A method for evaluating compact reservoir compressibility at well scale

Abstract

A tight reservoir compressibility evaluation method at logging scale belongs to the technical field of fracturing in downhole operation in the petroleum industry, and comprises the following steps: S1, gamma, density, acoustic data are introduced into the ground stress profile software to obtain the ground stress, Young's modulus and Poisson's ratio parameters, and the horizontal stress difference coefficient, stress quality coefficient and brittleness index factor are respectively calculated according to the formula; S2, the minimum horizontal principal stress is taken as the main control factor of compressibility, and the partial correlation coefficients of the horizontal stress difference coefficient, stress quality coefficient, brittleness index, macroscopic crack factor and the main control factor are measured under the condition of controlling other factors; S3, the compressibility coefficient is obtained by multiplying the values of each influencing factor and the corresponding weight. Through post-pressure test analysis, it is verified that the tight reservoir compressibility evaluation method at logging scale is reliable and simple, and can effectively guide the single well fracturing design.

Description

Technical Field

[0001] This invention belongs to the field of downhole fracturing technology in the petroleum industry, specifically relating to a method for evaluating the compressibility of tight reservoirs at the logging scale. Background Technology

[0002] With conventional oil and gas resources dwindling, unconventional oil and gas reservoirs are gradually becoming the focus of exploration and development. Tight oil and gas reservoirs are rich in resources and have enormous development potential. Tight oil and gas reservoirs are characterized by multi-layered formations, diverse lithologies, well-developed natural fractures, and low matrix permeability, making volumetric fracturing a key technology for enhancing production in these reservoirs. If a reliable and accurate compressibility assessment of the reservoir can be conducted in the early stages of fracturing, and suitable target intervals for fracturing can be selected to achieve ideal stimulation effects, improve single-well productivity and economic benefits, then the compressibility assessment stage is particularly important.

[0003] Literature review revealed a similar technique for quantitatively evaluating the fracturability of shale oil reservoirs (publication number CN113283108A). This technique focuses on applying indoor mechanical testing results and acoustic emission Kaiser effect experimental data. It calculates the normalized fracturability index of shale oil reservoirs using brittleness index, horizontal stress difference index, and fracture toughness index. Then, it combines the normalized fracturability index with the minimum horizontal principal stress to establish a quantitative evaluation equation for shale oil reservoir fracturability. The fracturability of shale oil reservoirs is evaluated based on the normalized fracturability index and the fracturability value. However, this technique relies heavily on indoor rock mechanics testing and related experimental data. In actual oilfield production, only a few exploration and appraisal wells can obtain relevant indoor experimental data, making it ineffective for guiding a large number of development wells. Therefore, this invention proposes a logging-scale method for evaluating the fracturability of tight oil and gas reservoirs. Based on conventional logging data, it uses three major factors influencing fracture propagation paths—in-situ stress, stress quality coefficient, and macroscopic fractures—as quantitative evaluation parameters for reservoir fracturability. By introducing partial correlation analysis, the minimum horizontal principal stress is selected as the main controlling factor of compressibility. Under the condition of controlling or eliminating the influence of other factors, the partial correlation coefficients of factors such as horizontal stress difference coefficient, stress quality coefficient, brittleness index, and macroscopic fractures with the main controlling factor are measured. The weight values ​​of each factor are determined, and after standardization, a compressibility coefficient is formed. This method can simply and effectively evaluate the compressibility of a single well, providing a theoretical basis for the design and optimization of fracturing schemes in tight reservoirs. It is novel and provides a theoretical basis for the design and optimization of fracturing schemes in tight reservoirs. Summary of the Invention

[0004] In response to the above-mentioned shortcomings,

[0005] To address the aforementioned problems, this invention proposes a method for evaluating the compressibility of tight reservoirs at the well logging scale, comprising the following steps:

[0006] S1. Import the natural gamma, density, and acoustic data into the geostress profile software to obtain the geostress, Young's modulus, and Poisson's ratio parameters. Calculate the horizontal stress difference coefficient, stress quality coefficient, and brittleness index factor according to the formula.

[0007] S2. Using the partial correlation coefficient calculation formula, the minimum horizontal principal stress is taken as the main control factor of compressibility. Under the condition of controlling the influence of other factors, the partial correlation coefficient between the horizontal stress difference coefficient, stress quality coefficient, brittleness index, macroscopic crack factor and the main control factor is measured.

[0008] S3. Multiply each influencing factor value by its corresponding weight to obtain the compressibility coefficient.

[0009] The beneficial effects of this invention are as follows: The proposed method for evaluating the compressibility of tight reservoirs at the logging scale overcomes the limitations of existing technologies that rely on indoor mechanical experimental data, and can be applied on a large scale in a large number of development wells. Based on conventional logging data from a single well, it uses three major factors affecting fracture propagation paths—in-situ stress, stress quality coefficient, and macroscopic fractures—as quantitative evaluation parameters for reservoir compressibility. Partial correlation analysis is introduced, selecting the minimum horizontal principal stress as the main controlling factor for compressibility. Under the condition of controlling or eliminating the influence of other factors, the partial correlation coefficients between factors such as the horizontal stress difference coefficient, stress quality coefficient, brittleness index, and macroscopic fractures and the main controlling factor are measured to determine the weight values ​​of each factor. After standardization, a compressibility coefficient is formed, providing a theoretical basis for the design and optimization of tight reservoir fracturing schemes.

[0010] The method for evaluating the compressibility of tight reservoirs at the logging scale was effectively applied in five wells in the field. Post-fracturing testing and analysis verified the reliability and simplicity of this method, demonstrating its ability to effectively guide fracturing design for single wells. Attached Figure Description

[0011] Figure 1 This invention provides a diagram showing the relationship between three major factors in the quantitative evaluation of compressibility: geostress, stress quality coefficient, and macroscopic cracks.

[0012] Figure 2 This invention provides well logging interpretation diagrams and geostress profiles for the Deshen 88 well, an example of a quantitative evaluation of compressibility.

[0013] Figure 3 The key parameters such as geostress, Young's modulus, and Poisson's ratio obtained from the geostress profile of the Deshen 88 well, an example of the quantitative evaluation of compressibility provided by this invention, are as follows:

[0014] Figure 4 The stress difference coefficient, stress quality coefficient, and matrix brittleness diagram of the Deshen 88 well, which is an example of quantitative evaluation of compressibility provided by this invention, are obtained according to the corresponding formula.

[0015] Figure 5 This invention provides an example of quantitative evaluation of compressibility in the Deshen 88 well, which uses partial correlation analysis to determine the weights of various parameters.

[0016] Figure 6 This is a compressibility coefficient diagram obtained by multiplying the values ​​of each influencing factor and their corresponding weights in the Deshen 88 well, an example of quantitative evaluation of compressibility provided by this invention.

[0017] Figure 7 This is a graph of the compressibility quantitative evaluation standard parameters provided by the present invention;

[0018] Figure 8 The following is a graph showing the post-compressibility G-function analysis results of the Deshen 88 well, an example of quantitative evaluation of compressibility provided by this invention. Detailed Implementation

[0019] A method for evaluating the compressibility of tight reservoirs at the well logging scale is proposed, such as... Figure 1-8 As shown, it includes the following steps:

[0020] S1. Import the natural gamma, density, and acoustic data into the geostress profile software to obtain the geostress, Young's modulus, and Poisson's ratio parameters. Calculate the horizontal stress difference coefficient, stress quality coefficient, and brittleness index factor according to the formula.

[0021] S2. Using the partial correlation coefficient calculation formula, the minimum horizontal principal stress is taken as the main control factor of compressibility. Under the condition of controlling the influence of other factors, the partial correlation coefficient between the horizontal stress difference coefficient, stress quality coefficient, brittleness index, macroscopic crack factor and the main control factor is measured.

[0022] S3. Multiply each influencing factor value by its corresponding weight to obtain the compressibility coefficient.

[0023] The calculation in step S1 is as follows:

[0024] S11, The formula for calculating the horizontal stress difference coefficient is as follows:

[0025]

[0026] In the formula: K h : Horizontal stress difference coefficient; σ H : Maximum horizontal principal stress, MPa; σ h : Horizontal minimum principal stress, MPa;

[0027] S12, The formula for calculating the stress quality coefficient is as follows:

[0028]

[0029] In the formula: R: stress quality coefficient, 0 < R < 1; σ1, σ2 and σ3 are the triaxial principal stresses, σ1 > σ2 > σ3;

[0030] S13, the brittleness index is calculated by combining the brittleness modulus and the fracture density: the specific calculation formula is as follows:

[0031] S131, Formula for calculating modulus brittleness:

[0032]

[0033] Where: BI: modulus brittleness; E: Young's modulus, GPa; E max Maximum Young's modulus, GPa; E min υ: Minimum Young's modulus, GPa; υ: Poisson's ratio; υ max : Maximum Poisson's ratio; υ min Minimum Poisson's ratio;

[0034] S132, Formula for calculating fracture density:

[0035]

[0036]

[0037]

[0038] Where: K: bulk modulus; υ: Poisson's ratio; ρ c : Fracture density; K0: Maximum value in K; υ0: Maximum value in υ; v p Longitudinal wave velocity; v s Transverse wave velocity;

[0039] S133, Normalization of modulus brittleness and crack density:

[0040]

[0041]

[0042] S134, Brittleness Index:

[0043]

[0044] In the formula: B Brit : Brittleness index; F ck Normalized modulus brittleness; F ρc Normalized fracture density.

[0045] The calculation in step S2 is as follows:

[0046] Macroscopic fractures are obtained by imaging logging (FMI) or seismic prediction to determine fracture density. For formations without fracture development, a fracture density of 0.1 is assigned.

[0047] Then, applying the partial correlation coefficient calculation formula below, taking the minimum horizontal principal stress as the main controlling factor of compressibility, and controlling for the influence of other factors, the horizontal stress difference coefficient and stress are measured.

[0048]

[0049] Partial correlation coefficients between factors such as quality coefficient, brittleness index, and macroscopic cracks and the main controlling factors;

[0050] S21. Calculate the zero-order partial correlation coefficient r between variables x1 and x2. 12 :

[0051] In the formula r 12 x1 is the correlation coefficient between x2 and x1; N is the number of variable values, i.e., the sample size; x 1i Let the i-th set of data for variable x1 be the value; x is the average of all values ​​taken by variable x1; 2i Let x2 take the value of the i-th data set; The average of all values ​​taken by variable x2;

[0052] S22. With variable x3 fixed, calculate the first-order partial correlation coefficient r between x1 and x2. 12,3 :

[0053]

[0054] In the formula: r 12,3 The partial correlation coefficient between x1 and x2 after removing the influence of x3; r 12 r is the correlation coefficient between x1 and x2. 13 r is the correlation coefficient between x1 and x3; 23 r23 is the correlation coefficient between x2 and x3;

[0055] S23. With fixed variables x3 and x4, calculate the second-order partial correlation coefficient r between x1 and x2. 12,34 :

[0056]

[0057] In the formula: r 12,34 The partial correlation coefficient between x1 and x2 after removing the influence of x3 and x4; r 14,3 The partial correlation coefficient between x1 and x4 after removing the influence of x3; x 24，3 The partial correlation coefficient between x2 and x4 after removing the influence of x3;

[0058] Then, based on the partial correlation coefficients between the horizontal stress difference coefficient, stress quality coefficient, brittleness index, macroscopic cracks, and other factors calculated above and the minimum principal stress, the weight values ​​of each factor are determined.

[0059] Weighting formula:

[0060]

[0061] In the formula: α i : Weight values ​​of each factor; r i represents the partial correlation coefficients between each factor and the minimum principal stress.

[0062] The calculation in step S3 is as follows:

[0063] Finally, multiplying each influencing factor value by its corresponding weight yields the compressibility coefficient.

[0064] The formula for compressibility coefficient is as follows:

[0065] F frac =∑α i ·F i

[0066] In the formula: F frac : Compressibility coefficient, α i : Weight values ​​of each factor, F i : The corresponding factor values, where the horizontal stress difference coefficient is taken as (1-F) i ).

[0067] Taking the Deshen 88 well as an example, this well is a risk assessment well in the western peripheral area of ​​the Deshen 80 well area. The target layers are the II and IV sand groups, with a working layer thickness of 79 meters. The II sand group is the main layer developed in the early stage of the Deshen 80 well area, with a high-quality layer thickness of 14.2 meters. The IV sand group is a potential layer, with a high-quality layer thickness of 29.6 meters. Before fracturing, the compressibility assessment of the tight reservoir at the well logging scale was carried out, which effectively guided the design of the fracturing scheme.

[0068] The specific implementation process is as follows:

[0069] The first step is to import the logging data (natural gamma, density, sonic parameters, etc.) from the Deshen 88 well into the geostress profiling software to obtain key parameters such as geostress, Young's modulus, and Poisson's ratio; for example... Figure 2 As shown, by importing key parameters such as natural gamma, density, and sonic waves from well logging data into geostress profiling software, key parameters such as geostress, Young's modulus, and Poisson's ratio can be obtained.

[0070] The second step involves substituting the obtained key parameters such as geostress, Young's modulus, and Poisson's ratio into the corresponding calculation formulas to obtain factors such as the horizontal stress difference coefficient, stress quality coefficient, and brittleness index.

[0071] The third step involves using the partial correlation coefficient calculation formula to take the minimum horizontal principal stress as the main control factor of compressibility. Under the condition of controlling the influence of other factors, the partial correlation coefficients between factors such as horizontal stress difference coefficient, stress quality coefficient, brittleness index, and macroscopic cracks and the main control factor are obtained.

[0072] Finally, multiplying the values ​​of each influencing factor by their corresponding weights yields the compressibility coefficient, which helps to select the appropriate target fracturing section and effectively guide the design of fracturing schemes.

[0073] like Figure 7 As shown, the Deshen 88 well has four fracturing layers. The first and second layers have low compressibility coefficients (0.20-0.25), the third layer has medium coefficients (0.27-0.30), and the fourth layer has the best coefficients (0.31-0.6).

[0074] like Figure 8 As shown, the post-fracturing G-function analysis results of the Deshen 88 well, analyzed using Stimplan software, indicate that the first and second fracture sections are relatively simple, the second section's two-stage three-section fracture network fracturing is better than the first layer, the third layer's fractures are more complex, and the fourth layer's fractures are the most complex, consistent with the pre-fracturing compressibility analysis results.

[0075] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A method for evaluating the compressibility of tight reservoirs at the logging scale, characterized in that, Includes the following steps: S1. Import the natural gamma, density, and acoustic data into the geostress profile software to obtain the geostress, Young's modulus, and Poisson's ratio parameters. Calculate the horizontal stress difference coefficient, stress quality coefficient, and brittleness index factor according to the formula. S2. Using the partial correlation coefficient calculation formula, the minimum horizontal principal stress is taken as the main control factor of compressibility. Under the condition of controlling the influence of other factors, the partial correlation coefficient between the horizontal stress difference coefficient, stress quality coefficient, brittleness index, macroscopic crack factor and the main control factor is measured. The calculation in step S2 is as follows: Macroscopic fracture density is obtained using imaging logging (FMI) or seismic prediction. For formations without fracture development, the fracture density is assigned as 0.

1. Then, using the partial correlation coefficient calculation formula below, the minimum horizontal principal stress is taken as the main controlling factor of compressibility. Under the condition of controlling the influence of other factors, the partial correlation coefficient between the horizontal stress difference coefficient, stress quality coefficient, brittleness index, macroscopic crack factor and the main controlling factor is measured. S21. Calculate variables and zeroth order partial correlation coefficient : In the formula for and The correlation coefficient; This refers to the number of values ​​a variable can take, i.e., the sample size. For variables No. Group data values; For variables The average of all possible values; For variables No. Group data values; For variables The average of all possible values; S22, Fixed Variables ,calculate and First-order partial correlation coefficient : In the formula: To eliminate After the impact, and The partial correlation coefficient; for and The correlation coefficient; for and Correlation coefficient; r23 is and The correlation coefficient; S23, Fixed Variables and ,calculate and Second-order partial correlation coefficient : In the formula: To eliminate and After the impact, and The partial correlation coefficient; To eliminate After the impact, and The partial correlation coefficient; To eliminate After the impact, and The partial correlation coefficient; Then, based on the horizontal stress difference coefficient, stress quality coefficient, brittleness index, and partial correlation coefficient between macroscopic crack factors and minimum principal stress obtained above, determine the weight values ​​of each factor. Weighting formula: In the formula: Weight values ​​for each factor; represents the partial correlation coefficients between each factor and the minimum principal stress; S3. Multiply each influencing factor value by its corresponding weight to obtain the compressibility coefficient.

2. The method for evaluating the compressibility of tight reservoirs at the logging scale according to claim 1, characterized in that, The calculation in step S1 is as follows: S11, The formula for calculating the horizontal stress difference coefficient is as follows: In the formula: : Coefficient of difference in horizontal stress; : Maximum horizontal principal stress, in MPa; : Minimum horizontal principal stress, in MPa; S12, The formula for calculating the stress quality coefficient is as follows: In the formula: Stress quality coefficient ; , and It is a triaxial principal stress. ; S13. The brittleness index is calculated by combining the brittleness modulus and the crack density.

3. The method for evaluating the compressibility of tight reservoirs at the logging scale according to claim 2, characterized in that, The calculation in step S13 is as follows: S131, Formula for calculating modulus brittleness: In the formula: Modular brittleness; Young's modulus, measured in gigabytes per second (GPa). Maximum Young's modulus, in GPa; : Minimum Young's modulus, in GPa; Poisson's ratio; Maximum Poisson's ratio; Minimum Poisson's ratio.

4. The method for evaluating the compressibility of tight reservoirs at the logging scale according to claim 3, characterized in that, The calculation in step S13 is as follows: S132, Formula for calculating fracture density: In the formula: Bulk modulus; Poisson's ratio; : Crack density; : The maximum value in the middle; : The maximum value in the middle; Longitudinal wave velocity; : Transverse wave velocity.

5. The method for evaluating the compressibility of tight reservoirs at the logging scale according to claim 4, characterized in that, The calculation in step S13 is as follows: S133, Normalization of modulus brittleness and crack density: In the formula: Normalized modulus brittleness; Normalized fracture density.

6. The method for evaluating the compressibility of tight reservoirs at the logging scale according to claim 5, characterized in that, The calculation in step S13 is as follows: S134, Brittleness Index: In the formula: : Brittleness index; Normalized modulus brittleness; Normalized fracture density.

7. The method for evaluating the compressibility of tight reservoirs at the logging scale according to claim 1, characterized in that, The calculation in step S3 is as follows: Finally, multiplying each influencing factor value by its corresponding weight yields the compressibility coefficient. The formula for compressibility coefficient is as follows: In the formula: Compressibility coefficient Weight values ​​for each factor : Corresponding factor values, where the horizontal stress difference coefficient is taken as 1- .

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