume data volume provided by an embodiment of the present invention;

[0023] Figure 4 A plane display diagram of the extracted inclination data volume provided by an embodiment of the present invention;

[0024] Figure 5 A schematic diagram of a resistivity logging curve provided in this embodiment;

[0025] Figure 6 This is a flow chart of another method for predicting internal cracks in bedrock buried hills provided in the second embodiment of the present invention;

[0026] Figure 7 A schematic diagram of a plan view of crack density and orientation provided by an embodiment of the present invention;

[0027] Figure 8 This is a schematic diagram of the structure of a device for predicting internal cracks in a bedrock buried hill provided in Embodiment 3 of the present invention;

[0028] Fig. 9 It is a structural schematic diagram of an electronic device for implementing the method for predicting inner fractures of bedrock buried hills according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] Embodiment 1

[0031] Figure 1 The first embodiment of the present invention provides a flowchart of a method for predicting internal cracks in a bedrock buried hill. This embodiment is applicable to the case of predicting internal cracks in a bedrock buried hill through seismic data and well logging data. The method can be executed by a device for predicting internal cracks in a bedrock buried hill. The device for predicting internal cracks in a bedrock buried hill can be implemented in the form of hardware and / or software. The device for predicting internal cracks in a bedrock buried hill can be configured in an electronic device. Figure 1 As shown, the method includes:

[0032] S110, acquiring predicted seismic data, and extracting a plurality of predicted seismic data volumes according to the predicted seismic data.

[0033] Among them, the predicted seismic data can be a collection of multiple seismic signals recorded by seismic instruments. Exemplarily, the predicted seismic data can include seismic wave propagation data, earthquake times and focal depth. Optionally, the predicted seismic data can be post-stack pure wave fidelity seismic data. During seismic exploration, seismic data in the same area are covered and superimposed to obtain post-stack pure wave fidelity seismic data. Post-stack pure wave fidelity seismic data has higher resolution and more accurate lithology description. Through the analysis of post-stack pure wave fidelity seismic data, multiple key parameters of the reservoir can be better understood, thereby optimizing oil and gas exploration and development strategies and improving oil and gas production efficiency.

[0034] The predicted seismic data volume may be a data volume obtained by processing the predicted address data, and the predicted seismic data volume may be used to provide data support for oil and gas exploration.

[0035] Optionally, the predicted address data body may include a coherent data body, a PADD data body, and a dip angle data body. Figure 2 This is a planar display diagram of a PADD data volume provided by an embodiment of the present invention. Specifically, Figure 2 As shown in FIG. 1 , a PADD (Parallelland Distributed Databases) data body can be a data body acquired at a specific depth underground. A PADD data body usually includes seismic data of multiple survey lines. Through PADD data, detailed and precise information about underground rock physics can be obtained, such as Figure 2 High values ​​in indicate fracture development.

[0036] Optional, Figure 3 The plane display diagram of the coherent volume data provided by the embodiment of the present invention. Specifically, Figure 3 As shown, coherent data volume can refer to the data volume that processes and analyzes seismic data through coherent technology in seismic data processing to extract more accurate and valuable information; usually, the processing of coherent data volume is based on seismic trace similarity and matrix eigenvalues, which can effectively improve the signal-to-noise ratio and resolution of seismic data, so as to better understand the underground geological structure and oil and gas reservoir characteristics. Figure 3 The high black values ​​in the indicate fracture development.

[0037] Optional, Figure 4 This is a plane display diagram of the inclination data volume provided by the embodiment of the present invention. Figure 4As shown, the dip data volume can refer to the depth-related information contained in the seismic data, usually indicating the dip direction of the underground geological body. The dip data volume is usually used to describe the dip direction, density change, and oil-bearing geological characteristics of the underground rock formation. For example Figure 8 High values ​​in indicate fracture development.

[0038] Specifically, seismic data related to bedrock buried hills input by users are received, predicted address data of fractures predicted by the seismic data are obtained, and multiple predicted seismic data volumes are extracted for different types of seismic attributes in the predicted seismic data.

[0039] Optionally, in another optional embodiment of the present invention, extracting a plurality of predicted seismic data volumes according to the predicted seismic data includes:

[0040] Acquire the top and bottom information and seismic interpretation layer of the target layer segment; determine the seismic layer data of the target layer segment according to the predicted seismic data, the top and bottom information of the layer segment and the seismic interpretation layer; extract multiple predicted seismic data volumes of the target layer segment according to the seismic layer data.

[0041] The layer top and bottom information may be the bottom and top stratigraphic units of the target layer in the bedrock buried hill stratum; the target layer can be clearly divided in the stratum through the layer top and bottom information.

[0042] Among them, the seismic interpretation layer can be the wave position and amplitude characteristics emitted when the seismic wave passes through different lithological interfaces; optionally, the seismic interpretation layer can provide information about the underground geological body in the bedrock buried hill stratum. The seismic interpretation layer can be used to determine the stratum structure and further identify the target layer segment.

[0043] The seismic layer data may be the predicted seismic data corresponding to each seismic trace of the target layer segment.

[0044] Optionally, the top and bottom information of the layer segment and the seismic interpretation layer are obtained, and data inspection and interpolation smoothing are performed on the top information of the layer segment and the seismic interpretation layer. The target layer segment is determined through the top and bottom information of the layer segment and the seismic interpretation layer, and the predicted seismic data corresponding to each seismic trace in the target layer segment are determined, and the predicted seismic data corresponding to each seismic trace are determined as the seismic layer data of the target layer segment.

[0045] Optionally, after the predicted seismic data is acquired, the top and bottom information and seismic interpretation information of the target layer segment of the predicted seismic data are matched to ensure that corresponding seismic layer data exist on each seismic trace of the target layer segment.

[0046] Specifically, after obtaining the layer top and bottom information and seismic interpretation layer of the target layer segment, the predicted address data, the layer top and bottom information and the seismic interpretation layer are matched to determine the seismic layer data of the target layer segment, and multiple predicted seismic data bodies of the target layer segment are extracted from the seismic layer data.

[0047] S120, acquiring predicted logging data, and generating a predicted fracture curve according to the predicted logging data.

[0048] The predicted logging data may be data obtained by testing and analyzing the formation through a logging method. Exemplarily, the predicted logging data may include at least one of a resistivity curve, a neutron logging curve, an electrical logging curve, an imaging logging curve, a natural gamma logging curve, a neutron lifetime logging curve, a microelectrode logging curve, an acoustic wave curve, a density curve, an induced polarization logging curve, and an induction electrical logging curve.

[0049] The predicted fracture curve may be a fracture indication curve obtained by performing curve analysis on the bedrock buried hill formation through predictive logging data.

[0050] Specifically, the predicted logging data of the bedrock buried hill is obtained, and the logging curve is calculated according to the predicted logging data to obtain the predicted fracture curve.

[0051] Optionally, in another optional embodiment of the present invention, generating a predicted fracture curve according to the predicted logging data includes:

[0052] The predicted logging data is subjected to data denoising and normalization to determine the logging data to be calculated; and the predicted fracture curve is determined according to the logging data to be calculated.

[0053] Data denoising can be to remove the noise in the prediction logging data; data denoising can make the prediction logging data purer and improve the analysis and processing efficiency of the prediction logging data. Exemplarily, data denoising can include: at least one of differential filtering, wavelet transform and cyclic denoising.

[0054] Normalization may be a data processing method that maps data within a range from 0 to 1. Normalization may be used to improve data processing efficiency.

[0055] The well logging data to be calculated may be data after preprocessing of the predicted well logging data; the well logging data to be calculated is used to calculate and generate data for the predicted fracture curve.

[0056] Specifically, after obtaining the predicted logging data, in order to ensure the analysis efficiency of the logging data calculation, the predicted logging data is quality controlled and standardized, and the predicted logging data is denoised and normalized to achieve preprocessing of the predicted logging data and obtain the logging data to be calculated. The predicted fracture curve is determined based on the logging data to be calculated.

[0057] Optionally, in another optional embodiment of the present invention, determining the predicted fracture curve according to the well logging data to be calculated includes:

[0058] Generate an initial fracture curve based on the well logging data to be calculated; obtain an imaging well logging image curve and a curve interpretation result corresponding to the imaging well logging image curve; perform a curve interpretation on the initial fracture curve based on the imaging well logging image curve and the curve interpretation result; and determine the initial fracture curve as the predicted fracture curve when the initial fracture curve meets the curve quality monitoring requirement.

[0059] The initial fracture curve may be a deep induction resistivity logging curve directly calculated using the logging data to be calculated. Figure 5 A schematic diagram of a resistivity logging curve provided in this embodiment, such as Figure 5 As shown, in the deep induction resistivity logging curve, the low-angle fracture formation on the left side of the image shows obvious low resistivity against the background of high dual lateral resistivity. The stratification interfaces of deep, shallow and micro resistivity curves are consistent. The micro-spherical focusing resistivity changes drastically in a jagged manner, which is much lower than the deep and shallow lateral resistivity. The acoustic time difference value increases significantly, and the density curve value decreases significantly. The high-angle fracture formation on the right side of the image has higher dual lateral and micro-spherical focusing resistivity. The amplitudes of deep, shallow and micro resistivity curves are inconsistent. The greater the fracture inclination, the greater the difference in curve amplitude. The acoustic time difference, neutron and density curve features are not obvious. Figure 5 In the resistivity logging curve, the PEF in the first column is the photoelectric absorption cross-section index, SP is the natural potential curve, GR is the natural gamma curve, and CAL is the wellbore curve; the MSFL in the second column is the microspherical focusing resistivity curve, LLD is the deep lateral resistivity curve, and LLS is the shallow lateral resistivity curve; in the third column, ILD is the deep induction resistivity curve, ILM is the medium induction resistivity curve, and rmsl is the induction spherical focusing logging curve; the AC in the fourth column is the acoustic time difference curve, DEN is the density curve, and NPHI is the neutron porosity curve.

[0060] The imaging logging image curve may be a curve obtained by converting formation logging information into an image form, and the imaging logging image curve may be used to represent formation structure and characteristics.

[0061] The curve interpretation result may be an interpretation result used to interpret the fracture development degree of the imaging logging image curve.

[0062] The curve quality monitoring may be monitoring data pre-set to judge the curve quality.

[0063] Optionally, the initial fracture curve is compared with the imaging logging image curve and the curve interpretation result to interpret the fracture development degree corresponding to the initial fracture curve. For example, it can be defined that when the deep induction resistivity is greater than 0.2, it indicates a high fracture development degree, when the deep induction resistivity is less than or equal to 0.2 and greater than or equal to 0.1, it indicates a medium fracture development degree, and when the deep induction resistivity is less than 0.1, it indicates no fracture development degree.

[0064] Optionally, when performing curve quality monitoring, the formation depth corresponding to the initial fracture curve is determined. If the formation depth corresponding to the initial fracture curve can cover the target layer section, it is considered that the initial fracture curve meets the curve quality monitoring; if the formation depth corresponding to the initial fracture curve cannot guarantee coverage of the target layer section, it is considered that the initial fracture curve does not meet the curve quality monitoring.

[0065] Specifically, the initial fracture curve is generated by calculating the logging data to be calculated, the imaging logging image curve and the curve interpretation result corresponding to the imaging logging image curve are input, the initial fracture curve is interpreted by the imaging logging image curve and the curve interpretation result, and when the initial fracture curve meets the curve quality monitoring, the initial fracture curve is determined as the predicted fracture curve.

[0066] S130, performing fracture prediction according to the predicted seismic data volume and the predicted fracture curve, and determining the fracture development degree of the target layer segment.

[0067] The degree of fracture development may refer to the degree of fracture development and distribution patterns in the bedrock buried hill strata.

[0068] Specifically, the predicted seismic data volume and the predicted fracture curve are obtained, and the well-seismic correlation analysis is performed on the bedrock buried hill formation according to the predicted seismic data volume and the predicted fracture curve, and the fracture development degree of the target layer section in the bedrock buried hill formation is determined.

[0069] The technical solution of the embodiment of the present invention obtains predicted seismic data, extracts multiple predicted seismic data bodies according to the predicted seismic data, establishes corresponding seismic data bodies by obtaining seismic data corresponding to bedrock, and can effectively reflect seismic characteristics through actual seismic data, so that the prediction result is closer to reality; obtains predicted well logging data, generates predicted fracture curves according to the predicted well logging data, and the well logging curves generated by the well logging data can interpret the degree of fracture development and improve the accuracy of fracture prediction; performs fracture prediction according to the predicted seismic data body and the predicted fracture curve, determines the degree of fracture development in the target layer segment, and improves the accuracy of prediction of bedrock buried hill internal fractures through seismic data bodies and fracture curves, solves the technical problem of difficulty in predicting bedrock buried hill internal fractures in the prior art, improves the efficiency of predicting bedrock buried hill internal fractures, and through the participation of actual production materials, it is more in line with the actual situation of bedrock buried hill internal fractures, and improves the accuracy of predicting favorable zones and fracture development.

[0070] Embodiment 2

[0071] Figure 6 This is a flow chart of another method for predicting internal cracks in bedrock buried hills provided by the second embodiment of the present invention. The relationship between this embodiment and the above embodiment is a specific method for predicting the degree of crack development in the target layer. Figure 6 As shown in FIG. 1 , the prediction method of the internal fractures of the bedrock buried hill includes:

[0072] S610: Acquire predicted seismic data, and extract multiple predicted seismic data volumes according to the predicted seismic data.

[0073] S620: Acquire predicted logging data, and generate a predicted fracture curve according to the predicted logging data.

[0074] S630, performing correlation analysis on the multiple predicted seismic data volumes according to the predicted crack curve, determining a correlation sequence between the multiple predicted seismic data volumes and the predicted crack curve, and determining the predicted seismic data volume with the highest correlation with the predicted crack curve as a sensitive seismic data volume.

[0075] The correlation sequence may be a ranking of the correlation between a plurality of predicted address data and the predicted crack curve.

[0076] Specifically, the obtained multiple predicted seismic data bodies are sequentially subjected to correlation analysis with the predicted crack curve to determine the correlation between each predicted seismic data body and the predicted crack curve, and are sorted according to the correlation between the predicted address data body and the predicted crack curve and displayed in a correlation histogram. The predicted seismic data body with the highest correlation with the predicted crack curve is determined as the sensitive seismic data body.

[0077] S640: Perform fracture prediction based on the sensitive seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment.

[0078] The degree of fracture development can be a characteristic description of fractures in the target layer. For example, in bedrock, the degree of fracture development can be used to describe the length, density, shape and size of fractures in the bedrock.

[0079] Specifically, after the sensitive seismic data volume is determined, fracture prediction is performed on the target layer segment according to the sensitive seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment.

[0080] Optionally, in another optional embodiment of the present invention, the performing of fracture prediction according to the sensitive seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment includes:

[0081] The pre-trained fracture prediction model is used to perform model prediction according to the sensitive seismic data volume and the predicted fracture curve to determine the fracture data volume; and the fracture development degree of the target layer segment is determined according to the fracture data volume.

[0082] The fracture prediction model may be a neural network model pre-trained to predict the degree of fracture development in the target layer. Optionally, the fracture prediction model may be trained by a SVM (Support Vector Machine) algorithm.

[0083] The fracture data volume may be a data volume obtained by training a fracture prediction model from a sensitive seismic data volume. It should be noted that the fracture data volume is a curve whose correlation with the predicted fracture curve meets a preset threshold.

[0084] Optionally, the training process of the crack prediction model can be as follows: the sensitive seismic data volume is used as the training data set of the crack prediction model, the predicted crack curve is used as the target data set of the crack prediction model, the training data set and the target data set are respectively input into the SVM training model, the training data set is obtained through a preset algorithm function and output to the training curve, and it is determined whether the correlation between the training curve and the target data set meets the preset correlation threshold. If the correlation between the training curve and the target data set does not meet the preset correlation threshold, the training error is obtained according to the error correction function, and the training error is fed back to the SVM training model until a training curve whose correlation with the target data set meets the preset correlation threshold is obtained, and a successfully trained crack prediction model is obtained. Among them, the preset algorithm function can be shown in the following formula:

[0085]

[0086] Among them, xi is the sensitive seismic data volume; yi is the fracture data volume; ω is the high-dimensional space vector; T represents transposition; b is the offset; St represents the satisfied condition.

[0087] The error correction function can be expressed as follows:

[0088]

[0089] Specifically, after obtaining the sensitive seismic data volume, the sensitive seismic data volume and the predicted fracture curve are input into the fracture prediction model, the fracture prediction model outputs the fracture data volume, and the fracture development degree of the target layer section is determined according to the fracture data volume.

[0090] Optionally, in another optional embodiment of the present invention, determining the degree of fracture development of the target layer segment according to the fracture data volume includes:

[0091] Attributes are extracted from the fracture data volume to determine fracture attribute information of the target layer segment; wherein the fracture attribute information includes any one of plane attributes and profile features; and the fracture development degree of the target layer segment is predicted using the fracture attribute information.

[0092] The fracture attribute information may be attribute information used to describe the fracture curve; the fracture attribute information includes any one of plane attributes and cross-sectional features. The plane attributes of the fracture curve may include at least one of horizontal distribution features, width variation features, peak features, fracture density, fracture strike and inclination. The cross-sectional features of the fracture curve may include at least one of curvature features, strike features, inclination features, stretching features and bending features.

[0093] Optional, Figure 7 A schematic diagram of a plan view of crack density and orientation provided by an embodiment of the present invention. Figure 7 As shown, after the fracture attribute information of the target layer is obtained, the fracture density and direction are calculated by the fracture attribute information of the target layer. Further, after the fracture density and direction are calculated by the fracture attribute information of the target layer, the actual collected predicted fracture information can be compared with the predicted fracture density and direction to determine the prediction effect.

[0094] Specifically, according to the attribute extraction range corresponding to the target layer segment, the attributes of the fracture data volume are extracted to obtain the fracture attribute information of the target layer segment, and the fracture prediction is performed based on the fracture attribute information of the target layer segment to determine the fracture development degree of the target layer segment.

[0095] The technical solution of the embodiment of the present invention obtains predicted seismic data, extracts multiple predicted seismic data bodies according to the predicted seismic data, establishes corresponding seismic data bodies by obtaining seismic data corresponding to bedrock, and can effectively reflect seismic characteristics through actual seismic data, so that the predicted results are closer to reality; obtains predicted well logging data, generates predicted fracture curves according to the predicted well logging data, and the well logging curves generated by the well logging data can interpret the degree of fracture development and improve the accuracy of fracture prediction; performs correlation analysis on the multiple predicted seismic data bodies according to the predicted fracture curve, determines the correlation sequence of multiple predicted seismic data bodies with the predicted fracture curve, and determines the predicted seismic data body with the highest correlation with the predicted fracture curve as the sensitive seismic data body. By selecting the predicted address data body with the highest correlation with the predicted fracture curve, the amount of predicted data is further reduced, the efficiency of prediction is improved, and the prediction accuracy is improved through the sensitive seismic data body. Fracture prediction is performed based on the sensitive seismic data body and the predicted fracture curve to determine the degree of fracture development in the target layer segment. The accuracy of prediction of internal fractures in bedrock buried hills is improved through the sensitive seismic data body and the fracture curve, and the technical problem of difficulty in predicting internal fractures in bedrock buried hills in the prior art is solved, thereby improving the efficiency of predicting internal fractures in bedrock buried hills. Furthermore, through the participation of actual production materials, the method is more in line with the actual situation of internal fractures in bedrock buried hills, thereby improving the accuracy of predicting favorable zones and fracture development.

[0096] Embodiment 3

[0097] Figure 8 Schematic diagram of the structure of a device for predicting internal cracks in bedrock buried hills provided in the third embodiment of the present invention. Figure 8 As shown, the device includes: a seismic attribute extraction module 810, a crack curve generation module 820 and a crack prediction module 830, wherein:

[0098] The earthquake attribute extraction module 810 is used to obtain predicted earthquake data and extract multiple predicted earthquake data volumes according to the predicted earthquake data;

[0099] A fracture curve generating module 820 is used to obtain predicted well logging data and generate a predicted fracture curve according to the predicted well logging data;

[0100] The fracture prediction module 830 is used to perform fracture prediction based on the predicted seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment.

[0101] The technical solution of the embodiment of the present invention obtains predicted seismic data, extracts multiple predicted seismic data bodies according to the predicted seismic data, establishes corresponding seismic data bodies by obtaining seismic data corresponding to bedrock, and can effectively reflect seismic characteristics through actual seismic data, so that the prediction result is closer to reality; obtains predicted well logging data, generates predicted fracture curves according to the predicted well logging data, and the well logging curves generated by the well logging data can interpret the degree of fracture development and improve the accuracy of fracture prediction; performs fracture prediction according to the predicted seismic data body and the predicted fracture curve, determines the degree of fracture development in the target layer segment, and improves the accuracy of prediction of bedrock buried hill internal fractures through seismic data bodies and fracture curves, solves the technical problem of difficulty in predicting bedrock buried hill internal fractures in the prior art, improves the efficiency of predicting bedrock buried hill internal fractures, and through the participation of actual production materials, it is more in line with the actual situation of bedrock buried hill internal fractures, and improves the accuracy of predicting favorable zones and fracture development.

[0102] Optionally, the crack prediction module is specifically used to: perform correlation analysis on the multiple predicted seismic data volumes according to the predicted crack curve, determine a correlation sequence between the multiple predicted seismic data volumes and the predicted crack curve, and determine the predicted seismic data volume with the highest correlation with the predicted crack curve as the sensitive seismic data volume;

[0103] Fracture prediction is performed based on the sensitive seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment.

[0104] Optionally, the crack prediction module is further used for:

[0105] By using a pre-trained fracture prediction model, model prediction is performed according to the sensitive seismic data volume and the predicted fracture curve to determine the fracture data volume;

[0106] The fracture development degree of the target layer segment is determined according to the fracture data volume.

[0107] Optionally, the crack prediction module is further used for:

[0108] Extracting attributes from the fracture data volume to determine fracture attribute information of the target layer segment; wherein the fracture attribute information includes any one of plane attributes and cross-sectional features;

[0109] The degree of fracture development in the target layer segment is predicted using the fracture attribute information.

[0110] Optionally, the seismic attribute extraction module is specifically used for:

[0111] Performing data denoising and normalization on the predicted well logging data to determine the well logging data to be calculated;

[0112] The predicted fracture curve is determined according to the well logging data to be calculated.

[0113] Optionally, the seismic attribute extraction module is further used for:

[0114] Calculate and generate an initial fracture curve according to the well logging data to be calculated;

[0115] Obtaining an imaging logging image curve and a curve interpretation result corresponding to the imaging logging image curve;

[0116] Performing curve interpretation on the initial fracture curve according to the imaging logging image curve and the curve interpretation result;

[0117] In the case where the initial crack curve meets the curve quality monitoring, the initial crack curve is determined as the predicted crack curve.

[0118] Optionally, the crack curve generating module is specifically used for:

[0119] Acquiring the top and bottom information of the target layer and the seismic interpretation horizon;

[0120] Determine the seismic layer data of the target layer segment according to the predicted seismic data, the layer segment top and bottom information and the seismic interpretation layer segment;

[0121] A plurality of predicted seismic data volumes of the target layer segment are extracted according to the seismic layer data.

[0122] The device for predicting inner cracks of bedrock buried hills provided in the embodiment of the present invention can execute the method for predicting inner cracks of bedrock buried hills provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0123] Embodiment 4

[0124] Fig. 9 A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0125] like Fig. 9As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. An input / output (I / O) interface 15 is also connected to the bus 14.

[0126] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0127] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for predicting fractures inside a bedrock buried hill.

[0128] In some embodiments, the prediction method of the internal cracks of the bedrock buried hill can be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program can be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the prediction method of the internal cracks of the bedrock buried hill described above can be performed. Alternatively, in other embodiments, the processor 11 can be configured to execute the prediction method of the internal cracks of the bedrock buried hill in any other appropriate manner (for example, by means of firmware).

[0129] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), load programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0130] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0131] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0132] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0133] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0134] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0135] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0136] Embodiment 5

[0137] This embodiment provides a computer-readable storage medium having a computer program stored thereon. When the program is executed by a processor, the method steps for predicting internal fractures of bedrock buried hills provided in any embodiment of the present invention are implemented. The method comprises:

[0138] Acquiring predicted seismic data, and extracting a plurality of predicted seismic data volumes according to the predicted seismic data;

[0139] Acquiring predicted logging data, and generating a predicted fracture curve according to the predicted logging data;

[0140] Fracture prediction is performed based on the predicted seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment.

[0141] The computer storage medium of the embodiment of the present invention can adopt any combination of one or more computer-readable media. The computer-readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to: an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples (non-exhaustive list) of computer-readable storage media include: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this document, a computer-readable storage medium can be any tangible medium containing or storing a program, which can be used by an instruction execution system, device or device or used in combination with it.

[0142] Computer-readable signal media may include data signals propagated in baseband or as part of a carrier wave, which carry computer-readable program code. Such propagated data signals may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. Computer-readable signal media may also be any computer-readable medium other than a computer-readable storage medium, which may send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device.

[0143] The program code embodied on the computer readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.

[0144] Computer program code for performing the operations of the present invention may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0145] It should be understood by those skilled in the art that the modules or steps of the present invention described above can be implemented by a general-purpose computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, optionally, they can be implemented by a program code executable by a computer device, so that they can be stored in a storage device and executed by the computing device, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0146] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0147] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for predicting internal cracks in bedrock buried hills, characterized in that: include: Acquiring predicted seismic data, and extracting a plurality of predicted seismic data volumes according to the predicted seismic data; Acquiring predicted logging data, and generating a predicted fracture curve according to the predicted logging data; Fracture prediction is performed based on the predicted seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment.

2. The method according to claim 1, characterized in that The performing of fracture prediction according to the predicted seismic data volume and the predicted fracture curve to determine the fracture development degree of the target layer section includes: Performing correlation analysis on the multiple predicted seismic data volumes according to the predicted crack curve, determining a correlation sequence between the multiple predicted seismic data volumes and the predicted crack curve, and determining the predicted seismic data volume with the highest correlation with the predicted crack curve as a sensitive seismic data volume; Fracture prediction is performed based on the sensitive seismic data volume and the predicted fracture curve to determine the degree of fracture development in the target layer segment.

3. The method according to claim 2, characterized in that The method of performing fracture prediction according to the sensitive seismic data volume and the predicted fracture curve to determine the fracture development degree of the target layer section includes: By using a pre-trained fracture prediction model, model prediction is performed according to the sensitive seismic data volume and the predicted fracture curve to determine the fracture data volume; The fracture development degree of the target layer segment is determined according to the fracture data volume.

4. The method according to claim 3, characterized in that Determining the fracture development degree of the target layer segment according to the fracture data volume includes: Extracting attributes from the fracture data volume to determine fracture attribute information of the target layer segment; wherein the fracture attribute information includes any one of plane attributes and cross-sectional features; The degree of fracture development in the target layer segment is predicted using the fracture attribute information.

5. The method according to claim 1, characterized in that The generating a predicted fracture curve according to the predicted logging data comprises: Performing data denoising and normalization on the predicted well logging data to determine the well logging data to be calculated; The predicted fracture curve is determined according to the well logging data to be calculated.

6. The method according to claim 5, characterized in that The step of determining the predicted fracture curve according to the well logging data to be calculated comprises: Calculate and generate an initial fracture curve according to the well logging data to be calculated; Obtaining an imaging logging image curve and a curve interpretation result corresponding to the imaging logging image curve; Performing curve interpretation on the initial fracture curve according to the imaging logging image curve and the curve interpretation result; In the case where the initial crack curve meets the curve quality monitoring, the initial crack curve is determined as the predicted crack curve.

7. The method according to claim 1, characterized in that The step of extracting a plurality of predicted seismic data volumes according to the predicted seismic data comprises: Acquiring the top and bottom information of the target layer and the seismic interpretation horizon; Determine the seismic layer data of the target layer segment according to the predicted seismic data, the layer segment top and bottom information and the seismic interpretation layer segment; A plurality of predicted seismic data volumes of the target layer segment are extracted according to the seismic layer data.

8. A device for predicting internal cracks in bedrock buried hills, characterized in that: include: An earthquake attribute extraction module, used for acquiring predicted earthquake data, and extracting a plurality of predicted earthquake data volumes according to the predicted earthquake data; A fracture curve generating module, used for acquiring predicted well logging data and generating a predicted fracture curve according to the predicted well logging data; The fracture prediction module is used to perform fracture prediction based on the predicted seismic data volume and the predicted fracture curve to determine the fracture development degree of the target layer segment.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting internal fractures of bedrock buried hills as described in any one of claims 1-7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting internal fractures of bedrock buried hills as described in any one of claims 1 to 7 when executed.

Images