A refined fracturing geological modeling method based on scratch testing

Abstract

This application provides a refined fracturing geological modeling method based on scratch testing. By performing scratch testing on core samples, scratch test data characterizing the mechanical properties of the core sample material is accurately obtained. Mechanical data is calculated based on the scratch test data, and wavelet transform is performed on the mechanical data to obtain a feature dataset for constructing a fracturing mechanical model. Compared with the prior art, scratch testing accurately obtains the mechanical parameters of reservoir rocks, characterizes the mechanical properties of the rocks, thereby optimizing the mechanical feature model and providing more accurate design data for the geological model.

Description

Technical Field

[0001] This application relates to the field of hydraulic fracturing fracture propagation technology, and in particular to a fine fracturing geological modeling method based on scratch testing. Background Technology

[0002] Hydraulic fracturing is a key technology for improving the development efficiency of low-permeability reservoirs, significantly improving reservoir seepage conditions through the formation of fracture networks. Geological modeling is used to optimize the design of hydraulic fracturing methods. However, fracture generation and propagation are influenced by a combination of factors, including the mechanical properties of the reservoir rock, the distribution of natural fractures, and the geostress field. Traditional geological modeling methods struggle to accurately characterize these factors, leading to decreased modeling accuracy.

[0003] In existing technologies, rock mechanics parameters are used as the core input for crack simulation to construct geological models.

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

[0005] This application provides a refined fracturing geological modeling method based on scratch testing to address the problem of reduced accuracy of modeling data in existing technologies.

[0006] In a first aspect, embodiments of this application provide a refined fracturing geological modeling method based on scratch testing, including:

[0007] Step 1: Obtain core samples from the reservoir well and cut the core samples to obtain core samples with flat surfaces;

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

[0009] Step 3: Perform a scratch test on the calibrated core sample using a scratch testing 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 feature dataset for the reservoir fracturing mechanical model.

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

[0013] In one possible implementation, the core samples in step 1 include mudstone and fine sandstone interbedded samples, sandstone and mudstone interbedded samples, fine sandstone mudstone interlayer samples, and grayish-brown oil-stained fine sandstone samples.

[0014] In one possible implementation, step 2 involves calibrating the lithology, lithological interfaces, and lamination distribution of the core sample on the flat surface to obtain a calibrated core sample.

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

[0016] In one possible implementation, the formula for calculating the shear stress, normal stress, scoring depth, and blade width is:

[0017] F s =εA

[0018] F n =ζεA

[0019] A = wh

[0020] ζ = tan(θ + ψ)

[0021] In the formula, F s F represents shear stress. n ε represents normal stress; ζ represents the inherent breaking work of the rock; A represents the cross-sectional area of ​​the etched surface; w represents the width of the etch tool; h represents the etch depth; θ represents the blade back tilt angle; and ψ represents the interface friction angle.

[0022] In one 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 one possible implementation, the formula for calculating the compressive strength, tensile strength, Young's modulus, Poisson's ratio, and fracture toughness is:

[0024]

[0025] σ t =kσ c

[0026]

[0027]

[0028] In the formula, σ c Indicates compressive strength; σ tE represents tensile strength; V represents Young's modulus; K represents Poisson's ratio; IC This indicates fracture toughness.

[0029] In one possible implementation, step 5: performing wavelet transform on the scratch test data and the mechanical data to generate a feature dataset for the reservoir fracturing mechanical model, including:

[0030] The mechanical data are multiplied by the wavelet basis functions to obtain the continuous wavelet transform function;

[0031] The mechanical data is decomposed and reconstructed using the Mahlert algorithm to obtain the decomposed and reconstructed dataset;

[0032] The characteristic dataset of the reservoir fracturing mechanics model is obtained by analyzing the continuous wavelet transform function and the decomposed and reconstructed dataset.

[0033] In one 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] This application provides a refined fracturing geological modeling method based on scratch testing. By performing scratch testing on core samples, scratch test data characterizing the mechanical properties of the core sample material is accurately obtained. Mechanical data is calculated based on the scratch test data, and wavelet transform is performed on the mechanical data to obtain a feature dataset for constructing a fracturing mechanical model. Compared with existing technologies, scratch testing accurately obtains the mechanical parameters of reservoir rocks, characterizes the mechanical properties of the rocks, and thus optimizes the mechanical feature model, providing more accurate design data for geological models. Attached Figure Description

[0037] The accompanying drawings, which are incorporated in and form part of this specification, illustrate embodiments consistent with this application and, together with the description, serve to explain the principles of this application.

[0038] Figure 1 Statistical diagram of lithological interface strength differences in core samples provided in the embodiments of this application;

[0039] Figure 2 A schematic diagram of a scratch test provided in an embodiment of this application;

[0040] Figure 3 A schematic diagram illustrating the scratch test results of different core samples provided in the embodiments of this application;

[0041] Figure 4A schematic diagram of the decomposition of the Mahlert algorithm provided in the embodiments of this application;

[0042] Figure 5 This is a schematic diagram of the reconstruction of the Mahlert algorithm provided in the embodiments of this application;

[0043] Figure 6 A schematic diagram of the reservoir fracturing mechanical model parameters provided in the embodiments of this application;

[0044] Figure 7 This is a schematic diagram of the mechanical model corresponding to each mechanical parameter provided in the embodiments of this application.

[0045] The accompanying drawings illustrate specific embodiments of this application, which will be described in more detail below. These drawings and descriptions are not intended to limit the scope of the concept in any way, but rather to illustrate the concept of this application to those skilled in the art through reference to particular embodiments. Detailed Implementation

[0046] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numbers in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this application. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this application as detailed in the appended claims.

[0047] Hydraulic fracturing is a key technology for improving the development efficiency of low-permeability reservoirs, significantly improving reservoir seepage conditions through the formation of fracture networks. Geological modeling is used to optimize the design of hydraulic fracturing methods. However, fracture generation and propagation are influenced by a combination of factors, including the mechanical properties of reservoir rocks, the distribution of natural fractures, and the geostress field. Traditional geological modeling methods struggle to accurately characterize these factors, leading to decreased modeling accuracy. Current technologies use rock mechanical parameters as the core input for fracture simulation to construct geological models. However, obtaining these mechanical parameters requires large rock samples for experiments. Furthermore, for lithologies with significant heterogeneity within the reservoir, the mechanical parameters of a single rock sample are insufficient to reflect the actual mechanical distribution characteristics of the reservoir, further reducing the accuracy of the modeling data.

[0048] To address the aforementioned technical problems, this application proposes the following technical concept: The inventors considered calibrating the lithology and stable layer distribution of core samples with flat surfaces, performing scratch tests on the calibrated samples to generate scratch test data, calculating mechanical data based on the scratch test data, and performing wavelet transform on the scratch test data and mechanical data to generate a feature dataset for a reservoir fracturing mechanical model. This feature dataset is then used to optimize and construct the mechanical model. Compared to existing technologies, scratch testing accurately obtains the mechanical parameters of reservoir rocks, characterizing the mechanical features of the rocks, thereby optimizing the mechanical feature model and providing more accurate design data for geological models. Detailed embodiments are described below.

[0049] This application provides a refined fracturing geological modeling method based on scratch testing, including:

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

[0051] Step 2: Calibrate the core sample with a flat surface to obtain the calibrated core sample.

[0052] Step 3: Perform scratch tests on the calibrated core samples using scratch testing equipment to generate scratch test data.

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

[0054] Step 5: Perform wavelet transform on the scratch test data and mechanical data to generate the feature dataset of the reservoir fracturing mechanical model.

[0055] In step 1, the core sample is cut into a flat core sample with a size of 400mm×80mm×70mm, and the lithology, lithological interface and laminar distribution are calibrated.

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

[0057] In this embodiment, the core samples include mudstone and fine sandstone interbedded samples, sandstone and mudstone interbedded samples, fine sandstone mudstone interlayer samples, and grayish-brown oily fine sandstone samples.

[0058] In step 3, the cutting tool scratches the surface of the rock sample at a constant speed and depth. The rock strength parameters of the cross-section are tested by recording the displacement and force data of the cutting tool in real time, and the scratch test data is recorded.

[0059] Figure 1 A statistical diagram showing the difference in lithological interface strength of core samples provided in this application embodiment.

[0060] like Figure 1As shown, (1) is a statistical diagram of the difference in lithological interface strength between mudstone and fine sandstone interbedded samples; (2) is a statistical diagram of the difference in lithological interface strength between sandstone and mudstone interbedded samples; (3) is a statistical diagram of the difference in lithological interface strength between fine sandstone and mudstone interbedded samples; and (4) is a statistical diagram of the difference in lithological interface strength between gray-brown oil-stained fine sandstone samples.

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

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

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

[0064] Rock failure can be categorized into two forms: ductile failure and brittle failure. The form of rock failure is related to the depth of the incision, and there is a threshold depth for the incision. When the depth of the incision is less than the threshold depth, the rock failure is ductile; when the depth of the incision is greater than the threshold depth, the rock failure is brittle.

[0065] Figure 2 This is a schematic diagram of a scratch test provided in an embodiment of this application.

[0066] like Figure 2 As shown, the blade is subjected to a force F during the scribing process. k Let the horizontal direction (tangential direction) be defined as s, and the vertical direction (normal stress direction) as n, then the force F k It can be decomposed into normal stress F n and shear stress F s .

[0067] The formulas for shear stress, normal stress, scoring depth, and blade width are as follows:

[0068] F s =εA

[0069] F n =ζεA

[0070] A = wh

[0071] ζ = tan(θ + ψ)

[0072] In the formula, F s F represents shear stress. n ε represents normal stress; ζ represents the inherent breaking work of the rock; A represents the cross-sectional area of ​​the etched surface; w represents the width of the etch tool; h represents the etch depth; θ represents the blade back tilt angle; and ψ represents the interface friction angle.

[0073] In this embodiment, Table 1 shows the scratch testing equipment parameters set in step 3.

[0074] Table 1. Scratch testing equipment parameters

[0075] name Parameter range Core diameter 25~200mm Core length 2.0~50.0cm Force sensor range 1～2000N Horizontal displacement resolution 0.1mm Cutting depth resolution 0.001mm Sample data resolution 10pts / mm

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

[0077] 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.

[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 Indicates compressive strength; σ t E represents tensile strength; V represents Young's modulus; K represents Poisson's ratio; IC This indicates fracture toughness.

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

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

[0086] The wavelet basis functions are expressed as follows:

[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 length of the signal sampling window.

[0089] Specifically, L 2 (R) Taking the inner product of the mechanical data function x(t) in space with the wavelet basis functions, we obtain the continuous wavelet transform function:

[0090]

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

[0092] The relationship between the scaling factor and the signal frequency is as follows: the smaller the scaling factor, the more compressed the wavelet is, which measures the detail of the signal and corresponds to the high-frequency signal of the data; the larger the scaling factor, the more stretched the wavelet is, which measures the coarserness of the signal and corresponds to the low-frequency signal of the data.

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

[0094] Specifically, the signal is decomposed using the Mahlert algorithm. Let the signal to be processed be f(t), and in V... j There is an approximation in space. Let ψ represent the scaling function. j,k (t) represents the wavelet function, and {ψ j,k (t), k∈Z} are V j and W j If the orthogonal product is {t0, t1, ..., t}, then f(t) = {t0, t1, ..., t} j It can be decomposed into:

[0095]

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

[0097] f(t) is simplified by an infinite matrix, where get:

[0098]

[0099] In the formula, H n,k and G n,k This represents the result of the convolution.

[0100] Figure 4 This is a schematic diagram of the decomposition of the Mahlert algorithm provided in the embodiments of this application.

[0101] Figure 5 This is a schematic diagram of the reconstruction of the Mahlert algorithm provided in an embodiment of this application.

[0102] Specifically, after the signal f(t) is decomposed into different resolution levels for analysis and processing, the functions located at different resolution levels are superimposed to obtain V again. j Given the representation in the image, Mallet's reconstruction algorithm is as follows:

[0103]

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

[0105] Table 2 Parameters of the Three-Dimensional Mechanical Model

[0106]

[0107]

[0108] Figure 7 This is a schematic diagram of the mechanical model corresponding to each mechanical parameter provided in the embodiments of this application.

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

[0110] Finally, it should be noted that other embodiments of the invention will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This invention is intended to cover any variations, uses, or adaptations of the invention that follow the general principles of the invention and include common knowledge or customary techniques in the art not disclosed herein, and is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of the invention is limited only by the appended claims.

Claims

1. A refined fracturing geological modeling method based on scratch testing, characterized in that, include: Step 1: Obtain core samples from the reservoir well and cut the core samples to obtain core samples with flat surfaces; Step 2: Calibrate the core sample on the flat surface to obtain the calibrated core sample; Step 3: Perform a scratch test on the calibrated core sample using a scratch testing device to generate scratch test data. This scratch test data includes, but is not limited to, shear stress, normal stress, scratch depth, and the width of the scratching tool. The formulas for calculating the shear stress, normal stress, scratch depth, and the width of the scratching tool are as follows: In the formula, Indicates shear stress; Indicates normal stress; This indicates the inherent specific work required for rock fragmentation. It represents the ratio of normal stress to shear stress; Indicates the cross-sectional area of ​​the scribed surface; Indicates the width of the carving knife; Indicates the depth of the scratch; Indicates the blade tilt angle; Indicates the interface friction angle; Step 4: Calculate and generate mechanical data based on the scratch test data. This mechanical data includes, but is not limited to, compressive strength, tensile strength, Young's modulus, Poisson's ratio, and fracture toughness. The formulas for calculating the compressive strength, tensile strength, Young's modulus, Poisson's ratio, and fracture toughness are: In the formula, Indicates compressive strength; Indicates tensile strength; Indicates Young's modulus; Indicates Poisson's ratio; Indicates fracture toughness; Step 5: Perform wavelet transform on the scratch test data and the mechanical data to generate a feature dataset for the 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 400mm×80mm×70mm.

3. The method according to claim 1, characterized in that, The core samples in step 1 include mudstone and fine sandstone interbedded samples, sandstone and mudstone interbedded samples, fine sandstone mudstone interlayer samples, and grayish-brown oily fine sandstone samples.

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

5. The method according to claim 1, characterized in that, Step 5: Perform wavelet transform on the scratch test data and the mechanical data to generate a feature dataset for the reservoir fracturing mechanical model, including: The mechanical data are multiplied by the wavelet basis functions to obtain the continuous wavelet transform function; The mechanical data is decomposed and reconstructed using the Mahlert algorithm to obtain the decomposed and reconstructed dataset; The characteristic dataset of the reservoir fracturing mechanics model is obtained by analyzing the continuous wavelet transform function and the decomposed and reconstructed dataset.

6. The method according to claim 5, characterized in that, The continuous wavelet transform function is: In the formula, Indicates the scaling factor. Indicates the translation factor; Represents wavelet basis function operations; A function representing mechanical data.

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