Shale reservoir compressibility evaluation method and system based on support vector machine technology

Abstract

The invention provides a shale reservoir compressibility evaluation method and system based on a support vector machine technology, and relates to the technical field of energy engineering, and the method specifically comprises the steps: collecting geometric parameters and physical property parameters of a to-be-evaluated shale reservoir, calculating a stiffness index, a flow capacity evaluation coefficient and a heat energy storage evaluation coefficient, and calculating a flow capacity evaluation coefficient of the to-be-evaluated shale reservoir; generating an initial compressibility evaluation index; environment parameters of a shale reservoir to be evaluated are collected, an environment influence index is obtained through calculation based on the environment parameters, the initial compressibility evaluation index is corrected according to the obtained environment influence index, a comprehensive compressibility evaluation index is obtained, the generated comprehensive compressibility evaluation index serves as input, and the shale reservoir to be evaluated is obtained. According to the method, a compressibility evaluation model based on the support vector machine technology is constructed and used for predicting the compressibility of the shale reservoir to be evaluated, the environment parameters comprise the environment temperature and formation pressure data of the position where the shale reservoir to be evaluated is located, and a more scientific and reliable basis is provided for development of shale gas and shale oil.

Description

Technical Field

[0001] The present invention relates to the technical field of energy engineering, and particularly to a method and system for evaluating the compressibility of shale reservoirs based on support vector machine technology. Background Art

[0002] With the growing global demand for clean energy, the development of shale gas and shale oil has gradually become an important source of energy supply. However, the complexity of shale reservoirs makes the evaluation of their compressibility particularly complex. Traditional compressibility evaluation methods usually rely on simple experimental data and empirical formulas, and these methods are mostly based on a single physical parameter, such as permeability or porosity, to judge the compressibility of the reservoir. This single-index evaluation method cannot comprehensively reflect the actual situation of shale reservoirs, because the compressibility of shale is not only affected by porosity and permeability, but also closely related to the physical properties, structural characteristics of the rock and environmental factors. Therefore, traditional methods often lead to insufficient accuracy of evaluation results and are difficult to provide effective guidance for actual development.

[0003] In addition, there are obvious deficiencies in data processing and analysis in the prior art. Many evaluation methods fail to effectively integrate various influencing factors, especially when dealing with complex coupling relationships, often ignoring the interaction between different physical parameters. This lack of systematic method leads to one-sidedness of evaluation and is difficult to adapt to the characteristics of shale reservoirs under different geological conditions. At the same time, traditional methods are difficult to make dynamic adjustments under changing environmental conditions and cannot reflect the state of the reservoir in real time, thus affecting the scientific nature of development decisions. Therefore, there is an urgent need for a more comprehensive, accurate and adaptable compressibility evaluation method to meet the increasingly complex development requirements of shale reservoirs.

[0004] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0005] The purpose of the present invention is to provide a method and system for evaluating the compressibility of shale reservoirs based on support vector machine technology to solve the problems raised in the above background art.

[0006] To achieve the above purpose, the present invention provides the following technical solutions:

[0007] A method for evaluating the compressibility of shale reservoirs based on support vector machine technology, the specific steps include:

[0008] Step 1: Collect the geometric parameters and physical property parameters of the shale reservoir to be evaluated. The geometric parameters include porosity and permeability, and the physical property parameters include rock density, thermal conductivity, water saturation, and stiffness index. Additionally, an interaction term is processed for the rock density and water saturation, and the first coupling coefficient is calculated.

[0009] Step 2: Calculate the flow capacity evaluation coefficient based on the dimensionless porosity and permeability, and calculate the thermal energy storage evaluation coefficient based on the dimensionless first coupling coefficient, thermal conductivity.

[0010] Step 3: Combine the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient to generate an initial compressibility evaluation index. The weights of the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient in the calculation of the initial compressibility evaluation index are determined based on the analytic hierarchy process.

[0011] Step 4: Collect the environmental parameters of the shale reservoir to be evaluated, calculate the environmental impact index based on the environmental parameters, correct the initial compressibility evaluation index according to the obtained environmental impact index to obtain the comprehensive compressibility evaluation index. Use the generated comprehensive compressibility evaluation index as the input to construct a compressibility evaluation model based on support vector machine technology for predicting the compressibility of the shale reservoir to be evaluated. The environmental parameters include the environmental temperature and formation pressure data at the location of the shale reservoir to be evaluated.

[0012] Furthermore, take three equal - amount samples from the shale reservoir to be evaluated, calculate the porosity and water saturation respectively, and take the average of the three results as the porosity and water saturation of the shale reservoir to be evaluated. Denote the porosity of the shale reservoir to be evaluated as Denote the water saturation of the shale reservoir to be evaluated as S w ;

[0013] The formula for calculating porosity is:

[0014]

[0015] In the formula, φ is the porosity, V p is the pore volume, and V t is the total sample volume;

[0016] The formula for calculating water saturation is:

[0017]

[0018] In the formula, S w is the water saturation, V p is the pore volume, and V t is the volume of water in the pores;

[0019] To obtain the permeability of the shale reservoir to be evaluated, the specific logic is as follows: Extract a core sample from the shale reservoir, cut the core sample into a cylinder with a diameter of a and a length of n, and ensure that the surface is flat. Immerse the core sample in water to ensure that the pores are completely saturated, avoiding the influence of gas on the test results. Use a permeameter to test the water-saturated core sample: Apply a water head difference of ΔP at both ends of the core sample, record the flow rate change, and calculate the permeability of the core sample according to the following formula, and use this as the permeability of the shale reservoir to be evaluated:

[0020]

[0021] In the formula, k is the permeability, Q is the flow rate, A is the cross-sectional area of the core sample, ΔP is the water head difference, L is the length of the core sample, and is the viscosity of water.

[0022] Furthermore, to obtain the stiffness index, the specific logic is as follows: Obtain the elastic modulus and Poisson's ratio of the shale reservoir to be evaluated, and calculate the stiffness index according to the dimensionless elastic modulus and Poisson's ratio. The formula is as follows:

[0023]

[0024] In the formula, STI is the stiffness index, E is the elastic modulus, and σ is Poisson's ratio;

[0025] Perform an interaction term treatment on the dimensionless rock density and water saturation to calculate the first coupling coefficient. The formula is as follows:

[0026]

[0027] In the formula, C1 is the first coupling coefficient, ρ is the rock density, η is the water saturation, and η ref is the reference threshold of the water saturation.

[0028] Furthermore, calculate the flow capacity evaluation coefficient according to the dimensionless porosity and permeability. The formula is:

[0029]

[0030] In the formula, A is the flow capacity evaluation coefficient, is the porosity, k is the permeability, and e is the natural constant;

[0031] Calculate the thermal energy storage evaluation coefficient according to the dimensionless first coupling coefficient and thermal conductivity. The formula is:

[0032]

[0033] Wherein, B is the thermal energy storage evaluation coefficient, C1 is the first coupling coefficient, Λ is the thermal conductivity, and e is the natural constant;

[0034] Further, combine the stiffness index, the flowability evaluation coefficient, and the thermal energy storage evaluation coefficient to generate an initial compressibility evaluation index:

[0035]

[0036] Wherein, X is the initial compressibility evaluation index, STI is the stiffness index, A is the flowability evaluation coefficient, B is the thermal energy storage evaluation coefficient, and B ref is the reference threshold of the thermal energy storage evaluation coefficient, and ω1, ω2, and ω3 are weights determined according to the analytic hierarchy process.

[0037] Further, determine the weights of the stiffness index, the flowability evaluation coefficient, and the thermal energy storage evaluation coefficient in the calculation of the initial compressibility evaluation index according to the analytic hierarchy process. The specific logic is as follows:

[0038] Mark the three indicators of the stiffness index, the flowability evaluation coefficient, and the thermal energy storage evaluation coefficient, determine the numerical values of the relative importance between each pair through the nine-scale method, and construct a judgment matrix. Among them, mark the stiffness index as 1, the flowability evaluation coefficient as 2, and the thermal energy storage evaluation coefficient as 3. The constructed judgment matrix is:

[0039]

[0040] Wherein, both f and b represent the indices of the exponents, and f ∈ [1, 3], v ∈ [1, 3], and b fv represents the importance degree of the exponent with index f relative to the exponent with index v. The importance degree adopts the 1-9 scale method, and b fv The larger the value, the greater the importance degree of the exponent with index f compared to the exponent with index v, and

[0041] Divide each element value in the judgment matrix by the sum of its columns to obtain a normalized judgment matrix. Calculate the mean value of each row element value in the normalized judgment matrix, and take the mean value of the first row element value as the weight of the stiffness index, take the mean value of the second row element value as the weight of the flowability evaluation coefficient, and take the mean value of the third row element value as the weight of the thermal energy storage evaluation coefficient. With the constraint that the sum of the scaled values is equal to 1, scale the three weights proportionally, and take the scaled weights as the proportional coefficients of the corresponding exponents.

[0042] Further, collect the environmental temperature and formation pressure data at the location of the shale reservoir to be evaluated, and calculate the environmental impact index based on the environmental parameters:

[0043]

[0044] In the formula, ENV is the environmental impact index, T is the environmental temperature, and T ref is the reference threshold of the environmental temperature, G is the formation pressure, and G ref is the reference threshold of the formation pressure;

[0045] According to the obtained environmental impact index, the initial compressibility evaluation index is corrected to obtain the comprehensive compressibility evaluation index:

[0046] QS = X * (1 + μ * ENV)

[0047] In the formula, QS is the comprehensive compressibility evaluation index, X is the initial compressibility evaluation index, ENV is the environmental impact index, and μ is the weight coefficient of the environmental impact index, which is used to adjust the influence degree of the environmental impact index on the comprehensive compressibility evaluation index and is set according to the actual situation.

[0048] Furthermore, a compressibility evaluation model based on support vector machine technology is constructed. A number of groups of historical data sets are used to train the model to obtain a trained compressibility evaluation model. The historical data sets include historical comprehensive compressibility evaluation indexes and the compressibility evaluations of corresponding shale reservoirs. Among them, the compressibility evaluations in the historical data sets are specifically the compressibility grades of shale reservoirs, which are determined by the expert evaluation method; the comprehensive compressibility evaluation index of the to-be-evaluated shale reservoir generated is used as the input, and the model outputs the compressibility evaluation of the to-be-evaluated shale reservoir. Among them, the compressibility evaluation output by the model is the compressibility grade of the to-be-evaluated shale reservoir;

[0049] Among them, the classification standard of the compressibility grade is:

[0050] When QS ≤ 0.5QY, it is judged that the compressibility grade of the to-be-evaluated shale reservoir is low compressibility, the shale reservoir has poor compressibility, and the mining difficulty is high;

[0051] When 0.5QY < QS ≤ QY, it is judged that the compressibility grade of the to-be-evaluated shale reservoir is medium compressibility, the shale reservoir has good compressibility, and the mining is relatively smooth;

[0052] When QS > QY, it is judged that the compressibility grade of the to-be-evaluated shale reservoir is high compressibility, and the mining potential is large;

[0053] Among them, QS is the comprehensive compressibility evaluation index, and QY is the compressibility judgment threshold.

[0054] The present invention also provides a shale reservoir compressibility evaluation system based on support vector machine technology. The shale reservoir compressibility evaluation system based on support vector machine technology is used to execute the above-mentioned shale reservoir compressibility evaluation method based on support vector machine technology, including:

[0055] A parameter acquisition module is used to acquire the geometric parameters and physical property parameters of the shale reservoir to be evaluated. The geometric parameters include porosity and permeability, and the physical property parameters include rock density, thermal conductivity, water saturation, and stiffness index. An interaction term is added to the rock density and water saturation, and the first coupling coefficient is calculated.

[0056] A data processing module is used to calculate the flow capacity evaluation coefficient according to the dimensionless porosity and permeability, and calculate the thermal energy storage evaluation coefficient according to the dimensionless first coupling coefficient, thermal conductivity.

[0057] An index generation module is used to combine the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient to generate an initial compressibility evaluation index. The weights of the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient in the calculation of the initial compressibility evaluation index are determined based on the analytic hierarchy process.

[0058] A correction module is used to acquire the environmental parameters of the shale reservoir to be evaluated, calculate the environmental impact index based on the environmental parameters, correct the initial compressibility evaluation index according to the obtained environmental impact index to obtain the comprehensive compressibility evaluation index, and use the generated comprehensive compressibility evaluation index as the input to construct a compressibility evaluation model based on support vector machine technology for predicting the compressibility of the shale reservoir to be evaluated. The environmental parameters include the environmental temperature and formation pressure data of the location where the shale reservoir to be evaluated is located.

[0059] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0060] First, by comprehensively acquiring various geometric parameters and physical property parameters of the shale reservoir to be evaluated, including porosity, permeability, rock density, thermal conductivity, water saturation, and stiffness index, etc., this method can comprehensively reflect the physical characteristics of the reservoir. This multi-parameter acquisition and processing method, combined with the interaction term processing, ensures that the mutual influence between parameters is fully considered, thereby calculating the first coupling coefficient and providing a more accurate basis for subsequent evaluation.

[0061] Second, through the calculation of the flow capacity evaluation coefficient and the thermal energy storage evaluation coefficient, the present invention realizes the multi-dimensional evaluation of the reservoir compressibility. The calculation of the flow capacity evaluation coefficient and the thermal energy storage evaluation coefficient not only considers the dimensionless processing of porosity and permeability, but also improves the scientificity of the evaluation through the application of natural constants. This process, combined with the weights determined by the analytic hierarchy process, ensures the balance of the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient in the calculation of the initial compressibility evaluation index, so that the generated compressibility evaluation index can reflect the true reservoir performance.

[0062] Finally, the present invention also introduces the calculation of the environmental impact index, further enhancing the dynamic adaptability of the evaluation. By collecting environmental temperature and formation pressure data and combining the correction of the initial compressibility evaluation index with the environmental impact index, it ensures that the evaluation results can reflect the changes of the reservoir under different environmental conditions in real time. This dynamic correction mechanism not only improves the accuracy of the evaluation but also provides feasible development suggestions for decision-makers, thereby optimizing resource utilization and economic benefits. Overall, the invention realizes a comprehensive improvement of the shale reservoir compressibility evaluation method through advanced technical means, providing a more scientific and reliable basis for the development of shale gas and shale oil. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] Figure 1 It is a schematic diagram of the overall method flow of the present invention;

[0064] Figure 2 It is a schematic diagram of the system module of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0065] 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 in conjunction with specific embodiments.

[0066] It should be noted that unless otherwise defined, the technical terms or scientific terms used in the present invention should have the ordinary meanings understood by those of ordinary skill in the field to which the present invention belongs. The "first", "second", and similar terms used in the present invention do not denote any order, quantity, or importance, but are only used to distinguish different components. The terms such as "comprising" or "including" mean that the elements or objects appearing before this term cover the elements or objects listed after this term and their equivalents, without excluding other elements or objects. The terms such as "connected" or "coupled" are not limited to physical or mechanical connections, but may include electrical connections, whether direct or indirect. The terms such as "upper", "lower", "left", "right", etc. are only used to represent relative positional relationships, and when the absolute position of the object being described changes, the relative positional relationship may also change accordingly.

[0067] Embodiment:

[0068] Please refer to Figure 1 , the present invention provides a technical solution:

[0069] A method for evaluating the compressibility of a shale reservoir based on support vector machine technology, the specific steps include:

[0070] Step 1: Collect the geometric parameters and physical property parameters of the shale reservoir to be evaluated. The geometric parameters include porosity and permeability, and the physical property parameters include rock density, thermal conductivity, water saturation, and stiffness index. Additionally, an interaction term is processed for the rock density and water saturation, and the first coupling coefficient is calculated.

[0071] In this embodiment, three equal - amount samplings are performed on the shale reservoir to be evaluated. The porosity and water saturation are calculated respectively, and the average of the three results is taken as the porosity and water saturation of the shale reservoir to be evaluated. Denote the porosity of the shale reservoir to be evaluated as Denote the water saturation of the shale reservoir to be evaluated as S w ;

[0072] The formula for calculating porosity is:

[0073]

[0074] where φ is the porosity, V p is the pore volume, and V t is the total sample volume;

[0075] The formula for calculating water saturation is:

[0076]

[0077] where S w is the water saturation, V p is the pore volume, and V t is the volume of water in the pores;

[0078] To obtain the permeability of the shale reservoir to be evaluated, the specific logic is as follows: Extract a core sample from the shale reservoir. Cut the core sample into a cylinder with a diameter of a and a length of b, and ensure that the surface is flat. Immerse the core sample in water to ensure that the pores are completely saturated, avoiding the influence of gas on the test results. Use a permeameter to test the water - saturated core sample: Apply a head difference of ΔP at both ends of the core sample, record the flow rate change, and calculate the permeability of the core sample according to the following formula, and use this as the permeability of the shale reservoir to be evaluated:

[0079]

[0080] where k is the permeability, Q is the flow rate, A is the cross - sectional area of the core sample, ΔP is the head difference, L is the length of the core sample, is the viscosity of water.

[0081] The specific logic for obtaining the stiffness index is as follows: obtain the elastic modulus and Poisson's ratio of the shale reservoir to be evaluated, and calculate the stiffness index based on the dimensionless elastic modulus and Poisson's ratio. The formula is as follows:

[0082]

[0083] Where STI is the stiffness index, which reflects the rigidity and stability of the shale reservoir under external loads. The higher the value, the stronger the stability and deformation resistance of the shale reservoir under external loads. E is the elastic modulus, and σ is the Poisson's ratio. and E 2 The form of the equation allows the effects of Poisson's ratio and elastic modulus to be calculated in a nonlinear manner, making the formula more than just a linear superposition, which is more reasonable in the mechanical properties of actual materials. In this way, the stability of shale reservoirs can be more comprehensively reflected.

[0084] In the evaluation of the compressibility of shale reservoirs, the STI value can provide researchers with important information about the reservoir's bearing capacity, helping to predict stability and deformation behavior during the mining process, and thus affecting mining strategies and designs. A higher elastic modulus means that the material can maintain its shape and deform less when subjected to stress, indicating higher rigidity. The Poisson's ratio reflects the material's lateral deformation capacity. A higher Poisson's ratio indicates that the material deforms more laterally when subjected to stress, resulting in material instability. In other words, E is positively correlated with STI, and σ is negatively correlated with STI.

[0085] The interaction term is added to the dimensionless rock density and water saturation to calculate the first coupling coefficient. The formula is as follows:

[0086]

[0087] Where C1 is the first coupling coefficient, which is used to reflect the effect of the interaction between rock density and water saturation on the thermal energy storage capacity of shale reservoirs; ρ is rock density, η is water saturation, and η ref is the reference threshold of water saturation.

[0088] Rocks with higher density usually have lower porosity, which will affect the storage and heat exchange capacity of fluids, thus affecting the thermal energy storage performance. Therefore, when ρ increases, C1 decreases; when |η-η ref When | increases, it means that the gap between the current water saturation and the reference value becomes larger, the thermal energy storage capacity of the rock will be suppressed, and the influence of the coupling effect will be weakened, that is, C1 decreases.

[0089] The advantage of Step 1 is that by collecting the geometric parameters and physical property parameters of the shale reservoir to be evaluated and adding interaction term processing to calculate the first coupling coefficient, this step can comprehensively and systematically reflect the physical characteristics of the reservoir. This method is different from the traditional single-parameter evaluation and can effectively capture the mutual relationship between parameters, ensuring that the evaluation results are more accurate.

[0090] Compared with the traditional method, this step not only improves the accuracy of the compressibility evaluation through the collection and analysis of multi-dimensional data, but also can better adapt to the complexity under different geological conditions. By introducing the interaction term, the sensitivity of the model to the change of reservoir characteristics is further enhanced, making the evaluation results more realistic and reducing the risk of misjudgment caused by a single parameter. Adopting this step can lay a solid foundation for the calculation of the subsequent flow capacity evaluation coefficient and the thermal energy storage evaluation coefficient. Through accurate initial parameter input, the subsequent construction of the support vector machine model will be more reliable, thus enhancing the prediction ability of the overall scheme for the compressibility of the shale reservoir, improving the effectiveness and application value of the evaluation model, and providing a more scientific basis for actual development.

[0091] Step 2: Calculate the flow capacity evaluation coefficient according to the dimensionless porosity and permeability, and calculate the thermal energy storage evaluation coefficient according to the dimensionless first coupling coefficient and thermal conductivity;

[0092] In this embodiment, the formula for calculating the flow capacity evaluation coefficient according to the dimensionless porosity and permeability is as follows:

[0093]

[0094] In the formula, A is the flow capacity evaluation coefficient, is the porosity, k is the permeability, and e is the natural constant; the formula adopts an exponential form, and the exponential function is very sensitive to the change of the input value, which indicates that the slight change of the porosity and permeability will have a significant impact on the flow capacity evaluation coefficient, reflecting the complexity and non-linear characteristics of the fluid flow characteristics in the rock medium.

[0095] A higher porosity means that there is more space to accommodate the fluid, thus improving the flow capacity, that is, when increases, A increases accordingly; the permeability is used to describe the ability of the fluid to pass through the rock. A higher permeability means that the fluid can flow more easily in the rock, that is, when k increases, A increases accordingly.

[0096] The formula for calculating the thermal energy storage evaluation coefficient according to the dimensionless first coupling coefficient and thermal conductivity is as follows:

[0097]

[0098] In the formula, B is the thermal energy storage evaluation coefficient, C1 is the first coupling coefficient, Λ is the thermal conductivity, and e is the natural constant. By utilizing the characteristics of the exponential function, this formula combines the first coupling coefficient and the thermal conductivity, reflecting the complexity and non-linear characteristics of the thermal energy storage capacity. In this form, it is possible to effectively evaluate and understand the thermal energy storage performance of rock materials, which is of great significance for geothermal energy development and related engineering applications.

[0099] In the evaluation of thermal energy storage, the magnitude of the thermal conductivity directly affects the heat transfer efficiency and storage capacity. A higher thermal conductivity means that heat can be propagated more effectively in the rock, thereby enhancing the thermal energy storage capacity, that is, when Λ increases, B increases accordingly; C1 represents the influence of the interaction between rock density and water saturation on the thermal energy storage capacity, and when C1 increases, B increases correspondingly.

[0100] In step 2, the flow capacity evaluation coefficient is calculated by using the dimensionless porosity and permeability, and the thermal energy storage evaluation coefficient is calculated by using the dimensionless first coupling coefficient and thermal conductivity, thereby realizing the quantitative analysis of the flow characteristics and thermal energy storage characteristics of the shale reservoir to be evaluated. Through this dimensionless treatment method, the comparability between parameters is improved, and the error caused by different units or magnitudes is reduced, making the evaluation results more scientific and operable.

[0101] Compared with the traditional method, the dimensionless treatment adopted in step 2 can effectively eliminate the differences between different geological environments and samples, enhancing the adaptability and prediction ability of the evaluation model. The introduction of this methodology not only improves the evaluation accuracy of the flow capacity and thermal energy storage, but also makes the calculation of the comprehensive compressibility evaluation index more robust, helping to more truly reflect the actual situation of the shale reservoir to be evaluated. By adopting the calculation method in step 2, it provides key flow capacity and thermal energy storage data support for the subsequent comprehensive compressibility evaluation. These quantified and standardized evaluation coefficients will be directly incorporated into the generation of the initial compressibility evaluation index, ensuring that the overall scheme has higher accuracy and reliability when evaluating the compressibility of the shale reservoir, thereby enhancing the prediction ability and practical value of the entire model.

[0102] Step 3: Combine the stiffness index, the flow capacity evaluation coefficient, and the thermal energy storage evaluation coefficient to generate the initial compressibility evaluation index, and the weights of the stiffness index, the flow capacity evaluation coefficient, and the thermal energy storage evaluation coefficient in the calculation of the initial compressibility evaluation index are determined based on the analytic hierarchy process;

[0103] In this embodiment, the stiffness index, the flow capacity evaluation coefficient, and the thermal energy storage evaluation coefficient are combined to generate the initial compressibility evaluation index:

[0104]

[0105] Wherein, X is the initial compressibility evaluation index, STI is the stiffness index, A is the flowability evaluation coefficient, B is the heat storage evaluation coefficient, and B ref is the reference threshold of the heat storage evaluation coefficient, determined based on historical data analysis and the expert evaluation method. ω1, ω2, and ω3 are weights, determined according to the analytic hierarchy process. The cube root form is used in the formula to smooth the magnitude values, which can effectively reduce the influence brought by large numerical changes and make the final result more comparable. The logarithmic form is used to compress the numerical range, making the large-range data easier to process and understand and reducing the influence of extreme values on the result.

[0106] When STI increases, the degree of rock deformation will decrease, and the compressibility will decrease, that is, X will decrease; when A increases, it means that the fluidity of the material is stronger, the resistance of the fluid flowing in the material is smaller, and X will be larger, and the compressibility increases; when |B - B ref | increases, it indicates that the difference between the heat storage capacity of the shale reservoir and the reference value increases, resulting in the unstable performance of the shale reservoir under high or low temperature conditions, and the compressibility decreases. That is to say, STI, |B - B ref | is negatively correlated with X, and A is positively correlated with X.

[0107] According to the analytic hierarchy process, determine the weights of the stiffness index, the flowability evaluation coefficient, and the heat storage evaluation coefficient in the calculation of the initial compressibility evaluation index. The specific logic is as follows:

[0108] Mark the three indicators of the stiffness index, the flowability evaluation coefficient, and the heat storage evaluation coefficient, and determine the numerical values of the relative importance between each pair through the nine-scale method to construct a judgment matrix. Among them, mark the stiffness index as 1, the flowability evaluation coefficient as 2, and the heat storage evaluation coefficient as 3. The constructed judgment matrix is:

[0109]

[0110] Wherein, both f and v represent the indices of the indices, and f ∈ [1, 3], v ∈ [1, 3], and b fv represents the importance degree of the index with index f relative to the index with index v. The importance degree adopts the 1-9 scale method, and b fv The larger the value, the greater the importance degree of the index with index f compared to the index with index v, and

[0111] Divide each element value in the judgment matrix by the sum of its column to obtain a normalized judgment matrix. Calculate the mean value of the element values in each row of the normalized judgment matrix, take the mean value of the first row element values as the weight of the stiffness index, take the mean value of the second row element values as the weight of the flowability evaluation coefficient, and take the mean value of the third row element values as the weight of the heat storage evaluation coefficient. With the constraint that the sum of the scaled values equals 1, scale the three weights proportionally and use the scaled weights as the proportionality coefficients of the corresponding indices.

[0112] In step 3, combine the stiffness index, the flowability evaluation coefficient, and the heat storage evaluation coefficient to generate an initial compressibility evaluation index. This process uses the analytic hierarchy process to determine the weights of various parameters in the comprehensive evaluation, enabling different characteristics to be reasonably reflected in the final evaluation. This method of comprehensively considering multiple indicators can improve the overall accuracy and scientific nature of the evaluation, thus more comprehensively reflecting the actual compressibility of the shale reservoir.

[0113] Different from the traditional method that solely relies on a certain indicator or empirical formula, step 3 systematically processes multiple indicators, eliminating the one-sidedness that a single parameter may bring, making the compressibility evaluation more comprehensive and objective. This method of weight allocation can better adapt to changes under complex geological conditions, thereby improving the applicability and reliability of the evaluation model. Adopting step 3 to integrate different evaluation coefficients helps to form a more scientific initial compressibility evaluation index as the basis for the subsequent evaluation model. This integration not only enhances the prediction ability of the model but also provides more reliable initial data for the correction of the influence of environmental parameters, ultimately promoting the effectiveness and accuracy of the overall solution in practical applications. By establishing a more accurate evaluation framework, it promotes scientific decision-making and implementation in shale reservoir development.

[0114] Step 4: Collect the environmental parameters of the shale reservoir to be evaluated, calculate the environmental impact index based on the environmental parameters, correct the initial compressibility evaluation index according to the obtained environmental impact index to obtain the comprehensive compressibility evaluation index, use the generated comprehensive compressibility evaluation index as the input to construct a compressibility evaluation model based on support vector machine technology for predicting the compressibility of the shale reservoir to be evaluated. The environmental parameters include the environmental temperature and formation pressure data at the location of the shale reservoir to be evaluated.

[0115] In this embodiment, collect the environmental temperature and formation pressure data at the location of the shale reservoir to be evaluated, and calculate the environmental impact index based on the environmental parameters:

[0116]

[0117] In the formula, ENV is the environmental impact index, T is the environmental temperature, T refis the reference threshold of the ambient temperature, i.e., the ideal value of the ambient temperature, and G is the formation pressure, G ref is the reference threshold of the formation pressure, i.e., the ideal value of the formation pressure;

[0118] When |T - T ref | or |G - G ref | increases, it indicates that the gap between the current environment and the ideal state widens, resulting in a decline in the performance of the reservoir and a weakening of adaptability. That is to say, |T - T ref |, |G - G ref | is negatively correlated with ENV.

[0119] According to the obtained environmental impact index, the initial compressibility evaluation index is corrected to obtain the comprehensive compressibility evaluation index:

[0120] QS = X * (1 + μ * ENV)

[0121] In the formula, QS is the comprehensive compressibility evaluation index, X is the initial compressibility evaluation index, ENV is the environmental impact index, and μ is the weight coefficient of the environmental impact index, which is used to adjust the influence degree of the environmental impact index on the comprehensive compressibility evaluation index, and 0 ≤ μ ≤ 1. The specific value is set according to the actual situation.

[0122] This formula effectively takes into account the influence of the environmental impact index ENV on the initial compressibility evaluation index X. Such a combination form reasonably reflects the correction effect of environmental impact on the initial evaluation. When ENV increases, it means that the environmental conditions are approaching the reference state, the compressibility performance of the reservoir is improved, and QS increases. That is to say, ENV is positively correlated with QS.

[0123] Construct a compressibility evaluation model based on the support vector machine technology, and use several groups of historical data sets to train the model to obtain a trained compressibility evaluation model. The historical data sets include historical comprehensive compressibility evaluation indexes and the compressibility evaluations of the corresponding shale reservoirs. Among them, the compressibility evaluation in the historical data sets is specifically the compressibility grade of the shale reservoir, which is determined by the expert evaluation method; take the generated comprehensive compressibility evaluation index of the shale reservoir to be evaluated as the input, and the model outputs the compressibility evaluation of the shale reservoir to be evaluated. Among them, the compressibility evaluation output by the model is the compressibility grade of the shale reservoir to be evaluated;

[0124] Among them, the classification standard of the compressibility grade is:

[0125] When QS ≤ 0.5QY, it is judged that the compressibility grade of the shale reservoir to be evaluated is low compressibility, the shale reservoir has poor compressibility, and the mining difficulty is high;

[0126] When \(0.5QY \lt QS \leq QY\), it is determined that the compressibility grade of the shale reservoir to be evaluated is medium compressibility. The shale reservoir has good compressibility and the exploitation is relatively smooth;

[0127] When \(QS \gt QY\), it is determined that the compressibility grade of the shale reservoir to be evaluated is high compressibility and the exploitation potential is large;

[0128] Among them, \(QS\) is the comprehensive compressibility evaluation index, and \(QY\) is the compressibility judgment threshold;

[0129] The specific logic for obtaining \(QY\) is as follows: Obtain the geometric parameters and physical property parameters of a large number of shale reservoirs. Based on the expert evaluation method, on the basis of existing geological research and rock physics, combined with the on-site experience and judgment of experts, summarize different groups of geometric parameters and physical property parameters to obtain the corresponding compressibility grades. Then establish a mapping between multiple groups of compressibility grades and \(QS\) calculated based on geometric parameters and physical property parameters. Visualize the mapping relationship between \(QS\) and the compressibility grade based on the histogram method, and analyze the data distribution of \(QS\) to determine the threshold \(QY\).

[0130] In step 4, collect the environmental parameters of the shale reservoir to be evaluated, calculate the environmental impact index, and use this index to correct the initial compressibility evaluation index. This method makes the evaluation result closer to the actual situation by considering the influence of environmental factors on the reservoir compressibility. Environmental parameters can significantly change the physical properties of the shale reservoir, so dynamic correction of them can improve the accuracy and reliability of the evaluation.

[0131] Different from the traditional evaluation method that only depends on physical property parameters, the introduction of step 4 makes environmental factors an important part of the evaluation process. This comprehensive consideration method can better reflect the actual performance of the shale reservoir under different environmental conditions, thereby improving the adaptability of the evaluation model. This innovation makes the evaluation result more practical and can provide a more accurate basis for subsequent development decisions. Through the implementation of step 4, the overall scheme can be further corrected and improved on the basis of the initial compressibility evaluation to form a comprehensive compressibility evaluation index. This comprehensive index can not only enhance the prediction ability of the model, but also provide more accurate input data for the subsequent compressibility evaluation model based on support vector machines, making the final prediction result more reliable. At the same time, the introduction of environmental factors makes the overall scheme more flexible in practical applications, more adaptable to the needs under different geological and environmental conditions, and promotes the scientific development of shale reservoirs.

[0132] Please refer to Figure 2 , a shale reservoir compressibility evaluation system based on support vector machine technology, including:

[0133] A parameter acquisition module is used to acquire the geometric parameters and physical property parameters of the shale reservoir to be evaluated. The geometric parameters include porosity and permeability, and the physical property parameters include rock density, thermal conductivity, water saturation, and stiffness index. An interaction term is added to the rock density and water saturation, and the first coupling coefficient is calculated.

[0134] A data processing module is used to calculate the flow capacity evaluation coefficient according to the dimensionless porosity and permeability, and calculate the thermal energy storage evaluation coefficient according to the dimensionless first coupling coefficient, thermal conductivity.

[0135] An index generation module is used to combine the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient to generate an initial compressibility evaluation index. The weights of the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient in the calculation of the initial compressibility evaluation index are determined based on the analytic hierarchy process.

[0136] A correction module is used to collect the environmental parameters of the shale reservoir to be evaluated, calculate the environmental impact index based on the environmental parameters, correct the initial compressibility evaluation index according to the obtained environmental impact index to obtain the comprehensive compressibility evaluation index, and use the generated comprehensive compressibility evaluation index as the input to construct a compressibility evaluation model based on support vector machine technology to predict the compressibility of the shale reservoir to be evaluated. The environmental parameters include the environmental temperature and formation pressure data of the location where the shale reservoir to be evaluated is located.

[0137] The above formulas are all dimensionless and take their numerical values for calculation. The formulas are obtained by collecting a large amount of data for software simulation to get a formula closest to the real situation. The preset parameters in the formulas are set by those skilled in the art according to the actual situation.

[0138] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. Those skilled in the art can realize that the units and algorithm steps of each example described in combination with the embodiments disclosed herein can be implemented by electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are executed by hardware or software methods depends on the specific application and design constraints of the technical solution.

[0139] The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units. They may be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0140] As described above, it is only the specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art within the technical scope disclosed by the present application can easily think of changes or substitutions, which should all be covered within the protection scope of the present application.

Claims

1. A method for evaluating the compressibility of shale reservoirs based on support vector machine technology, characterized in that, The specific steps include: Step 1: Collect the geometric parameters and physical property parameters of the shale reservoir to be evaluated. The geometric parameters include porosity and permeability, and the physical property parameters include rock density, thermal conductivity, water saturation, and stiffness index. An interaction term is added to the rock density and water saturation, and the first coupling coefficient is calculated. Step 2: Calculate the flow capacity evaluation coefficient based on the dimensionless porosity and permeability, and calculate the thermal energy storage evaluation coefficient based on the dimensionless first coupling coefficient, thermal conductivity. Step 3: Combine the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient to generate an initial compressibility evaluation index. The weights of the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient in the calculation of the initial compressibility evaluation index are determined based on the analytic hierarchy process. Step 4: Collect the environmental parameters of the shale reservoir to be evaluated, calculate the environmental impact index based on the environmental parameters, correct the initial compressibility evaluation index according to the obtained environmental impact index to obtain the comprehensive compressibility evaluation index. Use the generated comprehensive compressibility evaluation index as the input to construct a compressibility evaluation model based on support vector machine technology to predict the compressibility of the shale reservoir to be evaluated. The environmental parameters include the environmental temperature and formation pressure data at the location of the shale reservoir to be evaluated.

2. The method for evaluating the compressibility of a shale reservoir based on support vector machine technology according to claim 1, wherein: Take three equal - volume samples from the shale reservoir to be evaluated, calculate the porosity and water saturation respectively, and take the average of the three results as the porosity and water saturation of the shale reservoir to be evaluated. Denote the porosity of the shale reservoir to be evaluated as Denote the water saturation of the shale reservoir to be evaluated as S w ; The formula for calculating porosity is: where φ is the porosity, V p is the pore volume, and V t is the total volume of the sample; The formula for calculating water saturation is: where S w is the water saturation, V p is the pore volume, and V t is the volume of water in the pores; The specific logic for obtaining the permeability of the shale reservoir to be evaluated is as follows: Extract a core sample from the shale reservoir, cut the core sample into a cylinder with a diameter of a and a length of b, and ensure that the surface is flat. Immerse the core sample in water to ensure that the pores are completely saturated and avoid the influence of gas on the test results. Use a permeameter to test the saturated core sample: Apply a head difference of ΔP at both ends of the core sample, record the flow rate change, and calculate the permeability of the core sample according to the following formula, and use this as the permeability of the shale reservoir to be evaluated: where k is the permeability, Q is the flow rate, A is the cross-sectional area of the core sample, ΔP is the head difference, L is the length of the core sample, and μ is the viscosity of water.

3. The method for evaluating the compressibility of shale reservoirs based on support vector machine technology according to claim 1, characterized in that: The specific logic for obtaining the stiffness index is as follows: Obtain the elastic modulus and Poisson's ratio of the shale reservoir to be evaluated, and calculate the stiffness index according to the dimensionless elastic modulus and Poisson's ratio. The formula is as follows: In the formula, STI is the stiffness index, E is the elastic modulus, and σ is Poisson's ratio; An interaction term is added to the dimensionless rock density and water saturation, and the first coupling coefficient is calculated. The formula is as follows: In the formula, C1 is the first coupling coefficient, ρ is the rock density, η is the water saturation, and η ref is the reference threshold of the water saturation.

4. The method for evaluating the compressibility of shale reservoirs based on support vector machine technology according to claim 1, characterized in that: The formula for calculating the flow capacity evaluation coefficient based on the dimensionless porosity and permeability is: where A is the flow capacity evaluation coefficient, is the porosity, k is the permeability, and e is the natural constant; The formula for calculating the thermal energy storage evaluation coefficient based on the dimensionless first coupling coefficient and thermal conductivity is: In the formula, B is the thermal energy storage evaluation coefficient, C1 is the first coupling coefficient, Λ is the thermal conductivity, and e is the natural constant.

5. The method for evaluating the compressibility of shale reservoirs based on support vector machine technology according to claim 1, wherein: Combine the stiffness index, flow capacity evaluation coefficient, and thermal energy storage evaluation coefficient to generate an initial compressibility evaluation index: Wherein, X is the initial compressibility evaluation index, STI is the stiffness index, A is the flowability evaluation coefficient, B is the thermal energy storage evaluation coefficient, and B ref is the reference threshold of the thermal energy storage evaluation coefficient, and ω1, ω2, and ω3 are weights determined according to the analytic hierarchy process.

6. The method for evaluating the compressibility of shale reservoirs based on support vector machine technology according to claim 5, wherein: Determine the weights of the stiffness index, flowability evaluation coefficient, and thermal energy storage evaluation coefficient in the calculation of the initial compressibility evaluation index according to the analytic hierarchy process. The specific logic is as follows: Mark the three indicators of the stiffness index, flowability evaluation coefficient, and thermal energy storage evaluation coefficient, and determine the numerical values of the relative importance between each pair through the nine-scale method to construct a judgment matrix. Among them, the stiffness index is marked as 1, the flowability evaluation coefficient is marked as 2, and the thermal energy storage evaluation coefficient is marked as 3. The constructed judgment matrix is: Among them, both f and v represent the indices of exponents, and f ∈ [1, 3], v ∈ [1, 3], b fv represents the importance of the exponent with index f relative to the exponent with index v. The importance is measured using a 1-9 scale method, and b fv The larger the value, the greater the importance of the exponent with index f compared to the exponent with index v, and b ff = 1, Divide each element value in the judgment matrix by the sum of its column to obtain a normalized judgment matrix. Calculate the mean value of each row element value in the normalized judgment matrix, and take the mean value of the first row element value as the weight of the stiffness index, the mean value of the second row element value as the weight of the flowability evaluation coefficient, and the mean value of the third row element value as the weight of the thermal energy storage evaluation coefficient. Under the constraint that the sum of the scaled values is equal to 1, scale the three weights proportionally, and take the scaled weights as the proportional coefficients of the corresponding indices.

7. The method for evaluating the compressibility of shale reservoirs based on support vector machine technology according to claim 1, characterized in that: Collect the environmental temperature and formation pressure data at the location of the shale reservoir to be evaluated, and calculate the environmental impact index based on the environmental parameters: Wherein, ENV is the environmental impact index, T is the environmental temperature, and T ref is the reference threshold of the environmental temperature, G is the formation pressure, and G ref is the reference threshold of the formation pressure; Correct the initial compressibility evaluation index according to the obtained environmental impact index to obtain the comprehensive compressibility evaluation index: QS = X * (1 + μ * ENV) In the formula, QS is the comprehensive compressibility evaluation index, X is the initial compressibility evaluation index, ENV is the environmental impact index, and μ is the weight coefficient of the environmental impact index, which is used to adjust the influence degree of the environmental impact index on the comprehensive compressibility evaluation index and is set according to the actual situation.

8. The method for evaluating the compressibility of a shale reservoir based on the support vector machine technology according to claim 7, characterized in that: Construct a compressibility evaluation model based on the support vector machine technology, and use a number of historical data sets to train the model to obtain a trained compressibility evaluation model. The historical data sets include historical comprehensive compressibility evaluation indices and the compressibility evaluations of the corresponding shale reservoirs. Among them, the compressibility evaluations in the historical data sets are specifically the compressibility grades of the shale reservoirs, which are determined by the expert evaluation method; use the generated comprehensive compressibility evaluation index of the shale reservoir to be evaluated as the input, and the model outputs the compressibility evaluation of the shale reservoir to be evaluated. Among them, the compressibility evaluation output by the model is the compressibility grade of the shale reservoir to be evaluated; Among them, the classification standard of the compressibility grade is: When QS ≤ 0.5QY, it is judged that the compressibility grade of the shale reservoir to be evaluated is low compressibility, the compressibility of the shale reservoir is poor, and the mining difficulty is high; When 0.5QY < QS ≤ QY, it is judged that the compressibility grade of the shale reservoir to be evaluated is medium compressibility, the compressibility of the shale reservoir is good, and the mining is relatively smooth; When QS > QY, it is judged that the compressibility grade of the shale reservoir to be evaluated is high compressibility, and the mining potential is large; Among them, QS is the comprehensive compressibility evaluation index, and QY is the compressibility judgment threshold.

9. A shale reservoir compressibility evaluation system based on support vector machine technology, characterized in that: The shale reservoir compressibility evaluation system based on support vector machine technology is used to execute the shale reservoir compressibility evaluation method according to any one of claims 1-8, including: A parameter acquisition module, configured to acquire geometric parameters and physical property parameters of the shale reservoir to be evaluated. The geometric parameters include porosity and permeability, and the physical property parameters include rock density, thermal conductivity, water saturation, and stiffness index. An interaction term is processed for the rock density and water saturation to calculate a first coupling coefficient; A data processing module, configured to calculate a flow capacity evaluation coefficient according to the dimensionless porosity and permeability, and calculate a heat storage capacity evaluation coefficient according to the dimensionless first coupling coefficient, thermal conductivity; An index generation module, configured to combine the stiffness index, the flow capacity evaluation coefficient, and the heat storage capacity evaluation coefficient to generate an initial compressibility evaluation index. The weights of the stiffness index, the flow capacity evaluation coefficient, and the heat storage capacity evaluation coefficient in the calculation of the initial compressibility evaluation index are determined based on the analytic hierarchy process; A correction module, configured to acquire environmental parameters of the shale reservoir to be evaluated, calculate an environmental impact index based on the environmental parameters, correct the initial compressibility evaluation index according to the obtained environmental impact index to obtain a comprehensive compressibility evaluation index, use the generated comprehensive compressibility evaluation index as an input to construct a compressibility evaluation model based on support vector machine technology for predicting the compressibility of the shale reservoir to be evaluated. The environmental parameters include environmental temperature and formation pressure data at the location where the shale reservoir to be evaluated is located.

Images