Three-dimensional compressible distribution method and device for glutenite exploration area

Abstract

The invention discloses a compressibility three-dimensional distribution method and device for a glutenite exploration area, and the method comprises the following steps: obtaining the experimental data, logging curve, logging data, test data and three-dimensional seismic data volume of an observation area; determining the oil-bearing compressibility and engineering compressibility influence parameters of the glutenite; constructing an exploration area glutenite rock physical forward model, and complementing a missing well elastic curve; constructing an oil-containing compressibility and engineering compressibility parameter calculation model under the logging scale; constructing a three-dimensional oil-containing compressibility and engineering compressibility parameter body; calculating the weight of each parameter, and constructing a comprehensive compressibility index model; and according to the comprehensive compressibility index model, calculating to obtain a multi-parameter fused exploration area glutenite compressibility comprehensive evaluation three-dimensional body. The glutenite compressibility evaluation complex is established by integrating oil-containing compressibility and engineering compressibility factors, and the compressibility of glutenite can be accurately represented. And an objective basis is provided for realizing compressibility evaluation of glutenite at different dimensions and different positions in a target exploration area.

Description

Technical Field

[0001] This invention belongs to the field of geological exploration and development technology, and in particular relates to a method and apparatus for compressibility three-dimensional distribution in sandstone and conglomerate exploration areas. Background Technology

[0002] Conglomerate reservoirs possess abundant resources, but their poor physical properties lead to unsatisfactory development and utilization, necessitating hydraulic fracturing. Compressibility is used to quantitatively evaluate the operational difficulty, complexity, and scale of artificial fractures during hydraulic fracturing. Based on experience in US shale oil and gas development, over 20 methods for evaluating reservoir compressibility and brittleness have been developed. These methods are applicable to fine-grained homogeneous rocks but not to conglomerate reservoirs with a granular composite structure composed of gravel and matrix cementation.

[0003] The lack of well logging data in the exploration area limits the accuracy and systematic nature of the compressibility assessment of sandstone and conglomerate. Furthermore, current compressibility assessments primarily rely on experimental and well logging scales, offering little reference value for subsequent development of the exploration area.

[0004] Therefore, there is an urgent need for a method to achieve efficient and accurate assessment of sandstone and conglomerate reservoirs in exploration areas. Summary of the Invention

[0005] The problem to be solved by the present invention is to provide a method and apparatus for three-dimensional distribution of compressibility in sandstone and conglomerate exploration areas. This method can efficiently and accurately evaluate sandstone and conglomerate reservoirs in exploration areas.

[0006] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is: a method for three-dimensional distribution of compressibility in sandstone and conglomerate exploration areas, comprising the following steps:

[0007] S1: Acquire experimental data, well logging curves, well logging data, test data, and 3D seismic data volumes for the observation area;

[0008] S2: Determine the parameters affecting the oil compressibility and engineering compressibility of sandstone and conglomerate;

[0009] S3: Based on the experimental data and some well elastic logging curves, construct a forward model of the petrology of the sandstone and conglomerate area in the exploration area, and complete the missing well elastic curves;

[0010] S4: Based on well logging data and experimental data, construct a calculation model for oil-bearing compressibility and engineering compressibility parameters at the well logging scale;

[0011] S5: Using geostatistical methods, a three-dimensional parameter body for oil-bearing compressibility and engineering compressibility was constructed;

[0012] S6: Based on the well test flow rate, calculate the weights of each parameter and construct a comprehensive compressibility index model; based on the comprehensive compressibility index model, calculate the three-dimensional comprehensive evaluation of the compressibility of sandstone and conglomerate in the exploration area using multi-parameter fusion.

[0013] Furthermore, in S1, the experimental data includes formation temperature and pressure, fluid parameters, mineral composition, mechanical parameters, stress and strain, tensile strength, compressive strength, fracture toughness, geostress, and gravel composition; the logging curves include P-wave velocity, S-wave velocity, and density curves; the logging data includes the whole-well mineral content; the test data is pressure and flow rate data; and the three-dimensional seismic data volume includes root mean square amplitude attribute volume and instantaneous frequency volume.

[0014] Furthermore, in S2, the parameters affecting the compressibility of the conglomerate oil-bearing rock include porosity and saturation; the parameters affecting the engineering compressibility of the conglomerate include stress difference coefficient, brittleness index, fracture toughness, and gravel heterogeneity index.

[0015] Furthermore, the formula for calculating the gravel heterogeneity index is as follows:

[0016] I s =I gc I H

[0017] Among them, I H I is the material heterogeneity index of conglomerate. gc It is the structural heterogeneity index;

[0018]

[0019] Among them, C g For gravel content, when I gc When >1, I gc =1;

[0020]

[0021] Among them, I H H is the material heterogeneity index of conglomerate. g For gravel hardness, H m The hardness is determined by the heterostructure.

[0022] Furthermore, in S3, using the rock physics forward modeling method, Powerlog software, and the XU-WHITE model, elastic curves of all wells in the observation area are constructed.

[0023] Furthermore, in S4, the gravel heterogeneity index at the logging scale is calculated using the following formula:

[0024]

[0025] Where Den is the density curve, and h i The hardness of the i-th mineral; c i The content of the i-th mineral obtained from the logging data.

[0026] Furthermore, in S4, the parameters are normalized. The brittleness index and porosity are positively correlated parameters, while the water saturation, gravel heterogeneity index, fracture toughness, and stress difference coefficient are negatively correlated parameters. The normalization formulas for the positive and negatively correlated parameters are used respectively.

[0027] Furthermore, in S5, geostatistical methods are used to establish a three-dimensional oil-bearing compressibility volume and an engineering compressibility parameter volume, constrained by a three-dimensional seismic data volume.

[0028] Furthermore, in S6, the weights of each parameter are calculated using the analytic hierarchy process based on grey relational analysis, the correlation degree of each parameter is calculated and sorted, and a judgment matrix of the analytic hierarchy process is established based on the correlation degree to obtain the weight values ​​of each parameter and construct a comprehensive compressibility index model.

[0029] Furthermore, the present invention also provides a compressibility three-dimensional distribution device for sandstone and conglomerate exploration areas, utilizing the aforementioned compressibility three-dimensional distribution method for sandstone and conglomerate exploration areas, including,

[0030] The data acquisition module is used to acquire experimental data, well logging curves, well logging data, test data, and 3D seismic data volumes in the observation area.

[0031] The elastic curve calculation module is used to construct a forward model of the petrology of sandstone and conglomerate in the exploration area based on experimental data and elastic logging curves of some wells, and to complete the elastic curves of missing wells.

[0032] The module for calculating the compressibility parameters of sandstone and conglomerate at the logging scale is used to construct calculation models for oil-bearing compressibility and engineering compressibility parameters at the logging scale based on logging data and experimental data.

[0033] The 3D sandstone and conglomerate compressibility parameter calculation module is used to construct a 3D oil-bearing compressibility and engineering compressibility parameter body using geostatistical methods;

[0034] The comprehensive compressibility index calculation module is used to calculate the weights of each parameter based on the well test flow rate and construct a comprehensive compressibility index model; based on the comprehensive compressibility index model, a three-dimensional comprehensive evaluation of the compressibility of sandstone and conglomerate in the exploration area with multiple parameters is calculated.

[0035] The advantages and positive effects of this invention are:

[0036] The method of this invention comprehensively considers both oil-bearing compressibility and engineering compressibility factors, and innovatively establishes a gravel heterogeneity index, which can accurately characterize the compressibility of sandstone and conglomerate. Furthermore, considering the limited experimental and logging data in offshore exploration areas, the method uses rock physics simulation to forward model the elasticity curves of all wells in the exploration area, thereby establishing a compressibility model and a three-dimensional compressibility complex at the logging scale. This facilitates the widespread application of compressibility evaluation methods in actual production. Attached Figure Description

[0037] Figure 1 This is a schematic diagram of the overall process of an embodiment of the method of the present invention.

[0038] Figure 2 This is a schematic diagram of the overall structure of an embodiment of the device of the present invention.

[0039] Figure 3 This is a diagram of the elastic curve calculation model of an embodiment of the method of the present invention.

[0040] Figure 4 This is a compressibility evaluation curve established based on the elasticity curve in an embodiment of the method of the present invention.

[0041] Figure 5 This is a three-dimensional volumetric schematic diagram of porosity parameters in an embodiment of the method of the present invention.

[0042] Figure 6 This is a three-dimensional volumetric schematic diagram of the saturation parameter in an embodiment of the method of the present invention.

[0043] Figure 7 This is a three-dimensional schematic diagram of the brittleness index parameter in an embodiment of the method of the present invention.

[0044] Figure 8 This is a three-dimensional schematic diagram of the horizontal stress difference coefficient parameter in an embodiment of the method of the present invention.

[0045] Figure 9 This is a three-dimensional schematic diagram of the gravel heterogeneity index parameters in an embodiment of the method of the present invention.

[0046] Figure 10 This is a three-dimensional schematic diagram of the fracture toughness parameters of an embodiment of the method of the present invention.

[0047] Figure 11 This is a schematic diagram of a three-dimensional complex for evaluating the compressibility of sandstone and conglomerate according to an embodiment of the method of the present invention. Detailed Implementation

[0048] The technical solution of the present invention will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0049] The embodiments of the present invention will be further described below with reference to the accompanying drawings:

[0050] like Figure 1 As shown, a method for three-dimensional distribution of compressibility in a conglomerate exploration area includes the following steps:

[0051] S1: Acquire experimental data, well logging curves, well logging data, test data, and 3D seismic data volumes for the observation area. Specifically, experimental data includes formation temperature and pressure, fluid parameters, mineral composition, mechanical parameters, stress and strain, energy evolution, tensile strength, compressive strength, fracture toughness, in-situ stress, and gravel composition. Well logging curves include P-wave velocity, S-wave velocity, density curves, and well logging curves. Well logging data includes the total mineral content of the well. 3D seismic data volumes include root-mean-square amplitude attribute volumes and instantaneous frequency volumes.

[0052] S2: Determine the parameters influencing the oil-bearing compressibility and engineering compressibility of sandstone and conglomerate. The parameters influencing the oil-bearing compressibility of sandstone and conglomerate include porosity and saturation. The parameters influencing the engineering compressibility of sandstone and conglomerate include stress difference coefficient, brittleness index, fracture toughness, and gravel heterogeneity index.

[0053] Specifically, the formula for calculating the stress difference coefficient is as follows:

[0054]

[0055] Where, σ Hmax σ hmin σ represents the maximum and minimum values ​​of the maximum horizontal in-situ stress in the study area. H σ h To study the maximum and minimum horizontal ground stress of the sample.

[0056] The formula for calculating the brittleness index is as follows:

[0057]

[0058] Where E is the elastic modulus of the sample, E max E min The maximum and minimum elastic moduli of the conglomerate in the study area are represented by μ. max μ min The maximum and minimum Poisson's ratios for conglomerate rocks in the study area are dimensionless. The brittleness index ranges from 0 to 1, with higher values ​​indicating greater brittleness.

[0059] The formula for calculating fracture toughness is as follows:

[0060]

[0061] K IIC =0.0956σ n +0.1383S t -0.0820

[0062]

[0063] Among them, the fracture toughness of type I is K IC Type II fracture toughness is K IIC ;σ n For confining pressure, S t For tensile strength; S max S min The maximum and minimum bond strengths of the sample are S and S', respectively; S' is the bond strength. c This represents the normalized bond strength.

[0064] The formula for calculating the gravel heterogeneity index is as follows:

[0065] I s =I gc I H

[0066] Among them, I H I is the material heterogeneity index of conglomerate. gc It is the structural heterogeneity index;

[0067]

[0068] Among them, C g For gravel content, when I gc When >1, I gc =1;

[0069]

[0070] Among them, I H H is the material heterogeneity index of conglomerate. g H represents the hardness of gravel. m The hardness is determined by the heterostructure.

[0071] S3: Based on experimental data and elastic logging curves from some wells, a forward modeling model of the sandstone and conglomerate rock in the exploration area was constructed to complete the missing elastic curves of the wells. Using the forward modeling method of rock physics, Powerlog software was used, and the XU-WHITE model was selected to construct the elastic curves of all wells in the observation area.

[0072] Specifically, the logging response of dry clay points was obtained using a triangular plot on the neutron-density cross-plot to evaluate the clay content. A three-porosity curve was used to correct for clay content in the porosity. The Archie formula was used to evaluate water saturation pairs. The XU-WHITE model was selected to establish a forward model of the rock physics of the observation area, thereby obtaining the elasticity curves of all wells in the region.

[0073] S4: Based on well logging data and experimental data, construct a calculation model for oil-bearing compressibility and engineering compressibility parameters at the well logging scale.

[0074] Specifically, the horizontal stress difference coefficient at the logging scale is calculated using the following formula:

[0075]

[0076] σ v =∫ρgdz

[0077] Where, σ H σ h These represent the maximum and minimum horizontal ground stresses, respectively, where α is the Biot coefficient, set to 1, and P P ζ represents the formation pore pressure, ζ1 and ζ2 are the geostress tectonic coefficients in the directions of maximum and minimum horizontal principal stress, respectively, and z is the reservoir depth.

[0078] The brittleness index at the well logging scale is calculated using the following formula:

[0079]

[0080] Among them, B n E is the normalized rock brittleness index. max and E min These are the maximum and minimum Young's modulus, ν. max and ν min These represent the maximum and minimum Poisson's ratios, respectively, and B is the brittleness index. max B min These are the minimum and maximum values ​​of the brittleness index, respectively.

[0081] Fracture toughness at the logging scale is calculated using the following formula:

[0082]

[0083]

[0084] K IIC =0.0956σ n +0.1383S t -0.0820

[0085] Where, σn For confining pressure, S t It represents the uniaxial tensile strength.

[0086] The gravel heterogeneity index at the logging scale is calculated using the following formula:

[0087]

[0088] Where Den is the density curve, and h i The hardness of the i-th mineral; c i The content of the i-th mineral obtained from the logging data.

[0089] The parameters were normalized. The brittleness index and porosity were positively correlated parameters, while the water saturation, gravel heterogeneity index, fracture toughness, and stress difference coefficient were negatively correlated parameters. The normalization formulas for the positive and negative correlated parameters were used respectively.

[0090] Specifically, the following formula is used to normalize the positive and negative correlation parameters:

[0091]

[0092] Where: N a N b These represent the normalized values ​​of the positive and negative parameters, respectively; X represents the original values ​​of the logging parameters, X... max X min These represent the maximum and minimum values ​​of logging parameters for each target layer.

[0093] S5: Using geostatistical methods, a three-dimensional volume of oil-bearing compressibility and engineering compressibility parameters is constructed. Specifically, using geostatistical methods, based on the wellbore compressibility parameter curves, and constrained by the root mean square amplitude attribute volume and instantaneous frequency attribute volume of seismic data, a three-dimensional volume calculation of compressibility parameters is carried out.

[0094] S6: Based on the well test mobility, calculate the weights of each parameter and construct a comprehensive compressibility index model; based on the comprehensive compressibility index model, calculate the three-dimensional comprehensive evaluation of the compressibility of sandstone and conglomerate in the exploration area using multi-parameter fusion. Specifically, the weights of each parameter are calculated using the analytic hierarchy process (AHP) based on grey relational analysis, the correlation degree of each parameter is calculated and sorted, and based on the correlation degree, a judgment matrix for AHP is established to obtain the weight values ​​of each parameter, thus constructing the comprehensive compressibility index model. Preferably, the reference column for the above grey relational calculation is the well test mobility.

[0095] Using well test mobility as a reference column, and porosity, water saturation, brittleness index, gravel heterogeneity index, stress difference coefficient, and stress difference coefficient as evaluation items, the grey relational method is used to calculate the correlation coefficient between each evaluation item and the reference column. The correlation degree is further calculated, and the correlation degree is ranked. The calculation is carried out with reference to the correlation degree and ranking. A judgment matrix for hierarchical analysis is established, and hierarchical analysis calculation is performed to obtain the weight values ​​of each relevant parameter of sandstone and conglomerate compressibility. Thus, a comprehensive compressibility evaluation model for sandstone and conglomerate in the exploration area is established.

[0096] like Figure 2 As shown, the present invention also provides a compressibility three-dimensional distribution device for sandstone and conglomerate exploration areas, which utilizes the above-mentioned compressibility three-dimensional distribution method for sandstone and conglomerate exploration areas, including,

[0097] The data acquisition module is used to acquire experimental data, well logging curves, well logging data, test data, and 3D seismic data volumes in the observation area.

[0098] The elastic curve calculation module is used to construct a forward model of the rock physics of sandstone and conglomerate in the exploration area based on experimental data and elastic logging curves of some wells, and to complete the elastic curves of missing wells.

[0099] The module for calculating the compressibility parameters of sandstone and conglomerate at the logging scale is used to construct calculation models for oil-bearing compressibility and engineering compressibility parameters at the logging scale based on logging data and experimental data.

[0100] The 3D sandstone and conglomerate compressibility parameter calculation module is used to construct a 3D oil-bearing compressibility and engineering compressibility parameter body using geostatistical methods.

[0101] The comprehensive compressibility index calculation module is used to calculate the weights of each parameter based on the well test flow rate and construct a comprehensive compressibility index model; based on the comprehensive compressibility index model, a three-dimensional comprehensive evaluation of the compressibility of sandstone and conglomerate in the exploration area with multiple parameters is calculated.

[0102] The compressibility evaluation and three-dimensional distribution of the middle and deep sandstone and conglomerate reservoirs in the Bohai Sea are then carried out using the method provided by this invention.

[0103] like Figure 3 As shown, the compressibility parameter curve is established using the elastic curve obtained from rock physics forward modeling. Figure 4 As shown. A three-dimensional compressibility parameter volume was constructed using geostatistical methods, such as... Figure 5-10 As shown. Figure 11 As shown in Table 1, the correlation degree and ranking between each parameter and the mobility were established using the grey relational analysis method.

[0104] Table 1. Association Results

[0105] Evaluation items correlation Ranking Porosity 0.694 3 water saturation 0.744 1 Brittleness Index 0.408 6 Gravel heterogeneity index 0.737 2 Stress difference coefficient 0.512 4 fracture toughness 0.505 5

[0106] Based on the correlation results and rankings, a hierarchical analysis judgment matrix is ​​established, as shown in Table 2.

[0107] Table 2 Parameter Judgment Matrix

[0108]

[0109] The calculated weights for porosity, water saturation, brittleness index, gravel heterogeneity index, stress difference coefficient, and fracture toughness are 0.19, 0.21, 0.11, 0.2, 0.14, and 0.14, respectively.

[0110] Therefore, the comprehensive evaluation model for the compressibility of sandstone and conglomerate reservoirs is as follows:

[0111]

[0112] As can be seen from the above embodiments, the method provided by the present invention comprehensively considers both oil-bearing compressibility and engineering compressibility factors, and innovatively establishes a gravel heterogeneity index, which can accurately characterize the compressibility of sandstone and conglomerate. Furthermore, considering the limited experimental and logging data in offshore exploration areas, the method uses rock physics simulation to forward model the elasticity curves of all wells in the exploration area, thereby establishing a compressibility model and a three-dimensional compressibility complex at the logging scale. This facilitates the promotion and application of compressibility evaluation methods in actual production, and the practical application results are good.

[0113] The foregoing has provided a detailed description of one embodiment of the present invention, but this description is merely a preferred embodiment and should not be construed as limiting the scope of the invention. All equivalent variations and modifications made within the scope of the claims of this invention should still fall within the patent coverage of this invention.

Claims

1. A method for three-dimensional distribution of compressibility in sandstone and conglomerate exploration areas, characterized in that: Includes the following steps, S1: Acquire experimental data, well logging curves, well logging data, test data, and 3D seismic data volumes for the observation area; S2: Determine the parameters affecting the oil compressibility and engineering compressibility of sandstone and conglomerate; S3: Based on the experimental data and some well elastic logging curves, construct a forward model of the petrology of the sandstone and conglomerate area in the exploration area, and complete the missing well elastic curves; S4: Based on well logging data and experimental data, construct a calculation model for oil-bearing compressibility and engineering compressibility parameters at the well logging scale; S5: Using geostatistical methods, a three-dimensional system of oil-bearing compressibility and engineering compressibility parameters was constructed; S6: Based on the well test flow rate, calculate the weights of each parameter and construct a comprehensive compressibility index model; based on the comprehensive compressibility index model, calculate the three-dimensional comprehensive evaluation of the compressibility of sandstone and conglomerate in the exploration area using multi-parameter fusion.

2. The method for three-dimensional distribution of compressibility in a sandstone and conglomerate exploration area according to claim 1, characterized in that: In S1, the experimental data includes formation temperature and pressure, fluid parameters, mineral composition, mechanical parameters, stress and strain, tensile strength, compressive strength, fracture toughness, geostress, and gravel composition; the logging curves include P-wave velocity, S-wave velocity, and density curves; the logging data includes the whole-well mineral content; the test data is pressure and flow rate data; and the three-dimensional seismic data volume includes root mean square amplitude attribute volume and instantaneous frequency volume.

3. A method for three-dimensional distribution of compressibility in a sandstone and conglomerate exploration area according to claim 1 or 2, characterized in that: In S2, the parameters affecting the compressibility of oil-bearing conglomerate include porosity and saturation; the parameters affecting the engineering compressibility of conglomerate include stress difference coefficient, brittleness index, fracture toughness, and gravel heterogeneity index.

4. The method for three-dimensional distribution of compressibility in sandstone and conglomerate exploration areas according to claim 3, characterized in that: The formula for calculating the gravel heterogeneity index is as follows. I s =I gc I H Among them, I H I is the material heterogeneity index of conglomerate. gc It is the structural heterogeneity index; Among them, C g For gravel content, when I gc When >1, I gc =1; Among them, I H H is the material heterogeneity index of conglomerate. g H represents the hardness of gravel. m The hardness is determined by the heterostructure.

5. A method for three-dimensional distribution of compressibility in a sandstone and conglomerate exploration area according to claim 1 or 2, characterized in that: In S3, using the rock physics forward modeling method, Powerlog software, and the XU-WHITE model, elastic curves of all wells in the observation area were constructed.

6. The method for three-dimensional distribution of compressibility in a sandstone and conglomerate exploration area according to claim 3, characterized in that: In S4, the gravel heterogeneity index at the logging scale is calculated using the following formula: Where Den is the density curve, and h i The hardness of the i-th mineral; c i The content of the i-th mineral obtained from the logging data.

7. The method for three-dimensional distribution of compressibility in a sandstone and conglomerate exploration area according to claim 3, characterized in that: In step S4, each parameter is normalized. The brittleness index and porosity are positively correlated parameters, while the water saturation, gravel heterogeneity index, fracture toughness, and stress difference coefficient are negatively correlated parameters. The normalization formulas for the positive and negatively correlated parameters are used respectively.

8. A method for three-dimensional distribution of compressibility in a sandstone and conglomerate exploration area according to claim 1 or 2, characterized in that: In S5, geostatistical methods are used to establish a three-dimensional oil-bearing compressibility volume and an engineering compressibility parameter volume, constrained by a three-dimensional seismic data volume.

9. A method for three-dimensional distribution of compressibility in a sandstone and conglomerate exploration area according to claim 1 or 2, characterized in that: In step S6, the weights of each parameter are calculated using the analytic hierarchy process based on grey relational analysis, the correlation degree of each parameter is calculated and sorted, and a judgment matrix of the analytic hierarchy process is established based on the correlation degree to obtain the weight values ​​of each parameter and construct a comprehensive compressibility index model.

10. A compressibility three-dimensional distribution device for a sandstone and conglomerate exploration area, utilizing the compressibility three-dimensional distribution method for sandstone and conglomerate exploration areas as described in any one of claims 1 to 9, characterized in that: include, The data acquisition module is used to acquire experimental data, well logging curves, well logging data, test data, and 3D seismic data volumes in the observation area. The elastic curve calculation module is used to construct a forward model of the petrology of sandstone and conglomerate in the exploration area based on experimental data and elastic logging curves of some wells, and to complete the elastic curves of missing wells. The module for calculating the compressibility parameters of sandstone and conglomerate at the logging scale is used to construct calculation models for oil-bearing compressibility and engineering compressibility parameters at the logging scale based on logging data and experimental data. The 3D sandstone and conglomerate compressibility parameter calculation module is used to construct a 3D oil-bearing compressibility and engineering compressibility parameter body using geostatistical methods; The comprehensive compressibility index calculation module is used to calculate the weights of each parameter based on the well test flow rate and construct a comprehensive compressibility index model; based on the comprehensive compressibility index model, a three-dimensional comprehensive evaluation of the compressibility of sandstone and conglomerate in the exploration area with multiple parameters is calculated.

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