Method and device for predicting brittleness of shale oil reservoir based on stress-strain curve

Abstract

The present disclosure relates to a method and device for predicting the brittleness of shale oil reservoirs based on stress-strain curves, including: obtaining the stress-strain curves, logging data, and seismic data of typical shale samples in the study area; determining the correlation coefficients between Young's modulus and Poisson's ratio and the brittleness evaluation index according to the stress-strain curves; determining the weight factors of Young's modulus and Poisson's ratio according to the correlation coefficients between Young's modulus and Poisson's ratio and the brittleness evaluation index, and determining the brittleness prediction model of the shale reservoir according to the weight factors; determining the parameters of Young's modulus and Poisson's ratio through prestack seismic inversion according to the logging data and seismic data; and determining the brittleness of the shale in the study area according to the parameters of Young's modulus and Poisson's ratio and the brittleness prediction model, thereby completing the prediction of the brittleness of the shale oil reservoir in the study area. This is to solve the problems in the prediction of the brittleness of conventional shale oil reservoirs, such as high experimental costs, low measurement efficiency, unclear proportion of weight coefficients, etc., which lead to inaccurate prediction results.

Description

Technical Field

[0001] The present disclosure relates to the technical field of oil exploration and development, and particularly to a method and device for predicting the brittleness of shale oil reservoirs based on stress-strain curves. Background Art

[0002] As an important resource in the energy industry, the exploration and development of shale oil and gas have always received extensive attention from scholars at home and abroad. Due to the characteristics of low porosity, low permeability, and poor fluidity of shale reservoirs, it is difficult to obtain industrial production capacity under natural conditions. The key to realizing large-scale reserve increase and production construction of such reservoirs is hydraulic fracturing to create fractures. In addition to the degree of natural fracture development and the magnitude of the horizontal stress difference, the brittleness of the reservoir is also a main controlling factor that cannot be ignored. Practice has proved that the higher the brittleness of the formation, the easier it is to be fully transformed during artificial fracturing, thereby forming a complex fracture network and achieving the purpose of increasing production.

[0003] At present, there are many brittleness evaluation methods at home and abroad, mainly including those based on strength, hardness, firmness, composition analysis, stress-strain curves, elastic parameters, etc. Different methods have different definitions of brittleness.

[0004] The brittleness evaluation based on strength mainly uses the basic strength parameters of rocks to qualitatively reflect their brittleness characteristics. The defect is that it is not sensitive in quantitative evaluation; the brittleness evaluation based on stress-strain curves is mainly measured through uniaxial or triaxial experiments, and the brittleness is evaluated based on the stress-strain curve morphology during the whole process from loading to failure. This method is currently the most intuitive and effective method for evaluating the brittleness degree of rocks. The defect is that due to the heterogeneity of underground rocks and the high cost of coring, the efficiency of targeted experimental measurement is low; the brittleness evaluation based on mineral composition mainly uses the content of brittle minerals in rocks to obtain the brittleness index. The defect is that it does not consider the difference in the contribution of different minerals to brittleness and the influence of factors such as pores, particle size, and cementation degree on the brittleness of rocks; the brittleness evaluation based on elastic parameters mainly uses Young's modulus and Poisson's ratio to characterize the relative brittleness of rocks. The higher Young's modulus and the lower Poisson's ratio, the greater the brittleness. This method has strong practicability. The defect is that in the traditional brittleness evaluation model, it is considered that the weight coefficients of Young's modulus and Poisson's ratio are the same, and this understanding has no theoretical basis. Summary of the Invention

[0005] The present disclosure provides a method and device for predicting the brittleness of shale oil reservoirs based on stress-strain curves to solve the problems of high experimental cost, low measurement efficiency, unclear weight coefficient ratio, etc. in the previous prediction of the brittleness of shale oil reservoirs, resulting in inaccurate prediction results.

[0006] According to one aspect of the present disclosure, there is provided a method for predicting the brittleness of shale oil reservoirs based on stress-strain curves, including:

[0007] Obtain the stress-strain curves, logging data, and seismic data of typical shale samples in the study area;

[0008] According to the stress-strain curves, determine the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index;

[0009] According to the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index, determine the weight factors of Young's modulus and Poisson's ratio. According to the weight factors, determine the brittleness prediction model for shale reservoirs;

[0010] According to the logging data and seismic data, through prestack seismic inversion, determine the parameters of Young's modulus and Poisson's ratio;

[0011] According to the parameters of Young's modulus and Poisson's ratio and the brittleness prediction model, determine the brittleness of shale in the study area and complete the brittleness prediction of shale oil reservoirs in the study area.

[0012] Preferably, the method for determining the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index according to the stress-strain curves includes:

[0013] According to the stress-strain curves, respectively determine Young's modulus, Poisson's ratio parameters, and the brittleness evaluation index considering the peak stress drop magnitude and stress drop rate;

[0014] According to the brittleness evaluation index and the parameters of Young's modulus and Poisson's ratio, respectively determine the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index.

[0015] Preferably, the method for determining the brittleness evaluation index considering the peak stress drop magnitude and stress drop rate according to the stress-strain curves includes:

[0016] According to the stress-strain curves, using the brittleness evaluation index calculation formula, determine the brittleness evaluation index considering the peak stress drop magnitude and stress drop rate;

[0017] Among them, the brittleness evaluation index calculation formula is:

[0018]

[0019]

[0020]

[0021] In the formula: σ A is the peak strength of the stress-strain curve, σ B is the residual strength of the stress-strain curve, |k AB| is the absolute value of the slope of the line connecting the peak strength and the residual strength, B1 is the magnitude of the post-peak stress drop, B2 is the rate of the post-peak stress drop, and B3 is the constructed brittleness evaluation index.

[0022] Preferably, the method for determining the Young's modulus and Poisson's ratio parameters according to the stress-strain curve includes:

[0023] On the axial compression and axial strain curve, select the strain curve corresponding to the predetermined compressive strength range for linear fitting to determine the Young's modulus regression formula, and determine the Young's modulus according to the Young's modulus regression formula;

[0024] On the radial strain and axial strain curve, select the strain curve corresponding to the predetermined compressive strength range for linear fitting to determine the Poisson's ratio regression formula, and determine the Poisson's ratio parameter according to the Poisson's ratio regression formula.

[0025] Preferably, the method for determining the correlation coefficients between the Young's modulus and Poisson's ratio and the brittleness evaluation index according to the brittleness evaluation index and the Young's modulus and Poisson's ratio parameters includes:

[0026] Conduct a statistical intersection analysis on the brittleness evaluation index and the Young's modulus to determine the correlation coefficient between the Young's modulus and the brittleness evaluation index;

[0027] Conduct a statistical intersection analysis on the brittleness evaluation index and the Poisson's ratio parameter to determine the correlation coefficient between the Poisson's ratio and the brittleness evaluation index.

[0028] Preferably, the method for determining the weight factors of the Young's modulus and Poisson's ratio according to the correlation coefficients between the Young's modulus and Poisson's ratio and the brittleness evaluation index includes:

[0029] According to the correlation coefficients between the Young's modulus and Poisson's ratio parameters and the brittleness evaluation index, use the weight factor calculation formula to determine the weight factors of the Young's modulus and Poisson's ratio;

[0030] Among them, the weight factor calculation formula includes:

[0031]

[0032] c + d = 1;

[0033] In the formula: a is the correlation coefficient between the Poisson's ratio and the brittleness evaluation index, b is the correlation coefficient between the Young's modulus and the brittleness evaluation index, c is the Poisson's ratio weight coefficient, and d is the Young's modulus weight coefficient.

[0034] Preferably, the method for determining the brittleness prediction model of the shale reservoir according to the weight factors includes:

[0035] According to the weight factors, use the brittleness prediction model formula to determine the brittleness prediction model of the shale reservoir;

[0036] Among them, the brittle prediction model formula includes:

[0037]

[0038]

[0039] B XZ = c × B ν + d × B E ;

[0040] In the formula: c is the Poisson's ratio weight coefficient, d is the Young's modulus weight coefficient, E is the Young's modulus, ν is the Poisson's ratio, E min 、E max 、ν min 、ν max are the minimum and maximum Young's moduli and the minimum and maximum Poisson's ratios respectively, and B E 、B ν 、B XZ are the normalized Young's modulus, Poisson's ratio brittleness index, and the shale reservoir brittle prediction index established based on the stress-strain curve respectively.

[0041] Preferably, the method for determining the Young's modulus and Poisson's ratio parameters according to well logging data and seismic data through prestack seismic inversion includes:

[0042] According to well logging data and seismic data, through prestack seismic inversion, determine the P-wave velocity, S-wave velocity, and density;

[0043] According to the P-wave velocity, S-wave velocity, and density, use their conversion formulas with the Young's modulus and Poisson's ratio to determine the Young's modulus and Poisson's ratio parameters.

[0044] According to one aspect of the present disclosure, a brittle prediction device for shale oil reservoirs based on stress-strain curves is provided, including:

[0045] An acquisition unit for acquiring the stress-strain curves, well logging data, and seismic data of typical shale samples in the study area;

[0046] A correlation coefficient determination unit for determining the correlation coefficients between the Young's modulus, Poisson's ratio, and the brittle evaluation index according to the stress-strain curve;

[0047] A prediction model determination unit for determining the weight factors of the Young's modulus and Poisson's ratio according to the correlation coefficients between the Young's modulus, Poisson's ratio, and the brittle evaluation index, and determining the brittle prediction model of the shale reservoir according to the weight factors;

[0048] The Young's modulus and Poisson's ratio determination unit is used to determine the Young's modulus and Poisson's ratio parameters through prestack seismic inversion according to the logging data and seismic data;

[0049] The brittleness prediction unit is used to determine the brittleness of the shale in the study area according to the Young's modulus and Poisson's ratio parameters and the brittleness prediction model, and complete the brittleness prediction of the shale oil reservoir in the study area.

[0050] The present invention has at least the following beneficial effects:

[0051] The present disclosure proposes a method and device for predicting the brittleness of a shale oil reservoir based on a stress-strain curve. By using the correlation coefficients between the Young's modulus and Poisson's ratio determined from the stress-strain curve and the brittleness evaluation index, the weight factors of the Young's modulus and Poisson's ratio can be determined; according to the weight factors, a more accurate brittleness prediction model can be established, so that a more accurate prediction result can be obtained, solving the problem that the proportion of the weight coefficient in the brittleness evaluation method based on elastic parameters is unclear, and making the brittleness prediction result of the shale reservoir more reasonable. Description of the Drawings

[0052] The drawings here are incorporated into the specification and form a part of this specification. These drawings show embodiments consistent with the present disclosure and are used together with the specification to illustrate the technical solutions of the present disclosure.

[0053] Figure 1 Show a flowchart of a method for predicting the brittleness of a shale oil reservoir based on a stress-strain curve according to an embodiment of the present disclosure;

[0054] Figure 2 Show an intersection diagram of the stress-strain curve during the process of loading a shale sample to failure in a triaxial compression test according to an embodiment of the present disclosure;

[0055] Figure 3 Show an intersection diagram of the axial compression and axial strain of a shale sample according to an embodiment of the present disclosure;

[0056] Figure 4 Show an intersection diagram of the radial strain and axial strain of a shale sample according to an embodiment of the present disclosure;

[0057] Figure 5 Show an intersection diagram of the Young's modulus and the brittleness evaluation index according to an embodiment of the present disclosure;

[0058] Figure 6 Show an intersection diagram of the Poisson's ratio and the brittleness evaluation index according to an embodiment of the present disclosure;

[0059] Figure 7 Show a seismic prediction profile of the brittleness index passing through Well A according to an embodiment of the present disclosure;

[0060] Figure 8Shows the histogram of the number of microseismic events in different fracturing sections of Well A according to an embodiment of the present disclosure. Detailed implementation manners

[0061] Various exemplary embodiments, features, and aspects of the present disclosure will be described in detail below with reference to the accompanying drawings. The same reference numerals in the drawings denote elements having the same or similar functions. Although various aspects of the embodiments are shown in the drawings, the drawings are not necessarily drawn to scale unless otherwise specified.

[0062] The term "exemplary" used herein means "serving as an example, embodiment, or illustration". Any embodiment described as "exemplary" herein is not necessarily to be construed as superior or better than other embodiments.

[0063] The term "and / or" in this article is merely a description of the associated relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can represent: A exists alone, A and B exist simultaneously, and B exists alone. In addition, the term "at least one" in this article means any one of a plurality or any combination of at least two of a plurality. For example, including at least one of A, B, and C can represent any one or more elements selected from the set composed of A, B, and C.

[0064] In addition, for a better illustration of the present disclosure, numerous specific details are given in the following detailed implementation manners. Those skilled in the art should understand that the present disclosure can also be implemented without some specific details. In some instances, methods, means, elements, and circuits well known to those skilled in the art are not described in detail so as to highlight the gist of the present disclosure.

[0065] Figure 1 Shows the flowchart of a method for predicting the brittleness of a shale oil reservoir based on a stress-strain curve according to an embodiment of the present disclosure; Figure 2 Shows the intersection diagram of the stress-strain curves during the loading process to failure of a triaxial compression test of a shale sample according to an embodiment of the present disclosure; Figure 3 Shows the intersection diagram of the axial compressive stress and axial strain of a shale sample according to an embodiment of the present disclosure; Figure 4 Shows the intersection diagram of the radial strain and axial strain of a shale sample according to an embodiment of the present disclosure; Figure 5 Shows the intersection diagram of Young's modulus and brittleness evaluation index according to an embodiment of the present disclosure; Figure 6 Shows the intersection diagram of Poisson's ratio and brittleness evaluation index according to an embodiment of the present disclosure; Figure 7 Shows the seismic prediction profile of the brittleness index passing through Well A according to an embodiment of the present disclosure; Figure 8 Shows the histogram of the number of microseismic events in different fracturing sections of Well A according to an embodiment of the present disclosure. As Figures 1-8As shown in the figure, a method for predicting the brittleness of shale oil reservoirs based on stress-strain curves includes the following steps: Step S01: Obtain the stress-strain curves, logging data, and seismic data of typical shale samples in the study area; Step S02: Determine the correlation coefficients between Young's modulus and Poisson's ratio and the brittleness evaluation index according to the stress-strain curves; Step S03: Determine the weight factors of Young's modulus and Poisson's ratio according to the correlation coefficients between Young's modulus and Poisson's ratio and the brittleness evaluation index, and determine the brittleness prediction model of the shale reservoir according to the weight factors; Step S04: Determine the Young's modulus and Poisson's ratio parameters through prestack seismic inversion according to the logging data and seismic data; Step S05: Determine the brittleness of the shale in the study area according to the Young's modulus and Poisson's ratio parameters and the brittleness prediction model, and complete the prediction of the brittleness of the shale oil reservoir in the study area.

[0066] The method for predicting the brittleness of shale oil reservoirs based on stress-strain curves provided by the embodiments of the present invention specifically includes the following steps:

[0067] Step S01: Obtain the stress-strain curves, logging data, and seismic data of typical shale samples in the study area.

[0068] In the embodiments of the present disclosure, the stress-strain curves of the whole process from loading to failure can be obtained by performing triaxial compression tests on typical shale samples in the study area.

[0069] Figure 2 It is the cross plot of the stress σ-strain ε curve during the triaxial compression test of the shale sample from loading to failure. The abscissa is the strain and the ordinate is the stress. The instrument used for stress-strain testing is the MR-RM3000 triaxial rock mechanics measurement system. This system adopts the design of ball screw and can realize mechanical pressurization with constant stress or strain control. The stress acquisition system includes a confining pressure measurement sensor, a deviator stress measurement sensor, and a pore pressure sensor. The strain acquisition system includes a pair of axial linear differential transformers for measuring axial deformation and a cantilever bridge for measuring two pairs of radial deformations in the orthogonal direction. The diameter and length of the test sample are 38mm and 50mm respectively, and both ends of the sample are manually ground to a precision of ±0.25mm.

[0070] Step S02: Determine the correlation coefficients between Young's modulus and Poisson's ratio and the brittleness evaluation index according to the stress-strain curves.

[0071] In the present disclosure, the method for determining the correlation coefficients of Young's modulus, Poisson's ratio, and the brittleness evaluation index according to the stress-strain curve includes: determining Young's modulus, Poisson's ratio parameters, and a brittleness evaluation index considering the magnitude and rate of post-peak stress drop according to the stress-strain curve; and determining the correlation coefficients of Young's modulus and Poisson's ratio with the brittleness evaluation index according to the brittleness evaluation index, Young's modulus, and Poisson's ratio parameters respectively.

[0072] In the present disclosure, the method for determining a brittleness evaluation index considering the magnitude and rate of post-peak stress drop according to the stress-strain curve includes: determining a brittleness evaluation index considering the magnitude and rate of post-peak stress drop according to the stress-strain curve by using a brittleness evaluation index calculation formula; wherein, the brittleness evaluation index calculation formula is:

[0073]

[0074]

[0075]

[0076] In the formula: σ A is the peak strength of the stress-strain curve, σ B is the residual strength of the stress-strain curve, |k AB | is the absolute value of the slope of the line connecting the peak strength and the residual strength. Taking the logarithm with base 10 and dividing by 10 is to convert it to the range of 0 to 1. B1 is the magnitude of the post-peak stress drop, B2 is the rate of the post-peak stress drop, and B3 is the constructed brittleness evaluation index.

[0077] In an embodiment of the present disclosure, through the stress-strain curve morphology obtained in step S01 during the loading to failure process, the brittle characteristics of the rock can be quantitatively evaluated. If its strength can rapidly decrease to a very small value after the peak, it indicates a high degree of rock brittleness. If the strength decreases slowly after the peak and the residual strength is still very high, it indicates a low degree of rock brittleness. Based on this principle, according to formulas (1) and (2), the magnitude B1 of the post-peak stress drop and the rate B2 of the post-peak stress drop can be determined respectively, and then substituting them into formula (3) can obtain the brittleness evaluation index B3 considering the magnitude B1 of the post-peak stress drop and the rate B2 of the post-peak stress drop. The brittleness evaluation index B3 reflects the brittle degree of the underground rock. The greater the stress drop and the faster the rate, the higher the brittleness.

[0078] In the present disclosure, the method for determining Young's modulus and Poisson's ratio parameters according to the stress-strain curve includes: on the axial compression and axial strain curve, select the strain curve corresponding to the range of the predetermined compressive strength for linear fitting to determine the regression formula of Young's modulus, and determine Young's modulus according to the regression formula of Young's modulus; on the radial strain and axial strain curve, select the strain curve corresponding to the range of the predetermined compressive strength for linear fitting to determine the regression formula of Poisson's ratio, and determine the Poisson's ratio parameter according to the regression formula of Poisson's ratio.

[0079] In the embodiments of the present disclosure, in addition to being able to evaluate the brittleness of rocks, the stress-strain curve can also obtain Young's modulus and Poisson's ratio parameters; Young's modulus is the ratio of axial stress to axial strain, and Poisson's ratio is the opposite of the ratio of radial strain to axial strain.

[0080] Young's modulus is obtained through the cross-plot of axial compression and axial strain in the stress-strain curve; on the cross-plot of axial compression and axial strain, select the relatively linear segment corresponding to the range of the predetermined compressive strength for linear fitting to determine the regression formula of Young's modulus; among them, the range of the predetermined tensile strength can be selected as the relatively linear segment near the 50% pressure point of the compressive strength (axial compression) for fitting to obtain the axial strain coefficient in the fitting formula, which is Young's modulus.

[0081] Figure 3 This is the cross-plot of axial compression and axial strain of the shale sample in this embodiment. Among them, the black thin solid line is the curve of the change relationship between axial compression and axial strain, the black thick solid line is the calculation segment of Young's modulus, and the dotted line is the fitting straight line of Young's modulus. The fracture pressure of the shale sample is 129.8 MPa. Select the relatively linear segment near the pressure point of 64.9 MPa for axial compression for linear fitting, and the regression formula of Young's modulus is obtained as: y = 19.679x + 8.2013. The slope of the fitting straight line of the two, that is, 19.679, is the Young's modulus of the shale sample.

[0082] Poisson's ratio is obtained through the cross-plot of radial strain and axial strain in the stress-strain curve; on the cross-plot of radial strain and axial strain, select the relatively linear segment corresponding to the range of the predetermined compressive strength for linear fitting to determine the regression formula of Poisson's ratio. Among them, the range of the predetermined tensile strength is also selected as the relatively linear segment near the 50% pressure point of the compressive strength for fitting to obtain the axial strain coefficient in the fitting formula, which is the Poisson's ratio parameter.

[0083] Figure 4 This is the cross-plot of radial strain and axial strain in this embodiment. Among them, the black thin solid line is the curve of the change relationship between radial strain and axial strain, the black thick solid line is the calculation segment of Poisson's ratio, and the dotted line is the fitting straight line of Poisson's ratio. Similarly, select the relatively linear segment near the pressure point of 64.9 MPa for linear fitting, that is Figure 3 the corresponding relatively linear segment selected inFigure 4 For the partial relative linear segments, after fitting, the regression formula for Young's Poisson's ratio is obtained as: y = -0.0764x + 0.0731. The opposite of the slope of the fitting line between the two, that is, 0.0764, is the Poisson's ratio parameter of this shale sample.

[0084] In the present disclosure, the method for determining the correlation coefficients between the Young's modulus and Poisson's ratio and the brittleness evaluation index according to the brittleness evaluation index, the Young's modulus, and the Poisson's ratio parameter includes: performing a statistical crossplot analysis on the brittleness evaluation index and the Young's modulus to determine the correlation coefficient between the Young's modulus and the brittleness evaluation index; performing a statistical crossplot analysis on the brittleness evaluation index and the Poisson's ratio parameter to determine the correlation coefficient between the Poisson's ratio and the brittleness evaluation index.

[0085] In an embodiment of the present disclosure, based on the obtained brittleness evaluation index, Young's modulus, and Poisson's ratio parameter, a statistical crossplot analysis is performed. Figure 5 is a crossplot of the Young's modulus and the brittleness evaluation index. In Figure 5 , the points are the measured Young's modulus and brittleness of the core, and the straight line is the linear fitting line regressed based on the measured data points of the core; Figure 6 is a crossplot of the Poisson's ratio and the brittleness evaluation index. In Figure 6 , the points are the measured Poisson's ratio and brittleness of the core, and the straight line is the linear fitting line regressed based on the measured data points of the core; It can be seen from Figure 5 and 6 that the Poisson's ratio has a better correlation with the brittleness evaluation index. The correlation coefficient R 2 is 0.63, and the correlation coefficient between the Young's modulus and the brittleness evaluation index is only 0.38, indicating that the Poisson's ratio is more sensitive to the brittleness of the shale reservoir than the Young's modulus.

[0086] Step S03: Determine the weight factors of the Young's modulus and Poisson's ratio according to the correlation coefficients between the Young's modulus and Poisson's ratio and the brittleness evaluation index, and determine the brittleness prediction model of the shale reservoir according to the weight factors.

[0087] In the present disclosure, the method for determining the weight factors of the Young's modulus and Poisson's ratio according to the correlation coefficients between the Young's modulus and Poisson's ratio and the brittleness evaluation index includes: determining the weight factors of the Young's modulus and Poisson's ratio according to the correlation coefficients between the Young's modulus and Poisson's ratio parameters and the brittleness evaluation index by using the weight factor calculation formula;

[0088] Among them, the weight factor calculation formula includes:

[0089]

[0090] c + d = 1; (5)

[0091] Where: a is the correlation coefficient between the Poisson's ratio and the brittleness evaluation index, b is the correlation coefficient between the Young's modulus and the brittleness evaluation index, c is the Poisson's ratio weight coefficient, and d is the Young's modulus weight coefficient.

[0092] In the embodiments of the present disclosure, the traditional method for predicting brittleness is to perform weighted averaging to characterize rock brittleness based on the positive normalization of the Young's modulus and the inverse normalization of the Poisson's ratio. Through the analysis of the brittleness sensitivity of shale reservoirs, that is, by respectively determining the correlation coefficients between the Young's modulus and the Poisson's ratio and the brittleness evaluation index, it is found that the Poisson's ratio in the study area is more sensitive to shale brittleness than the Young's modulus. Therefore, the traditional brittleness prediction model, which considers that the Young's modulus and the Poisson's ratio contribute equally to the brittleness index, is not applicable to the shale reservoirs in the study area. To address this issue, the present disclosure scales the weight factors of the Young's modulus and the Poisson's ratio in the traditional brittleness index prediction method in an equal-proportion manner according to the brittleness evaluation index and the correlation coefficients of the Young's modulus and the Poisson's ratio, that is, formulas (4) and (5), and then adds the weight factors to the brittleness prediction model, thereby enabling a more accurate prediction result of the brittleness of shale oil reservoirs.

[0093] In the present disclosure, the method for determining the brittleness prediction model of shale reservoirs according to the weight factors includes: using the brittleness prediction model formula according to the weight factors to determine the brittleness prediction model of shale reservoirs;

[0094] Among them, the brittleness prediction model formula includes:

[0095]

[0096]

[0097] B XZ = c×B ν + d×B E ; (8)

[0098] Where: c is the Poisson's ratio weight coefficient, d is the Young's modulus weight coefficient, E is the Young's modulus, ν is the Poisson's ratio; E min 、E max 、ν min 、ν max are respectively the minimum, maximum Young's modulus and the minimum, maximum Poisson's ratio; B E 、B ν 、B XZ are respectively the normalized Young's modulus, Poisson's ratio brittleness index and the shale reservoir brittleness prediction index established based on the stress-strain curve.

[0099] In the embodiments of the present disclosure, substituting the weight factors of the Poisson's ratio and the Young's modulus determined by formulas (4) and (5) into the brittleness prediction model formula (8), the brittleness prediction model of the shale reservoirs in the study area of the present disclosure can be obtained.

[0100] Step S04: Determine the Young's modulus and Poisson's ratio parameters through prestack seismic inversion based on the well logging data and seismic data.

[0101] In the present disclosure, the method for determining the Young's modulus and Poisson's ratio parameters through prestack seismic inversion based on the well logging data and seismic data includes: determining the P-wave velocity, S-wave velocity, and density through prestack seismic inversion based on the well logging data and seismic data; and determining the Young's modulus and Poisson's ratio parameters according to the conversion formulas between the P-wave velocity, S-wave velocity, and density and the Young's modulus and Poisson's ratio.

[0102] In the embodiments of the present disclosure, the prestack seismic inversion technology uses seismic data with different incident angles and well logging curves such as P-wave, S-wave, and density to jointly invert multiple elastic parameters such as P-wave velocity, S-wave velocity, and density related to lithology, physical properties, and hydrocarbon-bearing properties. Then, based on the conversion relationships between the P-wave velocity, S-wave velocity, density and the Young's modulus, Poisson's ratio, the Young's modulus and Poisson's ratio parameters are calculated. At the same time, the brittleness of the shale in the study area is calculated according to the brittleness prediction model of the shale reservoir in the study area established in step S03.

[0103] Among them, the conversion formulas between the P-wave velocity, S-wave velocity, and density and the Young's modulus and Poisson's ratio include:

[0104]

[0105]

[0106] In the formula: V p is the P-wave velocity, V s is the S-wave velocity, ρ is the density, E is the Young's modulus, and ν is the Poisson's ratio.

[0107] Step S05: Determine the brittleness of the shale in the study area according to the Young's modulus and Poisson's ratio parameters and the brittleness prediction model, and complete the brittleness prediction of the shale oil reservoir in the study area.

[0108] In the embodiments of the present disclosure, the brittleness of the shale oil reservoir is described based on the Young's modulus and Poisson's ratio parameters determined in step S04 and the brittleness value of the shale reservoir in the study area calculated by the brittleness prediction model of the shale reservoir determined in step S03, and the favorable area of the brittleness distribution is preferably selected. Among them, the larger the rock brittleness value, the better the brittleness of the shale.

[0109] Figure 7 This is the seismic prediction profile of the brittleness index passing through Well A in the embodiments of the present disclosure. Figure 7The darker part of the color close to black and the lighter part of the color close to white around it in the [description] have a high degree of brittleness, and complex fracture networks are likely to form during fracturing; the remaining gray parts have a low degree of brittleness, and it is not easy to form complex fracture networks during fracturing; the black curve is the actual horizontal well trajectory, the horizontal section length is 1800m, and a total of 32 sections are fractured for oil testing.

[0110] Figure 8 It is a histogram of the number of microseismic events in different fracturing sections of Well A. The area with more microseismic events indicates good fracturing effect. The prediction results show that the brittle degree of Sections 1-22 is high and the fracturability is good. The microseismic monitoring results show that there are many microseismic events in Sections 2-23, which has a good correspondence with the prediction results, indicating the effectiveness of the present invention in predicting the brittleness of shale reservoirs.

[0111] It can be understood that the above-mentioned various method embodiments mentioned in the present disclosure can be combined with each other to form a combined embodiment without violating the principle logic. Due to space limitations, the present disclosure will not elaborate further.

[0112] The execution subject of the brittle prediction method for shale oil reservoirs based on the stress-strain curve can be the brittle prediction device for shale oil reservoirs based on the stress-strain curve. For example, the brittle prediction method for shale oil reservoirs based on the stress-strain curve can be executed by a terminal device, a server, or other processing devices. Among them, the terminal device can be a user equipment (UE), a mobile device, a user terminal, a terminal, a cellular phone, a cordless phone, a personal digital assistant (PDA), a handheld device, a computing device, a vehicle-mounted device, a wearable device, etc. In some possible implementation manners, the brittle prediction method for shale oil reservoirs based on the stress-strain curve can be implemented by a processor calling computer-readable instructions stored in a memory.

[0113] Those skilled in the art can understand that in the above method of the specific implementation manner, the writing order of each step does not mean a strict execution order and does not constitute any limitation to the implementation process. The specific execution order of each step should be determined by its function and possible internal logic.

[0114] The present disclosure provides a brittle prediction device for shale oil reservoirs based on stress-strain curves, including: an acquisition unit for acquiring stress-strain curves, logging data, and seismic data of typical shale samples in a study area; a correlation coefficient determination unit for determining the correlation coefficients between Young's modulus, Poisson's ratio, and brittle evaluation indicators according to the stress-strain curves; a prediction model determination unit for determining the weight factors of Young's modulus and Poisson's ratio according to the correlation coefficients between Young's modulus, Poisson's ratio, and brittle evaluation indicators, and determining a brittle prediction model for shale reservoirs according to the weight factors; a Young's modulus and Poisson's ratio determination unit for determining Young's modulus and Poisson's ratio parameters through prestack seismic inversion according to the logging data and seismic data; and a brittle prediction unit for determining the brittleness of shale in the study area according to the Young's modulus and Poisson's ratio parameters and the brittle prediction model, and completing the brittle prediction of the shale oil reservoir in the study area.

[0115] In some embodiments, the functions, modules, or units of the device provided by the embodiments of the present disclosure can be used to execute the methods described in the method embodiments above. The specific implementation can refer to the description of the method embodiments above. For the sake of brevity, it will not be repeated here.

[0116] Based on the triaxial compression test of shale samples, the present disclosure proposes a brittle evaluation indicator considering the magnitude and rate of stress drop after the peak, combines the rock elastic parameters obtained by synchronous measurement, analyzes the sensitivity of Young's modulus and Poisson's ratio to rock brittleness through statistical intersection, and scales the weight coefficients of Young's modulus and Poisson's ratio in the brittle evaluation method based on elastic parameters in an equal-proportion manner according to the correlation coefficients between the brittle evaluation indicator and Young's modulus and Poisson's ratio, establishes a brittle evaluation model that better describes the brittleness of shale reservoirs, and finally realizes the description of the spatial distribution of shale reservoir brittleness by combining prestack inversion technology.

[0117] The present disclosure combines the brittle evaluation based on stress-strain curves and the brittle evaluation based on elastic parameters, which not only solves the problems of high coring cost and low experimental measurement efficiency of the brittle evaluation method based on stress-strain curves, but also solves the problem of unclear proportion of weight coefficients in the brittle evaluation method based on elastic parameters, making the brittle prediction results of shale reservoirs more reasonable, practical, and innovative, and capable of guiding work such as hydraulic fracturing of shale reservoirs.

[0118] The above have described the embodiments of the present disclosure. The above description is exemplary, not exhaustive, and is not limited to the disclosed embodiments. Many modifications and variations are obvious to those of ordinary skill in the art in the technical field without departing from the scope and spirit of the described embodiments. The choice of terms used herein is intended to best explain the principles of the embodiments, practical applications, or improvements to technologies in the market, or to enable other ordinary technical personnel in the technical field to understand the embodiments disclosed herein.

Claims

1. A method for predicting the brittleness of shale oil reservoirs based on stress-strain curves, characterized in that, Comprising: Obtaining the stress-strain curve, logging data, and seismic data of typical shale samples in the study area; According to the stress-strain curve, determining the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index. The method includes: respectively determining Young's modulus, Poisson's ratio parameters, and the brittleness evaluation index considering the post-peak stress drop magnitude and stress drop rate based on the stress-strain curve; the calculation formula for the brittleness evaluation index is: ； In the formula: is the magnitude of the post-peak stress drop, is the rate of the post-peak stress drop, is the constructed brittleness evaluation index; According to the brittleness evaluation index, as well as Young's modulus and Poisson's ratio parameters, respectively determining the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index; According to the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index, determining the weight factors of Young's modulus and Poisson's ratio. The method includes: using the weight factor calculation formula to determine the weight factors of Young's modulus and Poisson's ratio; where the weight factor calculation formula includes: ； ； In the formula: a is the correlation coefficient between Poisson's ratio and the brittleness evaluation index, b is the correlation coefficient between Young's modulus and the brittleness evaluation index, c is the Poisson's ratio weight coefficient, and d is the Young's modulus weight coefficient; According to the weight factors, determining the brittleness prediction model for shale reservoirs; According to the logging data and seismic data, through prestack seismic inversion, determining Young's modulus and Poisson's ratio parameters. The method includes: on the axial compression and axial strain curves, selecting the corresponding strain curves within a predetermined compressive strength range for linear fitting to determine the Young's modulus regression formula, and determining Young's modulus according to the Young's modulus regression formula; on the radial strain and axial strain curves, selecting the corresponding strain curves within a predetermined compressive strength range for linear fitting to determine the Poisson's ratio regression formula, and determining Poisson's ratio parameters according to the Poisson's ratio regression formula; According to the Young's modulus, Poisson's ratio parameters, and the brittleness prediction model, determining the brittleness of shale in the study area, and completing the brittleness prediction of shale oil reservoirs in the study area.

2. The method for predicting the brittleness of shale oil reservoirs based on the stress-strain curve according to claim 1, characterized in that: ； ； In the formula: is the peak strength of the stress-strain curve, is the residual strength of the stress-strain curve,| k AB | is the absolute value of the slope of the line connecting the peak strength and the residual strength.

3. The brittle prediction method for shale oil reservoirs based on stress-strain curves according to claim 2, wherein, The method for determining the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index according to the brittleness evaluation index, as well as Young's modulus and Poisson's ratio parameters, includes: Performing statistical crossplot analysis on the brittleness evaluation index and Young's modulus to determine the correlation coefficient between Young's modulus and the brittleness evaluation index; Performing statistical crossplot analysis on the brittleness evaluation index and Poisson's ratio parameters to determine the correlation coefficient between Poisson's ratio and the brittleness evaluation index.

4. The brittle prediction method for shale oil reservoirs based on stress-strain curves according to claim 1, wherein The method for determining the brittleness prediction model for shale reservoirs according to the weight factors includes: According to the weight factors, using the brittleness prediction model formula to determine the brittleness prediction model for shale reservoirs; Wherein, the brittleness prediction model formula includes: ； ； ； where: c is the Poisson's ratio weight coefficient, d is the Young's modulus weight coefficient, E is the Young's modulus, is the Poisson's ratio, E min 、E max 、 、 are the minimum and maximum Young's moduli and the minimum and maximum Poisson's ratios respectively, 、 、 are the normalized Young's modulus, Poisson's ratio brittleness index and the shale reservoir brittleness prediction index established based on the stress-strain curve respectively.

5. The brittle prediction method for shale oil reservoirs based on stress-strain curves according to any one of claims 1-4, characterized in that, The method for determining Young's modulus and Poisson's ratio parameters through prestack seismic inversion according to the logging data and seismic data includes: According to the logging data and seismic data, through prestack seismic inversion, determining the P-wave velocity, S-wave velocity, and density; According to the P-wave velocity, S-wave velocity, and density, using their conversion formulas with Young's modulus and Poisson's ratio to determine Young's modulus and Poisson's ratio parameters.

6. A brittle prediction device for shale oil reservoirs based on stress-strain curves, characterized in that, Comprising: An acquisition unit for acquiring the stress-strain curve, logging data, and seismic data of typical shale samples in the study area; A correlation coefficient determination unit for determining the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index based on the stress-strain curve. The method includes: respectively determining Young's modulus, Poisson's ratio parameters, and the brittleness evaluation index considering the magnitude and rate of stress drop after the peak according to the stress-strain curve; the calculation formula for the brittleness evaluation index is: ； In the formula: is the magnitude of the post-peak stress drop, is the rate of the post-peak stress drop, is the constructed brittleness evaluation index; Respectively determining the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index based on the brittleness evaluation index, Young's modulus, and Poisson's ratio parameters; A prediction model determination unit for determining the weight factors of Young's modulus and Poisson's ratio based on the correlation coefficients between Young's modulus, Poisson's ratio, and the brittleness evaluation index. The method includes: determining the weight factors of Young's modulus and Poisson's ratio using the weight factor calculation formula; wherein, the weight factor calculation formula includes: ； ； In the formula: a is the correlation coefficient between Poisson's ratio and the brittleness evaluation index, b is the correlation coefficient between Young's modulus and the brittleness evaluation index, c is the Poisson's ratio weight coefficient, and d is the Young's modulus weight coefficient; Determining the brittleness prediction model of the shale reservoir according to the weight factors; A Young's modulus and Poisson's ratio determination unit for determining Young's modulus and Poisson's ratio parameters through prestack seismic inversion based on the logging data and seismic data. The method includes: on the axial compression and axial strain curve, selecting the strain curve corresponding to the predetermined compressive strength range for linear fitting to determine the Young's modulus regression formula, and determining Young's modulus according to the Young's modulus regression formula; on the radial strain and axial strain curve, selecting the strain curve corresponding to the predetermined compressive strength range for linear fitting to determine the Poisson's ratio regression formula, and determining Poisson's ratio parameters according to the Poisson's ratio regression formula; A brittleness prediction unit for determining the brittleness of shale in the study area according to the Young's modulus, Poisson's ratio parameters, and the brittleness prediction model, and completing the brittleness prediction of the shale oil reservoir in the study area.

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