Fine fracturing geological modeling method based on scratch test

Abstract

According to the fine fracturing geological modeling method based on the scratch test provided by the embodiment of the invention, the scratch test data representing the mechanical characteristics of a rock core sample material are accurately obtained by carrying out the scratch test on the rock core sample, the mechanical data are calculated and generated according to the scratch test data, and the wavelet transform is carried out on the mechanical data to obtain the fine fracturing geological modeling result. Compared with the prior art, the method has the advantages that the mechanical parameters of the reservoir rock are accurately obtained through scratch testing, the mechanical characteristics of the rock are represented, the mechanical characteristic model is optimized, and design data with higher accuracy is provided for a geological model.

Description

Technical Field

[0001] This application relates to the technical field of hydraulic fracturing crack propagation, and particularly to a fine fracturing geological modeling method based on scratch tests. Background Art

[0002] Hydraulic fracturing is a key technology for improving the development efficiency of low-permeability reservoirs, and can significantly improve the reservoir seepage conditions by forming a fracture network. The hydraulic fracturing method is optimized and designed through geological modeling. However, the generation and propagation of fractures are comprehensively affected by various factors such as the mechanical properties of reservoir rocks, the distribution of natural fractures, and the in-situ stress field. Traditional geological modeling methods are difficult to accurately characterize, resulting in a decrease in the accuracy of modeling.

[0003] In the prior art, rock mechanical parameters are used as the core input for fracture simulation to construct a geological model.

[0004] However, in the prior art, in order to obtain mechanical parameters, large rock samples are required for experiments, and for lithologies with significant heterogeneity in the reservoir, the mechanical parameters of a single rock sample are difficult to reflect the actual mechanical distribution characteristics of the reservoir, resulting in a decrease in the accuracy of modeling data. Summary of the Invention

[0005] An embodiment of this application provides a fine fracturing geological modeling method based on scratch tests to solve the problem of reduced accuracy of modeling data existing in the prior art.

[0006] In a first aspect, an embodiment of this application provides a fine fracturing geological modeling method based on scratch tests, including:

[0007] Step 1: Obtain a core sample from the reservoir downhole, and cut the core sample to obtain a core sample with a flat surface;

[0008] Step 2: Calibrate the core sample with the flat surface to obtain a calibrated core sample;

[0009] Step 3: Perform a scratch test on the calibrated core sample through a scratch test device to generate scratch test data;

[0010] Step 4: Calculate and generate mechanical data based on the scratch test data;

[0011] Step 5: Perform wavelet transform on the scratch test data and the mechanical data to generate a characteristic data set of the reservoir fracturing mechanical model.

[0012] In a possible implementation manner, in Step 1, the core sample is cut into a core sample with a flat surface of 400mm×80mm×70mm.

[0013] In a possible implementation, the core samples in step 1 include interbedded shale and fine sandstone samples, sand-shale interbedded samples, fine sandstone with muddy interlayers samples, and grayish-brown oil-stained fine sandstone samples.

[0014] In a possible implementation, in step 2, the lithology, lithological interface, and lamination distribution position of the core samples on the flat surface are calibrated to obtain the calibrated core samples.

[0015] In a possible implementation, the scratch test data in step 3 includes, but is not limited to, shear stress, normal stress, scratching depth, and the width of the cutting tool.

[0016] In a possible implementation, the formulas for calculating the shear stress, normal stress, scratching depth, and the width of the cutting tool are as follows:

[0017] F s = εA

[0018] F n = ζεA

[0019] A = wh

[0020] ζ = tan(θ + ψ)

[0021] In the formula, F s represents the shear stress; F n represents the normal stress; ε represents the specific work of rock fracture; ζ represents the ratio of the normal stress to the shear stress; A represents the cross-sectional area of the scratching surface; w represents the width of the cutting tool; h represents the scratching depth; θ represents the back rake angle of the blade; ψ represents the interface friction angle.

[0022] In a possible implementation, the mechanical data in step 4 includes, but is not limited to, compressive strength, tensile strength, Young's modulus, Poisson's ratio, and fracture toughness.

[0023] In a possible implementation, the formulas for calculating the compressive strength, tensile strength, Young's modulus, Poisson's ratio, and fracture toughness are as follows:

[0024]

[0025] σ t = kσ c

[0026]

[0027]

[0028] In the formula, σ c represents the compressive strength; σ tdenotes the tensile strength; E denotes the Young's modulus; v denotes the Poisson's ratio; K IC denotes the fracture toughness.

[0029] In a possible implementation, Step 5: Perform wavelet transform on the scratch test data and the mechanical data to generate a characteristic data set of the reservoir fracturing mechanical model, including:

[0030] Take the inner product of the mechanical data and the wavelet basis function to obtain a continuous wavelet transform function;

[0031] Decompose and reconstruct the mechanical data through the Mallat algorithm to obtain a decomposed and reconstructed data set;

[0032] Analyze the continuous wavelet transform function and the decomposed and reconstructed data set to obtain a characteristic data set of the reservoir fracturing mechanical model.

[0033] In a possible implementation, the continuous wavelet transform function is:

[0034]

[0035] In the formula, a represents the scaling factor and b represents the translation factor.

[0036] A fine fracturing geological modeling method based on scratch test provided by an embodiment of the present application accurately obtains scratch test data characterizing the mechanical properties of a core sample by performing a scratch test on the core sample, calculates and generates mechanical data based on the scratch test data, and performs wavelet transform on the mechanical data to obtain a characteristic data set for constructing a fracturing mechanical model. Compared with the prior art, the mechanical parameters of the reservoir rock are accurately obtained through the scratch test, the mechanical characteristics of the rock are characterized, thereby optimizing the mechanical characteristic model and providing design data with higher accuracy for the geological model. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] The accompanying drawings herein are incorporated into the specification and form a part of the specification, showing embodiments consistent with the present application and used together with the specification to explain the principles of the present application.

[0038] Figure 1 It is a statistical chart of the strength difference of the lithological interfaces of the core samples provided by an embodiment of the present application;

[0039] Figure 2 It is a schematic diagram of the scratch test provided by an embodiment of the present application;

[0040] Figure 3 It is a schematic diagram of the scratch test results of different core samples provided by an embodiment of the present application;

[0041] Figure 4Schematic diagram of the decomposition of the Mallat algorithm provided by the embodiments of the present application;

[0042] Figure 5 Schematic diagram of the reconstruction of the Mallat algorithm provided by the embodiments of the present application;

[0043] Figure 6 Schematic diagram of the parameters of the reservoir fracturing mechanical model provided by the embodiments of the present application;

[0044] Figure 7 Schematic diagram of the mechanical models corresponding to the respective mechanical parameters provided by the embodiments of the present application.

[0045] Through the above-mentioned drawings, specific embodiments of the present application have been shown, and there will be more detailed descriptions hereinafter. These drawings and textual descriptions are not intended to limit the scope of the concept of the present application in any way, but to illustrate the concept of the present application to those skilled in the art by referring to specific embodiments. Detailed Description of the Embodiments

[0046] Here, the exemplary embodiments will be described in detail, and the examples are shown in the drawings. When the following description refers to the drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0047] Hydraulic fracturing is a key technology for improving the development efficiency of low-permeability reservoirs and can significantly improve the reservoir seepage conditions by forming a fracture network. The hydraulic fracturing method is optimized and designed through geological modeling. However, the generation and propagation of fractures are comprehensively affected by various factors such as the mechanical properties of reservoir rocks, the distribution of natural fractures, and the in-situ stress field. Traditional geological modeling methods are difficult to accurately characterize, resulting in a decrease in the accuracy of modeling. In the prior art, rock mechanical parameters are used as the core input for fracture simulation to construct a geological model. However, in the prior art, in order to obtain mechanical parameters, large rock samples are required for experiments, and for lithologies with significant heterogeneity in the reservoir, the mechanical parameters of a single rock sample are difficult to reflect the actual mechanical distribution characteristics of the reservoir, resulting in a decrease in the accuracy of the modeling data.

[0048] To solve the above technical problems, the embodiments of the present application propose the following technical concepts: The inventors considered calibrating the lithology and the distribution position of stable layers for core samples with flat surfaces, conducting scratch tests on the calibrated samples to generate scratch test data, calculating mechanical data based on the scratch test data, performing wavelet transforms on the scratch test data and the mechanical data to generate a characteristic dataset for the reservoir fracturing mechanical model, and using the characteristic dataset to optimize and construct the mechanical model. Compared with the prior art, the mechanical parameters of reservoir rocks can be accurately obtained through scratch tests to characterize the mechanical characteristics of the rocks, thereby optimizing the mechanical characteristic model and providing design data with higher accuracy for geological models. The following will be described in detail with specific embodiments.

[0049] The embodiments of the present application provide a fine fracturing geological modeling method based on scratch tests, including:

[0050] Step 1: Obtain core samples from the reservoir wellbore and cut the core samples to obtain core samples with flat surfaces.

[0051] Step 2: Calibrate the core samples with flat surfaces to obtain calibrated core samples.

[0052] Step 3: Conduct scratch tests on the calibrated core samples through a scratch test device to generate scratch test data.

[0053] Step 4: Calculate and generate mechanical data based on the scratch test data.

[0054] Step 5: Perform wavelet transforms on the scratch test data and the mechanical data to generate a characteristic dataset for the reservoir fracturing mechanical model.

[0055] Among them, in Step 1, the core samples are cut into core samples with flat surfaces of size 400mm×80mm×70mm, and the lithology, lithological interfaces, and bedding distributions are calibrated.

[0056] In this embodiment, the maximum height of the core samples is 100mm.

[0057] In this embodiment, the core samples include interbedded samples of mudstone and fine sandstone, sand-mud interbedded samples, fine sandstone shale interlayer samples, and grayish-brown oil-spotted fine sandstone samples.

[0058] Among them, in Step 3, the cutter scratches the surface of the rock sample at a constant rate and constant depth, and tests the strength parameters of the cross-section rock by recording the displacement and force data of the cutter in real time, and records the scratch test data.

[0059] Figure 1 It is a statistical chart of the strength differences of lithological interfaces of the core samples provided by the embodiments of the present application.

[0060] Such as Figure 1As shown, (1) is a statistical chart of the strength difference of the lithologic interface of the mudstone and fine sandstone interbedded samples; (2) is a statistical chart of the strength difference of the lithologic interface of the sand-mud interbedded samples; (3) is a statistical chart of the strength difference of the lithologic interface of the fine sandstone argillaceous interlayer samples; (4) is a statistical chart of the strength difference of the lithologic interface of the grayish-brown oil stain fine sandstone samples.

[0061] In this embodiment, the maximum scribing length is 400 mm.

[0062] In this embodiment, the scratch test data in step 3 includes but is not limited to shear stress, normal stress, scribing depth, and the width of the cutting tool.

[0063] In this embodiment, the form of scribing to damage the surface of the rock sample is plastic failure.

[0064] Among them, there are two forms of rock failure: plastic failure and brittle failure. The form of rock failure is related to the scribing depth, and there is a threshold scribing depth for the rock. When the scribing depth is less than the threshold depth, the form of rock failure is plastic failure; when the scribing depth is greater than the threshold depth, the form of rock failure is brittle failure.

[0065] Figure 2 It is a schematic diagram of the scratch test provided by the embodiment of the present application.

[0066] As Figure 2 shown, during the scribing process, the blade is subjected to the force F k . Define the horizontal direction, i.e., the tangential direction, as s, and the vertical direction, i.e., the normal stress direction, as n. Then the force F k can be decomposed into the normal stress F n and the shear stress F s .

[0067] Among them, the formulas for shear stress, normal stress, scribing depth, and the width of the cutting tool are:

[0068] F s = εA

[0069] F n = ζεA

[0070] A = wh

[0071] ζ = tan(θ + ψ)

[0072] In the formula, F s represents the shear stress; F n represents the normal stress; ε represents the inherent specific work of rock fragmentation; ζ represents the ratio of normal stress to shear stress; A represents the cross-sectional area of the scribing surface; w represents the width of the cutting tool; h represents the scribing depth; θ represents the back rake angle of the blade; ψ represents the interface friction angle.

[0073] In this embodiment, Table 1 shows the parameters of the scratch test equipment set in Step 3.

[0074] Table 1 Parameters of the scratch test equipment

[0075] Name Parameter Range Core Diameter 25 - 200 mm Core Length 2.0 - 50.0 cm Force Sensor Range 1～2000N Horizontal Displacement Resolution 0.1 mm Cutting Depth Resolution 0.001 mm Sample Data Resolution 10 pts / mm

[0076] Figure 3 It is a schematic diagram of the scratch test results of different core samples provided by the embodiments of this application.

[0077] Among them, the mechanical data in Step 4 includes but is not limited to compressive strength, tensile strength, Young's modulus, Poisson's ratio, and fracture toughness.

[0078] In this embodiment, the formulas for calculating compressive strength, tensile strength, Young's modulus, Poisson's ratio, and fracture toughness are as follows:

[0079]

[0080] σ t = kσ c

[0081]

[0082]

[0083] In the formula, σ c represents compressive strength; σ t represents tensile strength; E represents Young's modulus; v represents Poisson's ratio; K IC represents fracture toughness.

[0084] In this embodiment, in Step 5, the wavelet transform method is used to extract the features of the mechanical data.

[0085] Specifically, the wavelet mother function is scaled and translated to obtain the wavelet basis function.

[0086] Among them, the wavelet basis function is expressed as:

[0087]

[0088] In the formula, a represents the scaling factor, which determines the time position of the approximation signal; b represents the translation factor, which determines the sampling window length of the signal.

[0089] Specifically, the inner product of the function x(t) of the mechanical data in the L 2 (R) space and the wavelet basis function is taken to obtain the continuous wavelet transform function:

[0090]

[0091] Wherein, a represents the scaling factor and b represents the translation factor.

[0092] Among them, the relationship between the scaling factor and the signal frequency is as follows: the smaller the scaling factor, the more the wavelet is compressed, measuring the details of the signal and corresponding to the high-frequency signal of the data; the larger the scaling factor, the more the wavelet is stretched, measuring the roughness of the signal and corresponding to the low-frequency signal of the data.

[0093] In this embodiment, data decomposition and reconstruction are performed through the Mallat algorithm.

[0094] Specifically, the signal is decomposed through the Mallat algorithm. Let the signal to be processed be f(t), and it has an approximation in the V j space. represents the scaling function, and ψ j,k (t) represents the wavelet function. And {ψ j,k (t), k ∈ Z} are the canonical orthogonal bases of V j and W j respectively. Then f(t) = {t0, t1, …, t j} can be decomposed into:

[0095]

[0096] In the formula, A j+1,n represents the vector of the scaling factor; B j+1,n represents the vector of the translation factor.

[0097] Simplify f(t) through an infinite matrix, where to obtain:

[0098]

[0099] In the formula, H n,k and G n,k represent the results of convolution.

[0100] Figure 4 is the decomposition schematic diagram of the Mallat algorithm provided by the embodiment of the present application.

[0101] Figure 5 is the reconstruction schematic diagram of the Mallat algorithm provided by the embodiment of the present application.

[0102] Specifically, after the signal f(t) is decomposed to different resolution levels for analysis and processing, the functions located at different resolution levels are superimposed again to obtain the representation form in V j again. Then the reconstruction algorithm of Mallat is:

[0103]

[0104] Figure 6 It is a schematic diagram of the reservoir fracturing mechanics model parameters provided by the embodiments of the present application. Specifically, a mechanics model is constructed based on the reservoir fracturing mechanics model parameters after wavelet transform, and each mechanics parameter is recorded in Table 2.

[0105] Table 2 Three-dimensional mechanics model parameters

[0106]

[0107]

[0108] Figure 7 It is a schematic diagram of the mechanics models corresponding to the respective mechanics parameters provided by the embodiments of the present application.

[0109] As Figure 7 shown, (1) is a schematic diagram of the Young's modulus model; (2) is a schematic diagram of the Poisson's ratio model; (3) is a schematic diagram of the tensile strength model; (4) is a schematic diagram of the compressive strength model; (5) is a schematic diagram of the fracture toughness model.

[0110] Finally, it should be noted that: After considering the specification and practicing the invention disclosed herein, those skilled in the art will readily think of other embodiments of the present invention. The present invention is intended to cover any variations, uses, or adaptations of the present invention, which follow the general principles of the present invention and include the common general knowledge or conventional technical means in the technical field not disclosed in the present invention. It is not limited to the exact structures already described and shown in the drawings, and various modifications and changes can be made without departing from its scope. The scope of the present invention is only limited by the appended claims.

Claims

1. A fine fracturing geological modeling method based on scratch testing, characterized in that: include: Step 1: Obtain a core sample from a reservoir well, and cut the core sample to obtain a core sample with a flat surface; Step 2: calibrating the core sample on the flat surface to obtain a calibrated core sample; Step 3: Performing a scratch test on the calibrated core sample using a scratch test device to generate scratch test data; Step 4: Calculate and generate mechanical data based on the scratch test data; Step 5: Perform wavelet transform on the scratch test data and the mechanical data to generate a characteristic data set of a reservoir fracturing mechanical model.

2. The method according to claim 1, characterized in that In step 1, the core sample is cut into a core sample with a flat surface of 400 mm×80 mm×70 mm.

3. The method according to claim 1, characterized in that: The core samples in step 1 include mudstone and fine sandstone interlayer samples, sand-mud interlayer samples, fine sandstone mud interlayer samples and gray-brown oil-stained fine sandstone samples.

4. The method according to claim 1, characterized in that In the step 2, the lithology, lithology interface and lamina distribution position of the core sample with the flat surface are calibrated to obtain a calibrated core sample.

5. The method according to claim 1, characterized in that The scratch test data in step 3 include but are not limited to shear stress, normal stress, scratch depth and width of the scratching knife.

6. The method according to claim 5, characterized in that The formula for calculating the shear stress, normal stress, scratching depth and width of the carving knife is: F s =εA F n =zeA A=wh ζ=tan(θ+ψ) In the formula, F s represents shear stress; F n represents normal stress; ε represents the inherent crushing specific work of rock; ζ represents the ratio of normal stress to shear stress; A represents the cross-sectional area of ​​the scratched surface; w represents the width of the carving knife; h represents the scratching depth; θ represents the blade rake angle; ψ represents the interface friction angle.

7. The method according to claim 1, characterized in that The mechanical data in step 4 include but are not limited to compressive strength, tensile strength, Young's modulus, Poisson's ratio and fracture toughness.

8. The method according to claim 7, characterized in that The formula for calculating the compressive strength, tensile strength, Young's modulus, Poisson's ratio and fracture toughness is: s t =kσ c In the formula, σ c Indicates compressive strength; σ t represents tensile strength; E represents Young's modulus; v represents Poisson's ratio; K IC Indicates fracture toughness.

9. The method according to claim 1, characterized in that: The step 5: performing wavelet transformation on the scratch test data and the mechanical data to generate a characteristic data set of a reservoir fracturing mechanical model, including: Taking the inner product of the mechanical data and the wavelet basis function to obtain a continuous wavelet transform function; Decomposing and reconstructing the mechanical data by using a Mallett algorithm to obtain a decomposed and reconstructed data set; The continuous wavelet transform function and the decomposed and reconstructed data set are analyzed to obtain a characteristic data set of a reservoir fracturing mechanics model.

10. The method according to claim 9, characterized in that The continuous wavelet transform function is: In the formula, a represents the scaling factor and b represents the translation factor.

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