Volcanic rock reservoir ground stress prediction, fracturing region selection method and device

Abstract

This invention provides a method and apparatus for predicting in-situ stress and selecting fracturing zones in volcanic rock reservoirs, belonging to the fields of geological engineering and petroleum exploration technology. The method for predicting in-situ stress in volcanic rock reservoirs includes: acquiring pre-stack seismic data and well logging data of the area to be tested; performing joint inversion using the pre-stack seismic data and well logging data; obtaining a first rock mechanical parameter based on the inversion results; calculating a second rock mechanical parameter based on the first rock mechanical parameter; performing azimuth anisotropy inversion using the pre-stack seismic data to obtain a third rock mechanical parameter; and calculating the in-situ stress of the area to be tested based on the first, second, and third rock mechanical parameters. This invention improves the accuracy of in-situ stress prediction in volcanic rock reservoirs by comprehensively considering various formation characteristics, which helps optimize the selection of fracturing zones and improve the efficiency and economic benefits of petroleum exploration and development.

Description

Technical Field

[0001] This invention relates to the fields of geological engineering and petroleum exploration technology, specifically to a method and apparatus for predicting in-situ stress in volcanic rock reservoirs and selecting fracturing zones. Background Technology

[0002] In geological exploration and engineering practice, geostress prediction is a crucial technology, playing a significant role in assessing engineering safety, optimizing engineering design, and predicting geological hazards. Currently, various methods have been proposed for geostress prediction, each with its own advantages and limitations.

[0003] Among them, the measurement method, as a direct method for obtaining geostress data, provides accurate and reliable data. However, due to the limited number of measurement points, poor data continuity, and high cost, this method is difficult to widely apply in regional stress prediction.

[0004] Well logging calculation methods use measurement data at well points, combined with rock physical properties and geological models, to calculate the geostress value at the well point. Although this method generates a large amount of data and provides rich information, it can only reflect the stress state at the well point and cannot provide comprehensive information on the regional stress distribution.

[0005] Numerical simulation relies on accurate geological models and powerful computational analysis capabilities to predict geostress by simulating changes in the underground stress field. However, this method requires high precision in the geological model and high computational power, making it difficult to implement, and its effectiveness in predicting geostress under complex geological conditions still needs further validation.

[0006] In recent years, earthquake prediction methods have made significant progress in the field of geostress prediction. This method estimates and predicts geostress by analyzing the propagation characteristics of seismic waves in the subsurface medium and combining this with physical parameters of the crust and mantle. In particular, methods utilizing the azimuth response of multi-component seismic data, rock physics equivalent models, and curvature calculations can more accurately predict geostress distribution. However, in practical applications, earthquake prediction methods still face many challenges.

[0007] For example, in volcanic reservoirs, the strong lateral heterogeneity and significant variations in rock properties and thickness across different sections make conventional geostress prediction models difficult to accurately reflect the true stress state. Furthermore, the complex fracture and pore structures within volcanic reservoirs cause significant local variations in geostress, further increasing the difficulty of geostress prediction. Therefore, it is necessary to develop new geostress prediction methods and technologies tailored to the characteristics of volcanic reservoirs to improve the accuracy and reliability of predictions. Summary of the Invention

[0008] This invention provides a method for predicting in-situ stress in volcanic rock reservoirs, a method for selecting fracturing zones, and an apparatus. It aims to solve the problem of inaccurate in-situ stress prediction in volcanic rock reservoirs, especially considering the lateral heterogeneity and the complexity of fracture pore structure in volcanic rock reservoirs. It provides a more accurate and reliable in-situ stress prediction method, and optimizes the selection of fracturing zones based on the prediction results, thereby improving the efficiency and economic benefits of oil exploration and development.

[0009] To achieve the above objectives, a first aspect of the present invention provides a method for predicting in-situ stress in volcanic reservoirs. The method includes: acquiring pre-stack seismic data and well logging data of the area to be measured; performing joint inversion using the pre-stack seismic data and well logging data; obtaining a first rock mechanical parameter based on the inversion result, wherein the first rock mechanical parameter includes Young's modulus, Poisson's ratio, and density; calculating a second rock mechanical parameter based on the first rock mechanical parameter, wherein the second rock mechanical parameter includes vertical stress; performing azimuth anisotropy inversion using the pre-stack seismic data to obtain a third rock mechanical parameter, wherein the third rock mechanical parameter includes fracture density; and calculating the in-situ stress of the area to be measured based on the first rock mechanical parameter, the second rock mechanical parameter, and the third rock mechanical parameter.

[0010] In some embodiments, calculating a second rock mechanical parameter based on a first rock mechanical parameter includes: obtaining the depth of the area to be measured; and calculating the vertical stress based on the depth and the density in the first rock mechanical parameter using the following formula: Where, σ v Let H be the vertical stress, H represent the depth of the area to be measured, h be the depth variable of depth H, ρ(h) be the density at depth variable h, and g be the gravitational acceleration.

[0011] In some embodiments, azimuth anisotropy inversion is performed using pre-stack seismic data to obtain a third rock mechanical parameter, including: performing azimuth anisotropy analysis on the pre-stack seismic data to extract azimuth anisotropy parameters; performing azimuth anisotropy inversion based on the azimuth anisotropy parameters to obtain the azimuth Young's modulus; performing ellipse fitting based on the azimuth Young's modulus; and obtaining the fracture density based on the parameters of the fitted ellipse, wherein the ellipticity of the ellipse is used to indicate the fracture density.

[0012] In some embodiments, the azimuth anisotropy parameters include P-wave velocity and S-wave velocity. Azimuth anisotropy inversion based on the azimuth anisotropy parameters includes: performing azimuth anisotropy inversion using the functional relationship between the azimuth anisotropy parameters and the azimuth Young's modulus, where the functional relationship is: Where E is Young's modulus, v s v is the transverse wave velocity. p This represents the longitudinal wave velocity.

[0013] In some embodiments, the geostress in the area to be measured is calculated using the following geostress calculation formula: Where, σ h For the minimum horizontal stress, σ H For the maximum horizontal stress, ε h ε is the minimum horizontal strain of the formation. H The maximum horizontal strain of the formation is ε, and the minimum horizontal strain of the formation is ε. h and the maximum horizontal strain ε of the formation H The constants at the same fault line, where E is Young's modulus, v is Poisson's ratio, and σ is... v For vertical stress, P P α represents the formation pore pressure, and α is the Biot coefficient.

[0014] In some embodiments, the strata in the area to be tested satisfy the applicable conditions of linear slip theory and Hooke's law in terms of mechanical properties, so that the faults in the strata are equivalently treated as a medium background with anisotropic characteristics in mechanical analysis.

[0015] A second aspect of the present invention also provides a method for selecting fracturing zones in volcanic reservoirs. The method includes: predicting the geostress of a region to be tested using the volcanic reservoir geostress prediction method of the first aspect or any embodiment of the first aspect of the present invention; the geostress including minimum horizontal stress and maximum horizontal stress; calculating a horizontal stress difference coefficient based on the minimum and maximum horizontal stress; performing a correlation analysis between the horizontal stress difference coefficient and the gas production capacity of the region to be tested; and selecting a region where the horizontal stress difference coefficient is less than a first threshold as the fracturing zone of the region to be tested when the horizontal stress difference coefficient is negatively correlated with the gas production capacity.

[0016] In some embodiments, the horizontal stress difference coefficient is calculated using the following formula: Wherein, DHSR is the horizontal stress difference coefficient, σ h For the minimum horizontal stress, σ H This represents the maximum horizontal stress.

[0017] A third aspect of the present invention provides a device for selecting fracturing zones in volcanic reservoirs. The device includes: a prediction module for predicting the geostress of a region to be tested using the volcanic reservoir geostress prediction method provided in the first aspect or any embodiment of the present invention, wherein the geostress includes a minimum horizontal stress and a maximum horizontal stress; a calculation module for calculating a horizontal stress difference coefficient based on the minimum and maximum horizontal stresses; an analysis module for performing a correlation analysis between the horizontal stress difference coefficient and the gas production capacity of the region to be tested; and a selection module for selecting regions where the horizontal stress difference coefficient is less than a first threshold as fracturing zones in the region to be tested, provided that the horizontal stress difference coefficient and the gas production capacity are negatively correlated.

[0018] A fourth aspect of the present invention also provides a machine-readable storage medium storing instructions that cause a machine to execute the volcanic reservoir in-situ stress prediction method provided in the first aspect or any embodiment of the first aspect of the present invention, and to execute the volcanic reservoir fracturing area selection method provided in the second aspect or any embodiment of the second aspect of the present invention.

[0019] The beneficial effects of this invention are as follows: by comprehensively considering various characteristics of the formation to calculate the geostress, the accuracy of geostress prediction for volcanic rock reservoirs is improved, which helps to optimize the selection of fracturing areas and improve the efficiency and economic benefits of oil exploration and development.

[0020] Other features and advantages of the embodiments of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0021] The accompanying drawings are provided to further illustrate embodiments of the present invention and form part of the specification. They are used together with the following detailed description to explain the embodiments of the present invention, but do not constitute a limitation thereof. In the drawings:

[0022] Figure 1 This is a method for predicting geostress in volcanic rock reservoirs according to an embodiment.

[0023] Figure 2 This is a method for selecting fracturing zones in volcanic rock reservoirs, as illustrated in one embodiment.

[0024] Figure 3 This is a schematic diagram of the horizontal stress difference coefficient calculated using the volcanic rock reservoir fracturing zone selection method provided in the embodiments of the present invention.

[0025] Figure 4 This is a schematic diagram of the horizontal stress difference coefficient calculated using conventional methods.

[0026] Figure 5 This is a schematic diagram illustrating the correlation between the horizontal stress difference ratio calculated under HTI anisotropic medium conditions and the test gas production capacity.

[0027] Figure 6 This is a schematic diagram illustrating the correlation between the horizontal stress difference ratio calculated under conventional isotropic medium conditions and the test gas production capacity.

[0028] Figure 7 This is a fracturing zone selection device for volcanic rock reservoirs according to one embodiment. Detailed Implementation

[0029] The specific embodiments of the present invention will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of the present invention.

[0030] This invention provides a method for predicting geostress in volcanic rock reservoirs, which takes into account the characteristics of fracture development in volcanic rock reservoirs.

[0031] To more accurately describe the characteristics of volcanic reservoirs, this invention employs an HTI (Horizontal Transverse Isotropy) medium model. Compared to existing models based on the assumption of homogeneous isotropy of the subsurface medium, this equivalent model can better reflect the azimuthal anisotropy of seismic wave propagation in fractured reservoirs, providing a theoretical basis for accurate prediction of in-situ stress. In HTI media, the attenuation of seismic waves is affected by the fluid properties and fracture density within the fractures. The greater the fracture density, the greater the attenuation gradient of the seismic waves.

[0032] Furthermore, in some embodiments, based on the assumption that the strata in the area to be tested satisfy the applicability conditions of linear slip theory and Hooke's law in terms of mechanical properties, faults in the strata can be equivalently treated as an anisotropic medium background in mechanical analysis. This further simplifies the computational complexity of geostress prediction and improves the accuracy and efficiency of prediction.

[0033] Figure 1 This is a method for predicting in-situ stress in volcanic rock reservoirs, illustrated in one embodiment. For example... Figure 1 As shown, the method includes steps S101-S106, and the specific steps are as follows:

[0034] Step S101: Obtain pre-stack seismic data and well logging data for the area to be measured.

[0035] The area to be tested refers to a specific geographical area that needs to be explored or studied, especially areas containing oil and gas resources.

[0036] Prestack seismic data is obtained during seismic exploration by recording and processing the propagation characteristics of seismic waves through strata. This data provides information on subsurface stratigraphic structure, lithology, and faults, which is crucial for understanding subsurface geological structures and predicting in-situ stress distribution. Acquisition of prestack seismic data primarily relies on seismic exploration equipment, such as seismographs and geophones, to receive seismic wave signals on the surface or in wells, and then processes the data using computers to obtain the final data.

[0037] Well logging data is acquired in the well using inter-well logging equipment (such as logging tools). It provides detailed information about formation physical parameters such as lithology, resistivity, density, and sonic velocity, as well as information about fluid properties within the formation. This information is crucial for assessing the reservoir capacity and permeability of a formation and is indispensable data in oil and gas exploration and development. Well logging data acquisition methods include electrical logging and non-electrical logging, such as apparent resistivity logging, spontaneous potential logging, sonic logging, and natural gamma ray logging, each with its specific applications and advantages.

[0038] Step S102: Perform joint inversion using pre-stack seismic data and well logging data.

[0039] Joint inversion is an iterative optimization process that combines the advantages of seismic data and well logging data. It uses mathematical models and algorithms to simulate the propagation of seismic waves in the strata and compares and matches them with the actual observed seismic data.

[0040] Specifically, the joint inversion process can be divided into the following steps:

[0041] 1) Data preprocessing: Preprocessing of prestack seismic data and well logging data, including filtering, denoising, interpolation, etc., to improve the signal-to-noise ratio and resolution of the data.

[0042] 2) Initial Model Establishment: Based on well logging data or other prior knowledge, an initial geological model and physical property model are established. These models describe the structure, lithology, physical property parameters, etc. of the formation.

[0043] 3) Forward modeling: Using the initial model and seismic wave propagation theory, the propagation process of seismic waves in the strata is simulated, and the simulated seismic data is calculated.

[0044] 4) Comparison and matching: The simulated seismic data is compared and matched with the actual pre-stack seismic data. The accuracy of the model is evaluated by calculating the difference or error between the two.

[0045] 5) Iterative optimization: Based on the comparison and matching results, the initial model is iteratively optimized, and the parameters and structure of the model are adjusted to reduce the difference between simulated seismic data and actual seismic data.

[0046] 6) Convergence judgment: Repeat the iterative process from 3) to 5) above until the model error reaches the preset threshold or meets other convergence conditions.

[0047] By performing joint inversion, more accurate geological and physical property parameters, such as Young's modulus, Poisson's ratio, and density of the strata, can be obtained. In this embodiment of the invention, the results of the joint inversion will be used for subsequent in-situ stress prediction and fracture development analysis.

[0048] Step S103: Obtain the first rock mechanics parameters based on the inversion results.

[0049] The first rock mechanical parameter is a parameter of rock mechanical properties obtained directly from the inversion results and is directly related to the elastic properties of the rock. The first rock mechanical parameter may include Young's modulus, Poisson's ratio, and density.

[0050] Young's modulus is a physical quantity that describes the resistance of a solid material to deformation, characterizing the ratio of stress to strain in the elastic stage of the material. The Young's modulus of a formation can be calculated by analyzing the stress-strain relationship in the inversion results.

[0051] Poisson's ratio describes the ratio of transverse normal strain to axial normal strain in a material under uniaxial tension or compression, i.e., the transverse deformation coefficient. By analyzing the deformation data of the strata under different stress states in the inversion results, the ratio of transverse to axial strain of the strata under stress can be calculated, i.e., Poisson's ratio.

[0052] Density is a measure of mass within a specific volume. Well logging data typically contains formation density information, and formation density parameters can be obtained by directly reading the density values ​​from the well logging data.

[0053] Step S104: Calculate the second rock mechanics parameter based on the first rock mechanics parameter.

[0054] The second rock mechanics parameter is an indirect rock mechanics parameter calculated based on the first rock mechanics parameter, used to describe the mechanical behavior of the formation under specific stress conditions. The second rock mechanics parameter may include vertical stress. In oil and gas exploration and development, vertical stress reflects the stress on the formation in the vertical direction. The calculation of vertical stress is usually based on the Earth's gravity and the rock mechanics properties of the formation.

[0055] In some embodiments, calculating the second rock mechanical parameter based on the first rock mechanical parameter includes: obtaining the depth of the area to be measured; and calculating the vertical stress using Formula 1 based on the depth and the density in the first rock mechanical parameter.

[0056] Formula 1:

[0057] Where, σ v Let H be the vertical stress, H represent the depth of the area to be measured, h be the depth variable of depth H, ρ(h) be the density at depth variable h, and g be the gravitational acceleration.

[0058] Step S105: Perform azimuth anisotropy inversion using pre-stack seismic data to obtain the third rock mechanical parameters.

[0059] The third rock mechanics parameter is a parameter related to the mechanical properties of rocks, used to describe the complex physical properties of rocks, such as the development of fractures. The third rock mechanics parameter may include fracture density.

[0060] In some embodiments, the following specific steps are used to perform azimuth anisotropy inversion and calculate crack density:

[0061] 1) Azimuth anisotropy analysis: Azimuth anisotropy analysis is performed on pre-stack seismic data. By studying the changes in propagation velocity and amplitude of seismic waves in different directions, azimuth anisotropy parameters are extracted. These parameters mainly include the variation of P-wave velocity (P-wave velocity) and S-wave velocity (S-wave velocity) with azimuth angle.

[0062] 2) Azimuth Anisotropy Inversion: Based on the extracted azimuth anisotropy parameters, azimuth anisotropy inversion is performed. Specifically, the inversion can be performed using the functional relationship between the azimuth anisotropy parameters (such as P-wave velocity and S-wave velocity) and the azimuth Young's modulus. This functional relationship is shown in Equation 2:

[0063] Formula 2:

[0064] Where E is Young's modulus, v s v is the transverse wave velocity. p This represents the longitudinal wave velocity.

[0065] 3) Ellipse Fitting: After obtaining the azimuth Young's modulus, ellipse fitting is performed. It is understandable that the presence of cracks causes seismic waves to exhibit anisotropy during propagation, which manifests as an elliptical pattern in the spatial distribution of the Young's modulus. Therefore, by fitting this ellipse, the parameters of the ellipse, such as its major axis, minor axis, and ellipticity, can be obtained.

[0066] 4) Crack density calculation: Crack density is calculated based on the parameters of the fitted ellipse (especially the ellipticity). It should be noted that the ellipticity reflects the degree and direction of crack development and can be used to indicate crack density.

[0067] In this embodiment of the invention, the characteristics of volcanic reservoirs, especially the influence of fracture development on formation anisotropy, are considered. Volcanic reservoirs typically exhibit low porosity, strong heterogeneity, and widespread medium- and high-angle fractures. These fractures lead to azimuthal anisotropy in the rock, thereby affecting the propagation characteristics of seismic waves. Therefore, obtaining fracture density through azimuthal anisotropy inversion can more accurately predict the in-situ stress in volcanic reservoirs.

[0068] Furthermore, OVT (Offset Vector Tile) domain seismic data can be used to calculate volcanic rock fracture density parameters through specific seismic attribute analysis techniques. This invention does not impose specific limitations on this aspect.

[0069] Step S106: Calculate the geostress in the area to be measured based on the first rock mechanical parameters, the second rock mechanical parameters, and the third rock mechanical parameters.

[0070] Different rock mechanics parameters reflect different mechanical properties of rocks. By comprehensively considering these parameters, the geostress state of the area under test can be assessed more comprehensively and accurately. For example, the calculation of vertical stress requires density and depth data, while fracture density provides information about the internal structure of the rock. Combining this information can improve the accuracy of the calculation.

[0071] In some embodiments, considering the characteristics of volcanic rocks such as complex lithology, well-developed fractures, and rapid lateral changes in reservoirs, a geostress calculation formula based on the equivalent anisotropic medium background composed of linear slip theory and the combined spring model theory is designed to achieve accurate prediction of geostress in volcanic rocks.

[0072] Specifically, the formula for calculating the ground stress is shown in Formula 3:

[0073] Formula 3:

[0074] Where, σ h For the minimum horizontal stress, σ H For the maximum horizontal stress, ε h ε is the minimum horizontal strain of the formation. H The maximum horizontal strain of the formation is ε, and the minimum horizontal strain of the formation is ε. h and the maximum horizontal strain ε of the formation H The constants at the same fault line, where E is Young's modulus, v is Poisson's ratio, and σ is... v For vertical stress, P P α represents the formation pore pressure, and α is the Biot coefficient. The Biot coefficient for volcanic rock reservoirs can be chosen as 1.

[0075] This invention specifically introduces fracture density as a parameter when predicting in-situ stress in volcanic reservoirs. By combining seismic and well logging data, the fracture density of the volcanic rock can be calculated and used as an important input parameter in the in-situ stress prediction model. By substituting this parameter into the in-situ stress calculation formula, the in-situ stress in the area to be measured can be calculated. This method not only considers factors such as fluid pressure and porosity in the formation but also the elastic properties and fracture development of the formation, thereby improving the accuracy and reliability of in-situ stress prediction.

[0076] This invention also provides a method for selecting fracturing zones in volcanic rock reservoirs, such as... Figure 2 As shown, the method includes steps S201-S204, and the specific steps are as follows:

[0077] Step S201: Predict the geostress in the area to be measured, and obtain the minimum and maximum horizontal stresses.

[0078] Step S202: Calculate the horizontal stress difference coefficient based on the minimum and maximum horizontal stresses.

[0079] The horizontal stress difference coefficient (DHSR) is a parameter used to quantify the difference in horizontal stress, reflecting the relative magnitude between the minimum and maximum horizontal stress.

[0080] In some embodiments, the horizontal stress difference coefficient is calculated using the following formula 4:

[0081] Formula 4:

[0082] Wherein, DHSR is the horizontal stress difference coefficient, σ h For the minimum horizontal stress, σ H This represents the maximum horizontal stress.

[0083] Step S203: Perform a correlation analysis between the horizontal stress difference coefficient and the test gas production capacity of the area to be tested.

[0084] The horizontal stress difference coefficient (DHSR) calculated in step S202 is correlated with the gas production capacity of the test area. Statistical and comparative analysis is used to determine the correlation between the two. If the analysis shows a negative correlation between the horizontal stress difference coefficient and the gas production capacity (i.e., the greater the horizontal stress difference, the lower the gas production capacity), then proceed to step S204.

[0085] Step S204: When the horizontal stress difference coefficient is negatively correlated with the test gas production capacity of the test area, select the area where the horizontal stress difference coefficient is less than the first threshold as the fracturing area of ​​the test area.

[0086] The first threshold, determined based on historical data and expert experience, is used to screen areas with low stress differences and high potential for gas testing capacity. These areas are considered optimal candidate regions for fracturing operations, which can improve fracturing effectiveness and increase oil and gas production.

[0087] To facilitate understanding, the following examples illustrate this further with practical applications:

[0088] For example, earthquake-based geostress prediction was conducted on the Huoshiling Formation volcanic rocks in a certain area to guide horizontal well fracturing.

[0089] It should be noted that the Huoshiling Formation volcanic rocks have specific rock composition and characteristics, containing high porosity and permeability, which is conducive to oil and gas accumulation and migration, and easily forms reservoirs. This application selects the Huoshiling Formation volcanic rocks for in-situ stress prediction, which is representative to a certain extent. Besides this, this application can also be applied to other types of volcanic rocks, such as basalt formation volcanic rocks and andesite formation volcanic rocks, etc., and this disclosure does not specifically limit the application to these types. Secondly, in-situ stress is one of the key factors affecting the fracturing effect of horizontal wells. The magnitude and direction of in-situ stress directly affect the propagation and distribution of fractures. By predicting the in-situ stress of the Huoshiling Formation volcanic rocks, the stress field characteristics of the region can be understood more accurately, thereby optimizing the fracturing design scheme and improving the fracturing effect and oil and gas recovery rate.

[0090] The specific implementation steps are as follows: Perform pre-stack elastic parameter inversion, calculate the vertical stress based on the inversion results, and calculate the maximum and minimum horizontal stresses using Formula 3 in the volcanic reservoir in-situ stress prediction method provided by this invention. Apply Formula 4 in the volcanic reservoir fracturing zone selection method provided by this invention to calculate the horizontal stress difference coefficient (e.g., ...). Figure 3 (as shown) and the horizontal stress difference coefficient calculated by the conventional algorithm (e.g.) Figure 4 As shown), correlation analyses were performed between the horizontal stress difference coefficient and the corresponding test gas production capacity (test gas production capacity is an indicator for evaluating the production capacity of oil and gas wells, which can be expressed by unobstructed flow rate, i.e., the maximum flow rate that an oil and gas well can achieve under certain conditions). The horizontal stress difference coefficient has a negative correlation with production capacity; a smaller difference coefficient indicates a greater likelihood of network fractures, which is more conducive to fracturing and stimulation of horizontal wells. However, the correlation between the horizontal stress difference ratio calculated under the assumed HTI medium conditions of volcanic rock and test gas production capacity (e.g.) Figure 5 The correlation between the horizontal stress difference ratio calculated under conventional isotropic medium conditions and the test gas production capacity (as shown in the figure) and (e.g.) Figure 6 Compared to the above, it can be seen that the correlation is higher under HTI medium conditions. Here, the y-axis represents the horizontal stress difference coefficient, and the x-axis represents the unobstructed flow rate of the test gas production capacity (unit: 10). 4 m 3 ), R 2R represents the degree of fit to a linear relationship. 2 The value of R ranges from 0 to 1. 2 The closer a value is to 1, the better the model fit, meaning the stronger the explanatory power of the independent variable on the dependent variable, or the higher the correlation between the two. (Reference) Figure 5 Under the HTI medium conditions of this invention, y = -0.0017x + 0.1017, R is calculated. 2 =0.6629, while reference Figure 6 It can be seen that under the condition of a conventional isotropic medium, y = -0.00007x + 0.1085, and R is calculated. 2 =0.132 < 0.6629. Therefore, the volcanic reservoirs in this region have HTI medium characteristics, and the in-situ stress prediction method for volcanic reservoirs provided by this invention is more consistent with the in-situ stress prediction of volcanic rocks in this region.

[0091] The method for selecting fracturing zones in volcanic rock reservoirs provided by this invention can comprehensively consider the relationship between geostress and gas testing capacity, and scientifically and rationally select fracturing zones, thereby improving the efficiency of oil and gas exploration and development.

[0092] This invention also provides a device for selecting fracturing zones in volcanic rock reservoirs, such as... Figure 7 As shown, the device 100 includes a prediction module 110, a calculation module 120, an analysis module 130, and a selection module 140. The prediction module 110 uses the volcanic reservoir in-situ stress prediction method provided by this invention to predict the in-situ stress of the area to be tested, wherein the in-situ stress includes minimum horizontal stress and maximum horizontal stress. The calculation module 120 calculates the horizontal stress difference coefficient based on the minimum and maximum horizontal stresses. The analysis module 130 performs a correlation analysis between the horizontal stress difference coefficient and the gas testing capacity of the area to be tested. The selection module 140 selects areas where the horizontal stress difference coefficient is less than a first threshold as the fracturing area of ​​the area to be tested, when the horizontal stress difference coefficient and the gas testing capacity of the area to be tested show a negative correlation.

[0093] For details and benefits of the fracturing region selection device for volcanic rock reservoirs provided in the embodiments of the present invention, please refer to the above description of the fracturing region selection method for volcanic rock reservoirs, which will not be repeated here.

[0094] This invention also provides a machine-readable storage medium storing instructions that cause a machine to execute the volcanic reservoir in-situ stress prediction method or the volcanic reservoir fracturing zone selection method in any of the above embodiments.

[0095] The acquisition, transmission, storage, use, and processing of data in this application all comply with the relevant provisions of national laws and regulations.

[0096] It should be noted that in the embodiments of the present invention, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of the present invention. However, they do not mean that the applicant has used or necessarily used the solution.

[0097] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0098] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for predicting in-situ stress in volcanic rock reservoirs, characterized in that, The method includes: Acquire pre-stack seismic data and well logging data for the area to be measured; Joint inversion was performed using the pre-stack seismic data and the well logging data; The first rock mechanical parameters are obtained based on the inversion results. The first rock mechanical parameters include Young's modulus, Poisson's ratio, and density. The second rock mechanical parameter is calculated based on the first rock mechanical parameter, and the second rock mechanical parameter includes vertical stress; Using the pre-stack seismic data, azimuth anisotropy inversion is performed to obtain a third rock mechanical parameter, which includes fracture density. The geostress of the area to be measured is calculated based on the first rock mechanical parameters, the second rock mechanical parameters, and the third rock mechanical parameters.

2. The method according to claim 1, characterized in that, The calculation of the second rock mechanical parameters based on the first rock mechanical parameters includes: Obtain the depth of the region to be measured; Based on the depth and the density in the first rock mechanical parameter, the vertical stress is calculated using the following formula: Where, σ v Let H be the vertical stress, H represent the depth of the area to be measured, h be the depth variable of depth H, ρ(h) be the density at depth variable h, and g be the gravitational acceleration.

3. The method according to claim 1, characterized in that, The method of using the pre-stack seismic data to perform azimuth anisotropy inversion to obtain the third rock mechanical parameters includes: Azimuth anisotropy analysis was performed on the pre-stack seismic data to extract the azimuth anisotropy parameters. Based on the azimuth anisotropy parameters, azimuth anisotropy inversion is performed to obtain the azimuth Young's modulus. Ellipse fitting is performed based on the Young's modulus of the orientation; The crack density is obtained from the parameters of the fitted ellipse, wherein the ellipticity of the ellipse is used to indicate the crack density.

4. The method according to claim 3, characterized in that, The azimuth anisotropy parameters include P-wave velocity and S-wave velocity. Azimuth anisotropy inversion is performed based on these parameters, including: Azimuth anisotropy inversion is performed using the functional relationship between the azimuth anisotropy parameters and the azimuth Young's modulus. The functional relationship is as follows: Where E is Young's modulus, v s v is the transverse wave velocity. p This represents the longitudinal wave velocity.

5. The method according to any one of claims 1-4, characterized in that, The geostress in the area to be measured is calculated using the following geostress calculation formula: Where, σ h For the minimum horizontal stress, σ H For the maximum horizontal stress, ε h ε is the minimum horizontal strain of the formation. H The maximum horizontal strain of the formation is ε, and the minimum horizontal strain of the formation is ε. h and the maximum horizontal strain ε of the formation H The constants at the same fault line, where E is Young's modulus, v is Poisson's ratio, and σ is... v For vertical stress, P P α represents the formation pore pressure, and α is the Biot coefficient.

6. The method according to any one of claims 1-4, characterized in that, The strata in the area to be tested satisfy the applicable conditions of linear slip theory and Hooke's law in terms of mechanical properties, so that the faults in the strata are equivalently treated as a medium background with anisotropic characteristics in mechanical analysis.

7. A method for selecting fracturing zones in volcanic rock reservoirs, characterized in that, The method includes: Using the volcanic rock reservoir geostress prediction method according to any one of claims 1-6, the geostress of the area to be measured is predicted, wherein the geostress includes minimum horizontal stress and maximum horizontal stress; The horizontal stress difference coefficient is calculated based on the minimum and maximum horizontal stresses. Correlation analysis was performed between the horizontal stress difference coefficient and the test gas production capacity of the area to be tested; When the horizontal stress difference coefficient is negatively correlated with the test gas production capacity, the region where the horizontal stress difference coefficient is less than the first threshold is selected as the fracturing region of the region to be tested.

8. The method according to claim 7, characterized in that, The horizontal stress difference coefficient is calculated using the following formula: Wherein, DHSR is the horizontal stress difference coefficient, σ h For the minimum horizontal stress, σ H This represents the maximum horizontal stress.

9. A device for selecting fracturing zones in volcanic rock reservoirs, characterized in that, The device includes: The prediction module is used to predict the geostress of the area to be measured using the geostress prediction method for volcanic rock reservoirs according to any one of claims 1-6, wherein the geostress includes minimum horizontal stress and maximum horizontal stress. The calculation module is used to calculate the horizontal stress difference coefficient based on the minimum and maximum horizontal stresses. The analysis module is used to perform correlation analysis between the horizontal stress difference coefficient and the test gas production capacity of the area to be tested; The selection module is used to select areas where the horizontal stress difference coefficient is less than a first threshold as the fracturing area of ​​the test area when the horizontal stress difference coefficient is negatively correlated with the test gas production capacity.

10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform the volcanic reservoir in-situ stress prediction method according to any one of claims 1-6 or the volcanic reservoir fracturing zone selection method according to any one of claims 7-8.

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