Three-dimensional Stress Prediction Method and System for Shale Reservoirs Based on Seismic Data

Abstract

The present invention is applicable to the field of geological exploration, and provides a three-dimensional stress prediction method and system for shale reservoirs based on seismic data. The method includes the following steps: obtaining original seismic data and logging data; performing well-seismic calibration based on the original seismic data and logging data to obtain a time-depth conversion relationship; obtaining the fracture spatial distribution based on the original seismic data and the time-depth conversion relationship; obtaining a reservoir inversion data volume and further obtaining a reservoir sweet spot prediction result through an optimal reservoir inversion method selected based on the logging data, original seismic data, time-depth conversion relationship, and fracture spatial distribution; predicting the overburden pressure and pore pressure based on the logging data and the reservoir sweet spot prediction result; calculating the minimum horizontal principal stress and the maximum horizontal principal stress based on the overburden pressure and pore pressure. The present invention solves the problem of insufficient prediction accuracy in the prior art when dealing with shale reservoirs that have experienced multiple tectonic movements and have strong heterogeneity.

Description

Technical Field

[0001] The present invention belongs to the field of geological exploration, and particularly relates to a three-dimensional stress prediction method and system for shale reservoirs based on seismic data. Background Art

[0002] Natural fractures show two sides in shale gas development: on the one hand, they improve the reservoir seepage capacity and increase the initial production capacity of the shale gas reservoir, achieving a relatively high gas production rate; on the other hand, the development network of natural fractures in the reservoir and their interaction with hydraulic fracturing fractures are important factors affecting the reservoir reconstruction volume, and thus affect the production well productivity. Therefore, the existence of fractures has an important impact on the shale gas extraction effect and hydraulic fracturing propagation.

[0003] Existing research methods for in-situ stress include measurement method, logging calculation method, numerical simulation method, seismic prediction method and other methods. The measurement method and the logging calculation method are mainly based on single-well information, and the numerical simulation method and the seismic prediction method can obtain the spatial distribution of three-dimensional in-situ stress. To effectively carry out in-situ stress prediction using seismic technology, relevant institutions have carried out a lot of work and beneficial explorations. Chen Chao deduced and established the quantitative relationship between the in-situ stress difference coefficient and various elastic parameters and anisotropic parameters based on the basic theory of in-situ stress and the seismic rock physics theory of seismic anisotropic media, and carried out in-situ stress inversion using azimuthal seismic data. Liu Haojuan proposed to calculate the magnitude and direction of formation in-situ stress using pre-stack elastic parameters such as Young's modulus and Poisson's ratio, curvature attributes and formation pore pressure information. Qi Qing obtained the in-situ stress parameters of the formation plane using the structural information, velocity information and density information of the formation, and predicted the maximum horizontal principal stress direction, the minimum horizontal principal stress direction and the in-situ stress difference coefficient. Wang Xiuchao established the Suárez-Rivera model based on transversely isotropic media, carried out one-dimensional horizontal principal stress calculation, and extended the one-dimensional horizontal principal stress and vertical principal stress to three-dimensional space through well-seismic combination to carry out three-dimensional in-situ stress fine modeling.

[0004] However, the above-mentioned existing technologies cannot make full use of three-dimensional seismic information to improve the prediction accuracy of the spatial distribution of in-situ stress. The prediction accuracy is insufficient when dealing with shale reservoirs that have experienced multiple tectonic movements, have strong heterogeneity and are in a superpressure state. Summary of the Invention

[0005] In the first aspect of the embodiments of the present invention, there is provided a three-dimensional stress prediction method for shale reservoirs based on seismic data, aiming to solve the problem of insufficient prediction accuracy of the existing technology when dealing with shale reservoirs that have experienced multiple tectonic movements, have strong heterogeneity and are in a superpressure state.

[0006] The embodiments of the present invention are implemented as follows. A three-dimensional stress prediction method for shale reservoirs based on seismic data, the three-dimensional stress prediction method for shale reservoirs based on seismic data includes the following steps:

[0007] Obtain the original seismic data and logging data of the target area;

[0008] Based on the original seismic data and logging data, perform well-seismic calibration to obtain the time-depth conversion relationship;

[0009] Based on the original seismic data and the time-depth conversion relationship, obtain the fracture spatial distribution;

[0010] Based on the logging data, original seismic data, time-depth conversion relationship and fracture spatial distribution, through the selected optimal reservoir inversion method, obtain the reservoir inversion data volume and then obtain the reservoir sweet spot prediction result;

[0011] Based on the logging data and the reservoir sweet spot prediction result, predict the overlying formation pressure and pore pressure;

[0012] Based on the overlying formation pressure and pore pressure, calculate the minimum horizontal principal stress and the maximum horizontal principal stress;

[0013] Through the statistical analysis methods of borehole breakout, cross-dipole fast and slow acoustic waves and stress release fractures, obtain the directions of the minimum horizontal principal stress and the maximum horizontal principal stress;

[0014] Form a three-dimensional stress model with the overlying formation pressure, pore pressure, minimum horizontal principal stress, direction of the minimum horizontal principal stress, maximum horizontal principal stress and direction of the maximum horizontal principal stress.

[0015] Furthermore, the three-dimensional stress prediction method for shale reservoirs based on seismic data further includes:

[0016] After the step of obtaining the fracture spatial distribution based on the original seismic data and the time-depth conversion relationship,

[0017] Based on the logging data, original seismic data, time-depth conversion relationship and fracture spatial distribution, obtain the reservoir seismic response characteristics and reservoir logging response characteristics;

[0018] Select the optimal reservoir inversion method based on the reservoir seismic response characteristics and reservoir logging response characteristics.

[0019] Furthermore, in the step of obtaining the reservoir inversion data volume and then obtaining the reservoir sweet spot prediction result based on the logging data, original seismic data, time-depth conversion relationship and fracture spatial distribution through the selected optimal reservoir inversion method, obtaining the reservoir inversion data volume and then obtaining the reservoir sweet spot prediction result specifically includes:

[0020] Based on the regression relationship between reservoir porosity parameters and P-wave impedance, convert the pre-stack P-wave impedance volume in the reservoir inversion data volume into a porosity volume;

[0021] Based on the regression relationship between the gas content parameter of the reservoir and density, convert the pre-stack density volume in the reservoir inversion data volume into a gas content volume;

[0022] Based on the regression relationship between the total organic carbon content parameter of the reservoir and density, convert the pre-stack density volume in the reservoir inversion data volume into the total organic carbon content;

[0023] Calculate the rock brittleness index of the shale reservoir based on Young's modulus and Poisson's ratio;

[0024] Based on the porosity volume, gas content volume, total organic carbon content, and rock brittleness index, obtain the prediction result of reservoir sweet spots, and the prediction result of reservoir sweet spots includes the porosity, gas content, organic carbon content, and brittleness distribution characteristics of the reservoir.

[0025] Further, the step of predicting the overlying formation pressure and pore pressure based on the well logging data and the prediction result of reservoir sweet spots specifically includes:

[0026] Calculate the overlying formation pressure gradient based on the density data in the well logging data;

[0027] Predict the overlying formation pressure according to the overlying formation pressure gradient;

[0028] Obtain the surface elevation data based on the well logging data;

[0029] Calculate the overlying formation pressure data volume based on the depth-domain density volume and the surface elevation data;

[0030] Predict the pore pressure based on the depth-domain velocity volume and the overlying formation pressure data volume through the trained well point pressure prediction model.

[0031] Further, the calculation of the overlying formation pressure gradient satisfies:

[0032] ;

[0033] Wherein, represents the overlying pressure gradient at the depth of point i, represents the water density, represents the water depth, represents the average density of the upper non-density well logging data, represents the average length of the upper non-density well logging data section, represents the density data of point i in the well logging data, represents and corresponding well logging interval thickness.

[0034] Further, the steps of calculating the minimum horizontal principal stress and the maximum horizontal principal stress based on the overlying formation pressure and the pore pressure satisfy:

[0035] ;

[0036] ;

[0037] wherein, represents the minimum horizontal principal stress, represents the maximum horizontal principal stress, represents the overlying formation pressure, represents the pore pressure, represents the Biot coefficient, represents the Poisson's ratio, represents the regional stress coefficient in the direction of the minimum horizontal principal stress, represents the regional stress coefficient in the direction of the maximum horizontal principal stress, represents the contribution coefficient of the tectonic residual stress to the minimum horizontal principal stress, represents the contribution coefficient of the tectonic residual stress to the maximum horizontal principal stress, represents the Young's modulus of the rock, represents the formation depth, represents the minimum horizontal principal strain, represents the maximum horizontal principal strain.

[0038] Further, the three-dimensional stress prediction method for shale reservoirs based on seismic data further includes: obtaining the small-scale fracture spatial distribution of the shale reservoir, specifically including the following steps:

[0039] Obtain the prestack CMP gather data in the original seismic data, perform prestack azimuth processing and azimuth angle division on the CMP gather data to obtain multiple azimuth angle gathers;

[0040] Extract multiple azimuth amplitude attributes based on the multiple azimuth angle gathers;

[0041] Perform azimuth ellipse fitting on the multiple azimuth amplitude attributes to obtain the small-scale fracture spatial distribution of the shale reservoir.

[0042] In the second aspect of the embodiments of the present invention, there is provided a three-dimensional stress prediction system for shale reservoirs based on seismic data, and the three-dimensional stress prediction system for shale reservoirs based on seismic data includes:

[0043] A data acquisition module, configured to acquire the original seismic data and well logging data of the target area;

[0044] A time-depth conversion module, based on the original seismic data and well logging data, performs well-seismic calibration to obtain a time-depth conversion relationship;

[0045] The fracture spatial distribution calculation module obtains the fracture spatial distribution based on the original seismic data and the time-depth conversion relationship;

[0046] The reservoir sweet spot prediction module obtains the reservoir inversion data volume and then obtains the reservoir sweet spot prediction result based on the well logging data, the original seismic data, the time-depth conversion relationship, and the fracture spatial distribution through the selected optimal reservoir inversion method;

[0047] The overburden pressure and pore pressure prediction module predicts the overburden pressure and pore pressure based on the well logging data and the reservoir sweet spot prediction result;

[0048] The minimum and maximum horizontal principal stress calculation module calculates the minimum horizontal principal stress and the maximum horizontal principal stress based on the overburden pressure and the pore pressure;

[0049] The in-situ stress direction analysis module can obtain the direction of the minimum horizontal principal stress and the direction of the maximum horizontal principal stress through the statistical analysis methods of borehole breakage, cross-dipole fast and slow acoustic waves, and stress release fractures;

[0050] The three-dimensional stress prediction module can form a three-dimensional stress model by combining the overburden pressure, the pore pressure, the minimum horizontal principal stress, the direction of the minimum horizontal principal stress, the maximum horizontal principal stress, and the direction of the maximum horizontal principal stress.

[0051] Furthermore, the three-dimensional stress prediction system for shale reservoirs based on seismic data further includes: a reservoir inversion method optimization module;

[0052] The reservoir inversion method optimization module obtains the reservoir seismic response characteristics and the reservoir well logging response characteristics based on the well logging data, the original seismic data, the time-depth conversion relationship, and the fracture spatial distribution; selects the optimal reservoir inversion method based on the reservoir seismic response characteristics and the reservoir well logging response characteristics.

[0053] Furthermore, the overburden pressure and pore pressure prediction module includes: an overburden pressure prediction unit and a pore pressure prediction unit;

[0054] The overburden pressure prediction unit calculates the overburden pressure gradient based on the density data in the well logging data; predicts the overburden pressure according to the overburden pressure gradient;

[0055] The pore pressure prediction unit obtains the surface elevation data based on the well logging data; calculates the overburden pressure data volume based on the depth-domain density volume and the surface elevation data; predicts the pore pressure through the trained well point pressure prediction model based on the depth-domain velocity volume and the overburden pressure data volume.

[0056] In a third aspect of the embodiments of the present invention, an electronic device is provided, including:

[0057] at least one processor; and

[0058] a memory communicatively connected to the at least one processor; wherein,

[0059] the memory stores instructions or programs executable by the processor, and the instructions or programs are used to be executed by the processor to implement the above-mentioned three-dimensional stress prediction method for shale reservoirs based on seismic data.

[0060] In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions, and the computer instructions are used to be executed by the computer to implement the above-mentioned three-dimensional stress prediction method for shale reservoirs based on seismic data.

[0061] A three-dimensional stress prediction method for shale reservoirs based on seismic data provided by the embodiments of the present invention can make full use of the original seismic data of the target area and consider the factors affecting the magnitude of in-situ stress, such as tectonic strain, rock mechanical properties, formation pore pressure, formation anisotropy caused by fractures, etc. By combining the Poisson effect of vertical stress on horizontal stress, the regional stress field in the horizontal direction, and the local tectonic stress disturbance, the distributions of the maximum and minimum horizontal principal stresses are obtained, improving the accuracy and scope of application of shale reservoir in-situ stress prediction; it can solve the problem of insufficient prediction accuracy in the prior art when dealing with shale reservoirs that have experienced multiple tectonic movements, have strong heterogeneity, and are in a superpressure state. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] Figure 1 is a flowchart of a three-dimensional stress prediction method for shale reservoirs based on seismic data provided by the embodiments of the present invention;

[0063] Figure 2 is a schematic diagram of the effect of optimizing the original CMP (Common Middle Point) gather in the embodiments of the present invention;

[0064] Figure 3 is a schematic diagram of the process of reservoir sweet spot prediction in the embodiments of the present invention;

[0065] Figure 4 is a schematic diagram of the effect of the three-dimensional stress model in the embodiments of the present invention;

[0066] Figure 5 is a structural block diagram of a three-dimensional stress prediction system for shale reservoirs based on seismic data provided by the embodiments of the present invention;

[0067] Figure 6It is a structural block diagram of another three-dimensional stress prediction system for shale reservoirs based on seismic data provided by an embodiment of the present invention;

[0068] Figure 7 It is a structural block diagram of the overburden pressure and pore pressure prediction module in the embodiment of the present invention. Detailed implementation manners

[0069] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0070] In the practice of the embodiments of the present invention, it is found that unconventional shale reservoirs have the characteristics of low porosity and low permeability. During exploration and development, large-scale fracturing transformation must be carried out to increase the production of shale reservoirs and expand economic benefits. Among them, in-situ stress is the key to fracturing transformation. There are many influencing factors of in-situ stress and its distribution is complex. At present, the research on the prediction of in-situ stress distribution is weak, especially the comprehensive research with the help of seismic data is less; and the reservoir space of shale gas reservoirs is a typical multi-scale system, which consists of macroscopic and microscopic fracture systems, microscopic grain-level pores and nano-scale organic intra-grain pores. Natural fractures show two sides in shale gas development: on the one hand, they improve the reservoir seepage capacity and increase the initial production capacity of shale gas reservoirs, reaching a relatively high gas production rate; on the other hand, the development network of natural fractures in the reservoir and their interaction with hydraulic fracturing fractures are important factors affecting the reservoir transformation volume, and thus affect the production well productivity. Therefore, the existence of fractures has an important impact on the shale gas extraction effect and the propagation of hydraulic fracturing. Taking the Longmaxi Formation shale gas reservoir in the Luzhou area of the Sichuan Basin as an example, the following details this embodiment with a large number of single-well measured in-situ stress data as constraints, combined with mechanical parameter and formation pore pressure parameter information.

[0071] Figure 1 It is a flowchart of a three-dimensional stress prediction method for shale reservoirs based on seismic data provided by an embodiment of the present invention, which specifically may include steps S100 to S900 as Figure 1 shown:

[0072] S100. Obtain the original seismic data and logging data of the target area;

[0073] Among them, the target area is the Longmaxi Formation shale gas reservoir in the Luzhou area of the Sichuan Basin. The original seismic data includes original pre-stack gathers, post-stack seismic data, and seismic velocity-related data. The logging data includes drilled well logging, mud logging, and pressure test data, etc.; in order to improve the prediction accuracy, the drilling, logging, and mud logging information can be finely calibrated to establish the connection with seismic data such as seismic velocity and pre-stack gathers;

[0074] In addition, formation velocity and density information can be obtained based on acoustic wave curves and density curves. Parameters such as single-well Poisson's ratio, Lame coefficients, and mineral content can be calculated through P-wave and S-wave curves to obtain the key physical parameters of shale reservoirs.

[0075] S200. Perform well-seismic calibration on the basis of the original seismic data and logging data to obtain the time-depth conversion relationship;

[0076] S300. Based on the original seismic data and the time-depth conversion relationship, obtain the spatial distribution of faults;

[0077] In this step, the three-dimensional stress prediction method for shale reservoirs based on seismic data further includes:

[0078] After the step of obtaining the spatial distribution of faults based on the original seismic data and the time-depth conversion relationship, perform step S400:

[0079] S400. Based on the logging data, original seismic data, time-depth conversion relationship, and spatial distribution of faults, obtain the seismic response characteristics of the reservoir and the logging response characteristics of the reservoir;

[0080] Select the optimal reservoir inversion method based on the seismic response characteristics of the reservoir and the logging response characteristics of the reservoir.

[0081] When selecting the optimal reservoir inversion method, the optimal reservoir inversion method can be screened out through pre-stack and post-stack joint inversion and combined with actual drilling verification;

[0082] In an example, through well-seismic calibration, it can be obtained that the lithological interface of the Wufeng Formation is the interface between shale and limestone, corresponding to the strongest seismic wave peak amplitude response characteristics, and the shale reservoir is the weak wave trough reflection characteristic above the strong wave peak. The recursive inversion method, model-based inversion method, pre-stack elastic parameter inversion method, and pre-stack simultaneous inversion method can be compared to select the optimal reservoir inversion method.

[0083] S500. Based on the logging data, original seismic data, time-depth conversion relationship, and spatial distribution of faults, through the selected optimal reservoir inversion method, obtain the reservoir inversion data volume and then obtain the reservoir sweet spot prediction result; the reservoir sweet spot prediction result includes shale distribution characteristics such as organic carbon content, porosity, gas content, Poisson's ratio, Lame coefficients, and shale brittleness of the shale reservoir;

[0084] In an implementation scenario, the reservoir sweet spot prediction result is as Figure 2 shown, and may include the porosity, gas content, organic carbon content, and shale brittleness distribution characteristics of the shale reservoir.

[0085] Such as Figure 3As shown, in a scenario of this embodiment, in the step of obtaining a reservoir inversion data volume and then obtaining a reservoir sweet spot prediction result based on the logging data, original seismic data, time-depth conversion relationship, and fracture spatial distribution through the selected optimal reservoir inversion method, obtaining the reservoir inversion data volume and then obtaining the reservoir sweet spot prediction result specifically includes:

[0086] Based on the regression relationship between reservoir porosity parameters and P-wave impedance, convert the pre-stack P-wave impedance volume in the reservoir inversion data volume into a porosity volume;

[0087] Based on the regression relationship between reservoir gas content parameters and density, convert the pre-stack density volume in the reservoir inversion data volume into a gas content volume;

[0088] Based on the regression relationship between reservoir total organic carbon content parameters and density, convert the pre-stack density volume in the reservoir inversion data volume into total organic carbon content;

[0089] Calculate the rock brittleness index of the shale reservoir based on Young's modulus and Poisson's ratio;

[0090] The rock brittleness index is expressed as BI:

[0091] (1);

[0092] In formula (1) thereof, represents Young's modulus, represents the minimum Young's modulus, represents the maximum Young's modulus, represents Poisson's ratio, represents the minimum Poisson's ratio, represents the maximum Poisson's ratio;

[0093] Based on the porosity volume, gas content volume, total organic carbon content, and rock brittleness index, obtain the reservoir sweet spot prediction result, and the reservoir sweet spot prediction result includes reservoir porosity, gas content, organic carbon content, and brittleness distribution characteristics; see Figure 2 .

[0094] S600. Predict the overlying formation pressure and pore pressure based on the logging data and the reservoir sweet spot prediction result;

[0095] Generally, formation pressure can be divided into three types: original formation pressure, current formation pressure, and static pressure of the oil and gas layer; before the oilfield is put into development, the entire oil layer is in a state of balanced pressure and there is no flow; in the initial stage of oilfield development, after the first or the first batch of oil wells are completed and the blowout is released, the well is shut in for pressure measurement, and the pressure measured at this time is the original formation pressure. The pressure coefficient is the ratio of the water column height converted from the pressure at a certain point underground to the burial depth of this point, that is, the ratio of the measured formation pressure to the hydrostatic pressure at the same depth.

[0096] The step of predicting the overlying formation pressure and pore pressure based on the logging data and reservoir sweet spot prediction results in this step specifically includes: predicting the overlying formation pressure and predicting the pore pressure.

[0097] Predicting the overlying formation pressure:

[0098] Based on the density data in the logging data, calculate the overlying formation pressure gradient;

[0099] According to the overlying formation pressure gradient, predict the overlying formation pressure;

[0100] Predicting the pore pressure:

[0101] Based on the logging data, obtain the surface elevation data;

[0102] Based on the depth-domain density volume and the surface elevation data, calculate the overlying formation pressure data volume;

[0103] Based on the depth-domain velocity volume and the overlying formation pressure data volume, predict the pore pressure through the trained well point pressure prediction model.

[0104] In one embodiment, the calculation of the overlying formation pressure gradient satisfies:

[0105] (2);

[0106] Wherein, represents the overlying pressure gradient at the depth of point i, represents the water density, represents the water depth, represents the average density of the upper part without density logging data, represents the average length of the upper part without density logging data section, represents the density data of point i in the logging data, represents and corresponds to the logging interval thickness.

[0107] It should be noted that based on the density data in the logging data, calculate the overlying formation pressure gradient (i.e., ); Calculating the overburden pressure gradient requires an accurate density curve for the entire well section. In actual project research, the measured density curve generally cannot meet this requirement. In some well sections, such as the surface layer and deep seawater environment, no curves are measured. Therefore, it is necessary to calculate the density curve using a reasonable empirical formula in the well sections without density curves. The Amoco formula is used to estimate the density trend of the surface weathered layer and shallow rocks on land surface. In the deep part, the Gardner formula can be used to estimate the density from acoustic logging or the Amoco formula can be used. On this basis, the overburden pressure gradient curve is generated using the depth average value of the bulk density curve or other empirical formulas, and then the overburden pressure gradient is calculated.

[0108] S700. Calculate the minimum horizontal principal stress and the maximum horizontal principal stress based on the overburden pressure and the pore pressure described above;

[0109] The step of calculating the minimum horizontal principal stress and the maximum horizontal principal stress based on the overburden pressure and the pore pressure in this step satisfies:

[0110] (3);

[0111] (4);

[0112] Among them, represents the minimum horizontal principal stress, represents the maximum horizontal principal stress, represents the overburden pressure, represents the pore pressure, represents the Biot coefficient, represents the Poisson's ratio, represents the regional stress coefficient in the direction of the minimum horizontal principal stress, represents the regional stress coefficient in the direction of the maximum horizontal principal stress, represents the contribution coefficient of the tectonic residual stress to the minimum horizontal principal stress, represents the contribution coefficient of the tectonic residual stress to the maximum horizontal principal stress, represents the Young's modulus of the rock, represents the formation depth, represents the minimum horizontal principal strain, represents the maximum horizontal principal strain.

[0113] S800. Obtain the direction of the minimum horizontal principal stress and the direction of the maximum horizontal principal stress through the statistical analysis methods of borehole breakage, cross-dipole fast and slow acoustic waves, and stress relief fractures;

[0114] As described above, factors affecting the magnitude of in-situ stress are considered, such as tectonic strain, rock mechanical properties, formation pore pressure, formation anisotropy caused by fractures, etc. The combined horizontal in-situ stress is affected by three main aspects: the Poisson effect of the vertical stress, the regional stress field in the horizontal direction, and local tectonic stress disturbance, and the distributions of the maximum and minimum horizontal principal stresses are obtained.

[0115] S900. A three-dimensional stress model is formed by the overburden pressure, pore pressure, minimum horizontal principal stress, direction of the minimum horizontal principal stress, maximum horizontal principal stress, and direction of the maximum horizontal principal stress. In this way, based on the obtained three-dimensional stress model, the three-dimensional stress of the shale reservoir in the target area can be predicted, and technical support can be provided for activities such as shale gas exploitation and drilling according to the prediction results.

[0116] In this embodiment, the method can make full use of the original seismic data and consider factors affecting the magnitude of in-situ stress, such as tectonic strain, rock mechanical properties, formation pore pressure, formation anisotropy caused by fractures, etc. The combined horizontal in-situ stress is affected by three main aspects: the Poisson effect of the vertical stress, the regional stress field in the horizontal direction, and local tectonic stress disturbance, and the distributions of the maximum and minimum horizontal principal stresses are obtained, improving the accuracy and scope of application of in-situ stress prediction for shale reservoirs; it can solve the problem of insufficient prediction accuracy in the prior art when dealing with shale reservoirs that have experienced multiple tectonic movements, have strong heterogeneity, and are in an overpressure state.

[0117] In this embodiment, the finally obtained three-dimensional stress model is as Figure 4 shown. The illustrated results show that the three-dimensional distribution of in-situ stress is not only related to fracture development but also related to the stress enhancement caused by tectonic compression. The seismic prediction results are consistent with the distribution trend of in-situ stress measured by a single well. Through the method of this embodiment, more factors affecting in-situ stress can be taken into account, improving the accuracy of the prediction results and the scope of application of the prediction method.

[0118] In a scenario of this embodiment, during the drilling process, since there is a direct relationship between the caving direction of the rock around the wellbore and the in-situ stress, the direction of the principal stress can be judged by the caving direction of the wellbore rock. At the same time, by calculating the cross-dipole fast and slow acoustic waves obtained by logging, the directions of the minimum horizontal principal stress and the maximum horizontal principal stress can be obtained; a three-dimensional model is established for the overburden pressure, pore pressure, minimum horizontal principal stress, direction of the minimum horizontal principal stress, maximum horizontal principal stress, and direction of the maximum horizontal principal stress, and a three-dimensional stress model can be formed.

[0119] In one embodiment, in step S300, based on the original seismic data and the time-depth conversion relationship, the fracture spatial distribution is obtained; wherein, the fracture spatial distribution includes: large-medium scale fracture spatial distribution and small scale fracture spatial distribution. By identifying and comparing faults through symmetric attribute profiles and symmetric attribute planes, the large-medium scale fracture spatial distribution is obtained; based on the original seismic data and the time-depth conversion relationship, the small scale fracture spatial distribution is obtained through fault imaging enhancement technology.

[0120] In one embodiment, obtaining the small scale fracture spatial distribution of a shale reservoir specifically includes the following steps:

[0121] Obtain the pre-stack CMP gather data in the original seismic data, and perform pre-stack azimuth processing and azimuth angle division on the CMP gather data to obtain multiple azimuth angle gathers;

[0122] Extract multiple azimuth amplitude attributes based on the multiple azimuth angle gathers;

[0123] Perform azimuth ellipse fitting on the multiple azimuth amplitude attributes to obtain the small scale fracture spatial distribution of the shale reservoir.

[0124] In another embodiment, as Figure 5 shown, it is a structural block diagram of a three-dimensional stress prediction system for a shale reservoir based on seismic data, specifically including:

[0125] A data acquisition module 100, configured to acquire the original seismic data and logging data of a target area;

[0126] A time-depth conversion module 200, which performs well-seismic calibration based on the original seismic data and the logging data to obtain the time-depth conversion relationship;

[0127] A fracture spatial distribution calculation module 300, which obtains the fracture spatial distribution based on the original seismic data and the time-depth conversion relationship;

[0128] A reservoir sweet spot prediction module 400, which obtains reservoir inversion data volume and then obtains the reservoir sweet spot prediction result based on the logging data, the original seismic data, the time-depth conversion relationship and the fracture spatial distribution through the selected optimal reservoir inversion method;

[0129] An overlying formation pressure and pore pressure prediction module 500, which predicts the overlying formation pressure and pore pressure based on the logging data and the reservoir sweet spot prediction result;

[0130] A horizontal principal stress calculation module 600, which calculates the minimum horizontal principal stress and the maximum horizontal principal stress based on the overlying formation pressure and the pore pressure;

[0131] The in-situ stress direction analysis module 700 can obtain the directions of the minimum horizontal principal stress and the maximum horizontal principal stress through statistical analysis methods of borehole breakouts, cross-dipole fast and slow acoustic waves, and stress relief fractures;

[0132] The three-dimensional stress prediction module 800 can form a three-dimensional stress model by using the overburden pressure, pore pressure, minimum horizontal principal stress, direction of the minimum horizontal principal stress, maximum horizontal principal stress, and direction of the maximum horizontal principal stress.

[0133] In another preferred embodiment, as Figure 6 shown, the three-dimensional stress prediction system for shale reservoirs based on seismic data further includes: a reservoir inversion method optimization module 900;

[0134] The reservoir inversion method optimization module 900 obtains the reservoir seismic response characteristics and reservoir logging response characteristics based on the logging data, original seismic data, time-depth conversion relationship, and fracture spatial distribution; and selects the optimal reservoir inversion method based on the reservoir seismic response characteristics and reservoir logging response characteristics.

[0135] The selected optimal reservoir inversion method can be screened from the recursive inversion method, model-based inversion method, pre-stack elastic parameter inversion method, and pre-stack simultaneous inversion method. Of course, this embodiment is not limited thereto.

[0136] As Figure 7 shown, in a scenario of this embodiment, the overburden pressure and pore pressure prediction module 500 includes: an overburden pressure prediction unit 510 and a pore pressure prediction unit 520;

[0137] The overburden pressure prediction unit 510 calculates the overburden pressure gradient based on the density data in the logging data; and predicts the overburden pressure according to the overburden pressure gradient.

[0138] The pore pressure prediction unit 520 obtains the surface elevation data based on the logging data; calculates the overburden pressure data volume based on the depth-domain density volume and the surface elevation data; and predicts the pore pressure through a trained well point pressure prediction model based on the depth-domain velocity volume and the overburden pressure data volume.

[0139] In this embodiment, the overburden pressure prediction unit 510 can be built based on the formula (2);

[0140] The pore pressure prediction unit 520 can achieve prediction based on a trained well point pressure prediction model. The well point pressure prediction model belongs to the prior art and will not be elaborated herein.

[0141] It should be understood that for this embodiment, those skilled in the art can clearly understand that for the convenience and brevity of description, the specific working process and related descriptions of the above-described system can refer to the corresponding process in the foregoing method embodiment and will not be repeated here.

[0142] A three-dimensional stress prediction method for shale reservoirs based on seismic data provided by an embodiment of the present invention can make full use of original seismic data and consider factors affecting the magnitude of in-situ stress, such as tectonic strain, rock mechanical properties, formation pore pressure, formation anisotropy caused by fractures, etc. By combining the influence of the Poisson effect of vertical stress on horizontal stress, the regional stress field in the horizontal direction, and local tectonic stress disturbance, the distributions of the maximum and minimum horizontal principal stresses are obtained, improving the accuracy and scope of application of shale reservoir in-situ stress prediction; it can solve the problem of insufficient prediction accuracy of the prior art when dealing with shale reservoirs that have experienced multiple tectonic movements, have strong heterogeneity, and are in an overpressure state.

[0143] In another embodiment, a computer device is provided. The computer device stores a computer program, and when the computer program is executed by a processor, the processor is caused to execute steps S100 - S900 of the three-dimensional stress prediction method for shale reservoirs based on seismic data.

[0144] In another embodiment, a computer-readable storage medium is provided. The computer-readable storage medium stores computer instructions for being executed by the computer to implement steps S100 - S900 of the above three-dimensional stress prediction method for shale reservoirs based on seismic data.

[0145] The computer device includes a processor, a memory, a network interface, an input device, and a display screen connected through a system bus. Among them, the memory includes a non-volatile storage medium and an internal memory. The non-volatile storage medium of the computer device stores an operating system and can also store a computer program. When the computer program is executed by the processor, the processor can implement the three-dimensional stress prediction method for shale reservoirs based on seismic data. The internal memory can also store a computer program. When the computer program is executed by the processor, the processor can execute the three-dimensional stress prediction method for shale reservoirs based on seismic data. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball, or touchpad provided on the outer shell of the computer device, or an external keyboard, touchpad, or mouse, etc.

[0146] It should be understood that although the steps in the flowcharts of the embodiments of the present invention are shown in sequence according to the indications of the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless there is a clear indication in this article, there is no strict order restriction for the execution of these steps, and these steps can be executed in other orders. Moreover, at least a part of the steps in each embodiment may include multiple sub-steps or multiple stages. These sub-steps or stages are not necessarily executed at the same time, but can be executed at different times. The execution order of these sub-steps or stages is not necessarily sequential, but can be executed alternately or in turn with at least a part of other steps or sub-steps or stages of other steps.

[0147] The technical features of the above-described embodiments can be combined arbitrarily. For the sake of concise description, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0148] The above-described embodiments only represent several implementation manners of the present invention. The description is relatively specific and detailed, but it should not be construed as a limitation on the scope of the invention patent of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present invention, several modifications and improvements can still be made, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the invention patent of the present invention shall be subject to the appended claims.

[0149] The above is only the preferred embodiment of the present invention and is not used to limit the present invention. Any modifications, equivalent replacements, and improvements made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A three-dimensional stress prediction method for shale reservoirs based on seismic data, characterized in that, the three-dimensional stress prediction method for shale reservoirs based on seismic data comprises the following steps: Obtain the original seismic data and logging data of the target area; Based on the original seismic data and logging data, perform well-seismic calibration to obtain the time-depth conversion relationship; Based on the original seismic data and the time-depth conversion relationship, obtain the fracture spatial distribution; Based on the logging data, original seismic data, time-depth conversion relationship and fracture spatial distribution, through the selected optimal reservoir inversion method, obtain the reservoir inversion data volume and then obtain the reservoir sweet spot prediction result; Based on the logging data and the reservoir sweet spot prediction result, predict the overburden pressure and pore pressure; Based on the overburden pressure and pore pressure, calculate the minimum horizontal principal stress and the maximum horizontal principal stress; Obtain the direction of the minimum horizontal principal stress and the direction of the maximum horizontal principal stress through the statistical analysis methods of borehole breakage, cross-dipole fast and slow acoustic waves, and stress release fractures; Form a three-dimensional stress model with the overburden pressure, pore pressure, minimum horizontal principal stress, direction of the minimum horizontal principal stress, maximum horizontal principal stress and direction of the maximum horizontal principal stress.

2. The three-dimensional stress prediction method for shale reservoirs based on seismic data according to claim 1, characterized in that, the three-dimensional stress prediction method for shale reservoirs based on seismic data further comprises: After the step of obtaining the fracture spatial distribution based on the original seismic data and the time-depth conversion relationship, Based on the logging data, original seismic data, time-depth conversion relationship and fracture spatial distribution, obtain the reservoir seismic response characteristics and reservoir logging response characteristics; Select the optimal reservoir inversion method based on the reservoir seismic response characteristics and reservoir logging response characteristics.

3. The three-dimensional stress prediction method for shale reservoirs based on seismic data according to claim 1, characterized in that, In the step of obtaining the reservoir inversion data volume and then obtaining the reservoir sweet spot prediction result through the selected optimal reservoir inversion method based on the logging data, original seismic data, time-depth conversion relationship and fracture spatial distribution, obtaining the reservoir inversion data volume and then obtaining the reservoir sweet spot prediction result specifically comprises: Based on the regression relationship between the reservoir porosity parameter and the P-wave impedance, convert the pre-stack P-wave impedance volume in the reservoir inversion data volume into a porosity volume; Based on the regression relationship between the reservoir gas content parameter and the density, convert the pre-stack density volume in the reservoir inversion data volume into a gas content volume; Based on the regression relationship between the reservoir total organic carbon content parameter and the density, convert the pre-stack density volume in the reservoir inversion data volume into the total organic carbon content; Calculate the rock brittleness index of the shale reservoir based on the Young's modulus and Poisson's ratio; Based on the porosity volume, gas content volume, total organic carbon content, and rock brittleness index, obtain the reservoir sweet spot prediction result, and the reservoir sweet spot prediction result includes reservoir porosity, gas content, organic carbon content, and brittleness distribution characteristics.

4. The three-dimensional stress prediction method for shale reservoirs based on seismic data according to claim 1, characterized in that, The steps of predicting the overlying formation pressure and pore pressure based on the well logging data and reservoir sweet spot prediction results specifically include: Calculate the overlying formation pressure gradient based on the density data in the well logging data; Predict the overlying formation pressure according to the overlying formation pressure gradient; Obtain the surface elevation data based on the well logging data; Calculate the overlying formation pressure data volume based on the depth domain density volume and the surface elevation data; Predict the pore pressure based on the depth domain velocity volume and the overlying formation pressure data volume through the trained well point pressure prediction model.

5. The three-dimensional stress prediction method for shale reservoirs based on seismic data according to claim 4, characterized in that, The calculation of the overlying formation pressure gradient satisfies: ； Among them, represents the overburden pressure gradient at the depth of point i, represents the water density, represents the water depth, represents the average density of the upper part without density logging data, represents the average length of the upper part without density logging data section, represents the density data of point i in the logging data, represents and corresponding logging interval thickness.

6. The three-dimensional stress prediction method for shale reservoirs based on seismic data according to claim 1, characterized in that, The steps of calculating the minimum horizontal principal stress and the maximum horizontal principal stress based on the overlying formation pressure and pore pressure satisfy: ； ； Among them, represents the minimum horizontal principal stress, represents the maximum horizontal principal stress, represents the overburden pressure, represents the pore pressure, represents the Biot coefficient, represents the Poisson's ratio, represents the regional stress coefficient in the direction of the minimum horizontal principal stress, represents the regional stress coefficient in the direction of the maximum horizontal principal stress, represents the contribution coefficient of the tectonic residual stress to the minimum horizontal principal stress, represents the contribution coefficient of the tectonic residual stress to the maximum horizontal principal stress, represents the Young's modulus of the rock, represents the formation depth, represents the minimum horizontal principal strain, represents the maximum horizontal principal strain.

7. The three-dimensional stress prediction method for shale reservoirs based on seismic data according to claim 1, characterized in that, The three-dimensional stress prediction method for shale reservoirs based on seismic data further includes: obtaining the small-scale fracture spatial distribution of the shale reservoir, specifically including the following steps: Obtain the prestack CMP gather data in the original seismic data, and perform prestack azimuth processing and azimuth angle division on the CMP gather data to obtain multiple azimuth angle gathers; Extract multiple azimuth amplitude attributes based on the multiple azimuth angle gathers; Perform azimuth ellipse fitting on the multiple azimuth amplitude attributes to obtain the small-scale fracture spatial distribution of the shale reservoir.

8. A three-dimensional stress prediction system for shale reservoirs based on seismic data, characterized in that, The three-dimensional stress prediction system for shale reservoirs based on seismic data includes: A data acquisition module for acquiring the original seismic data and well logging data of the target area; A time-depth conversion module for obtaining the time-depth conversion relationship through well-seismic calibration based on the original seismic data and well logging data; A fracture spatial distribution calculation module for obtaining the fracture spatial distribution based on the original seismic data and the time-depth conversion relationship; A reservoir sweet spot prediction module for obtaining the reservoir inversion data volume and then obtaining the reservoir sweet spot prediction result through the selected optimal reservoir inversion method based on the well logging data, the original seismic data, the time-depth conversion relationship and the fracture spatial distribution; An overlying formation pressure and pore pressure prediction module for predicting the overlying formation pressure and pore pressure based on the well logging data and the reservoir sweet spot prediction result; A horizontal principal stress calculation module for calculating the minimum horizontal principal stress and the maximum horizontal principal stress based on the overlying formation pressure and pore pressure; A in-situ stress direction analysis module capable of obtaining the direction of the minimum horizontal principal stress and the direction of the maximum horizontal principal stress through the statistical analysis methods of borehole breakage, cross-dipole fast and slow acoustic waves, and stress release fractures; A three-dimensional stress prediction module capable of forming a three-dimensional stress model with the overlying formation pressure, pore pressure, minimum horizontal principal stress, direction of the minimum horizontal principal stress, maximum horizontal principal stress and direction of the maximum horizontal principal stress.

9. The three-dimensional stress prediction system for shale reservoirs based on seismic data according to claim 8, characterized in that, the three-dimensional stress prediction system for shale reservoirs based on seismic data further comprises: a reservoir inversion method optimization module; the reservoir inversion method optimization module, based on the well logging data, original seismic data, time-depth conversion relationship and fracture spatial distribution, obtains the reservoir seismic response characteristics and reservoir well logging response characteristics; and selects the optimal reservoir inversion method based on the reservoir seismic response characteristics and reservoir well logging response characteristics.

10. The three-dimensional stress prediction system for shale reservoirs based on seismic data according to claim 8, the overburden pressure and pore pressure prediction module comprises: an overburden pressure prediction unit and a pore pressure prediction unit; the overburden pressure prediction unit calculates the overburden pressure gradient based on the density data in the well logging data; and predicts the overburden pressure according to the overburden pressure gradient; the pore pressure prediction unit obtains the surface elevation data based on the well logging data; calculates the overburden pressure data volume based on the depth domain density volume and the surface elevation data; and predicts the pore pressure through the trained well point pressure prediction model based on the depth domain velocity volume and the overburden pressure data volume.

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