An experimental method for quantitatively evaluating fracture complexity based on fracture morphology

Abstract

The application discloses an experimental method for quantitatively evaluating fracture complexity degree based on fracture morphology, belongs to the field of fracture morphology evaluation after hydraulic fracturing, and comprises the following steps: obtaining a two-dimensional hydraulic fracturing fracture morphology picture; according to different RGB color modes corresponding to a fracture area and a non-fracture area, processing the non-fracture area into black and the fracture area into white for the obtained two-dimensional hydraulic fracturing fracture morphology picture; performing discrete processing on image pixel points, and extracting specific pixel point coordinate values of the fracture area; searching for the position of a main fracture, calculating the range of different values corresponding to each position, i.e. the discrete degree of the values, and corresponding to the degree of secondary fracture offset from the main fracture; calculating the length of the main fracture, summing up the range corresponding to each position, and calculating the fracture complexity degree to evaluate the fracture complexity degree generated by hydraulic fracturing. The method can accurately evaluate the fracture complexity degree.

Description

Technical Field

[0001] This invention belongs to the field of post-fracturing fracture morphology evaluation in hydraulic fracturing, and more specifically, relates to an experimental method for quantitatively evaluating fracture complexity based on fracture morphology. Background Technology

[0002] Currently, significant breakthroughs have been achieved in the exploitation of unconventional oil and gas resources, thanks to continuous advancements in hydraulic fracturing technology. How to fracture oil and gas reservoirs and what form of fractures to produce have become direct tasks in reservoir stimulation. Only by ensuring thorough and sufficient stimulation (matching the oil and gas occurrence state with the fracture morphology) can high oil and gas production be achieved. Therefore, evaluating the morphology of hydraulic fractures after fracturing is beneficial for subsequent adjustments to construction parameters.

[0003] In existing technologies, oilfield operations primarily utilize microseismic detection. This involves acquiring seismic fracture data from the target reservoir using microseismic methods, extracting attribute data from the fracture data, and evaluating the complexity of the fractures based on extracted parameters such as fracture shape and orientation. The main characterization methods are SRV (Reservoir Stimulation Volume) and FCI (Fractured Complexity Index). SRV is a function of the fracture network half-fracture length, fracture width, and fracture spacing. FCI is primarily the ratio of fracture width to length. Chinese invention patent CN104297783A discloses a method and system for interpreting microseismic events in hydraulic fracturing, mainly focusing on statistically analyzing microseismic events to facilitate subsequent fracture morphology interpretation. Chinese invention patent CN114169261A discloses a multi-fracture parameter inversion method and device based on G-function curve analysis to quickly solve for fracture parameters. This method primarily uses bottom-hole pressure data after hydraulic fracturing pump shutdown to invert fracture length, fracture width, and filtration coefficient to rapidly solve for fracture parameters and ultimately evaluate the fracturing effect. Chinese invention patent CN105089597A discloses a method for evaluating fracture complexity. It primarily evaluates the complexity of fractures generated by multiple fracturing wells by obtaining the volume of fracturing fluid used in the fracturing well, as well as the volumes of horizontal and vertical fractures generated during fracturing, using the multi-fracture coefficient and fracture complexity index. Chinese invention patent CN112304770A discloses a method and system for quantitatively analyzing post-fracturing fracture complexity, mainly used in the shale gas fracturing field. This method quantitatively evaluates post-fracturing fracture complexity by using the number of peaks in the GdP / dG curve, the maximum slope of the first part of the GdP / dG curve, and the standard deviation of the second part. Chinese invention patent CN111779477A discloses a dynamic evaluation method for hydraulic fracture complexity based on fractal theory. It mainly involves quantifying the fractures extracted from fracturing simulations, characterizing fracture complexity based on fracture density, characterizing fracture complexity based on the energy level dispersion of event points, and finally evaluating fracture complexity based on fractal theory. The Chinese literature "A Quantitative Characterization of Complex Fractures in Shale Volumetric Fracturing" (Petroleum and Gas Geology, 2017, Issue 1) discloses a method for quantitatively characterizing complex fractures using the fracture potential index, which involves weighting the fracture fractal dimension corresponding to each angle within the complex fracture system with the production contribution ratio corresponding to that angle. The Chinese literature "Evaluation Method for the Expansion Scale of Hydraulic Fracture Networks in Shale Reservoirs" (Evaluation Method for the Expansion Scale of Hydraulic Fracture Networks in Shale Reservoirs, 2014, Issue 6) discloses a method for evaluating fracturing effectiveness using the fracture connectivity area (SRA), which is the sum of the fracture surface areas (including the main fracture and natural fractures and bedding planes connecting hydraulic fractures) in the post-fracturing sample. A larger SRA indicates better production enhancement and stabilization effects.

[0004] Existing technologies have several limitations: most rely on macroscopic construction parameters, such as pump pressure curves, fracturing fluid volume, fracture length, and fracture width, to indirectly infer fracture complexity. They cannot directly characterize fracture complexity through fracture morphology, nor can they accurately reflect the complexity of the microscopic fracture network, including the role of secondary fractures. Theoretically, the more secondary fractures, the more complex the fracture. Therefore, there is an urgent need for a method that can accurately and quantitatively characterize fracture complexity through fracture morphology. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide an experimental method for quantitatively evaluating the complexity of cracks based on crack morphology, so as to accurately evaluate the complexity of cracks.

[0006] The technical solution adopted by this invention to achieve the above objectives is: an experimental method for quantitatively evaluating the complexity of cracks based on crack morphology, the method comprising:

[0007] Step 1: Obtain two-dimensional images of hydraulic fracturing fracture morphology;

[0008] Step 2: Perform grayscale processing on the image;

[0009] For the obtained two-dimensional hydraulic fracturing fracture morphology images, the non-fractured areas are processed into black and the fractured areas are processed into white, according to the different RGB color modes corresponding to the fractured and non-fractured areas.

[0010] Step 3: Discretize the image pixels and extract the specific pixel coordinates of the crack area;

[0011] Step 4: Search for the location of the main fracture and calculate the range R of the different y values ​​corresponding to each x position. This range represents the dispersion of the y values, corresponding to the degree to which the secondary fracture deviates from the main fracture. R = y max -y min y max This represents the maximum value of y at position x. min This represents the minimum value of the y-value corresponding to the x-position;

[0012] Step 5: Calculate the length len(x) of the main crack and sum(R) of the range R of y at each x position to calculate the crack complexity. The complexity of the fractures generated by hydraulic fracturing is evaluated.

[0013] Furthermore, step 3 specifically includes: based on the size of the pixel values ​​of the image after grayscale processing, i.e. the corresponding image dimensions, the image is discretized into points with a pixel value of 1, the white crack area with RGB (255, 255, 255) values ​​is extracted, and then the specific pixel coordinate values ​​of the crack area are extracted.

[0014] Furthermore, in step 4, the location of the main crack is searched, and the range R of the different y values ​​corresponding to each x position is calculated, which represents the dispersion of the y values. Specifically, this includes:

[0015] For the extracted pixel coordinates, an initial x-position is selected. For each corresponding y-value, the two points that deviate most from the center point are chosen, and the average value of these two points is calculated. This represents the ordinate value of the main crack corresponding to the initial x-position, i.e. The difference between these two points represents the degree to which the secondary crack deviates from the primary crack, i.e., the range R = y. max -y min The range R is calculated sequentially for each x position.

[0016] Furthermore, in step 5, the process of calculating the length len(x) of the main crack is as follows:

[0017] By selecting the x and y coordinates of the initial x position Based on the two-dimensional Euclidean distance formula Where x1 is the x-coordinate of the first position x, x2 is the x-coordinate of the second position x, y1 is the y-coordinate of the first position x, y2 is the y-coordinate of the second position x, and d is the distance between the two points. The distances corresponding to any two adjacent x positions are calculated in turn, and these distances are summed to calculate sum(d), which is the length len(x) corresponding to the main crack.

[0018] The experimental method for quantitatively evaluating the complexity of a fracture based on its fracture morphology uses the fracture complexity ε to characterize the complexity of the fracture, i.e., the degree to which the secondary fractures corresponding to the fracture morphology deviate from the main fracture. The greater the fracture complexity, the more obvious the branch fractures are generated during the fracturing process. The less complex the fracture, the smaller the proportion of branch fractures generated during the fracturing process.

[0019] Through the above design scheme, the present invention can bring the following beneficial effects: The present invention provides an experimental method for quantitatively evaluating the complexity of fractures based on fracture morphology. By intuitively and quantitatively characterizing the complexity of fractures through the fracture morphology of hydraulic fracturing fractures, compared with the traditional method of indirectly evaluating the complexity of fractures through fracturing parameters, this method quantitatively evaluates the complexity of fractures by directly observing the fracture morphology and combining mathematical and artificial intelligence image processing methods. This enables a direct, accurate and rapid quantitative evaluation of the fracturing effect. Attached Figure Description

[0020] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0021] Figure 1 A flowchart of an experimental method for quantitatively evaluating crack complexity based on crack morphology, provided by an example of the present invention, is shown.

[0022] Figure 2 A schematic diagram of the morphology of a two-dimensional hydraulic fracturing fracture after grayscale processing is shown in the example of the present invention. Detailed Implementation

[0023] To provide a more detailed description of the technical features, objectives, and effects of the present invention, a comparison is now made with... Figure 1 and Figure 2 The embodiments of the present invention will be further described below. The accompanying drawings are for illustrative purposes only and do not represent the actual scale or dimensions of the methods described herein. To avoid obscuring the essence of the invention, well-known methods, processes, procedures, and elements are not described in detail.

[0024] The following will introduce an embodiment of the experimental method for quantitatively evaluating crack complexity based on crack morphology proposed in this invention, such as... Figure 1 As shown, the method includes the following steps:

[0025] 1) Obtain two-dimensional images of hydraulic fracturing fracture morphology.

[0026] The main method involves conducting physical or numerical simulation experiments of hydraulic fracturing, and using methods such as CT image processing and high-definition cameras to obtain two-dimensional images of hydraulic fracturing fracture morphology. At the same time, during the physical simulation experiments of hydraulic fracturing, a dye with distinct color is added to the fracturing fluid to facilitate the observation of fracture morphology later.

[0027] 2) Perform grayscale processing on the image.

[0028] The main approach involves processing the acquired two-dimensional hydraulic fracturing fracture morphology images, and then processing the non-fractured areas as black and the fractured areas as white, based on the different RGB color modes corresponding to the fractured and non-fractured areas, to facilitate subsequent numerical processing.

[0029] 3) Discretize the image pixels and extract the specific pixel coordinates of the crack area.

[0030] Based on the size of the pixel values ​​of the image after grayscale processing, i.e. the corresponding image dimensions, the image is discretized into points with a pixel value of 1. The white crack area with RGB (255, 255, 255) values ​​is extracted, and then the specific pixel coordinates of the crack area are extracted.

[0031] 4) Search for the location of the main crack, and calculate the range R = y for each x-position corresponding to different y values. max -y min The dispersion of the y-value is calculated, which represents the degree to which the secondary crack deviates from the main crack.

[0032] R represents the range value, y max This represents the maximum value of y at position x. min This represents the minimum y-value corresponding to the x-position. It is mainly achieved by selecting an initial x-position from the extracted pixel coordinates, and for each corresponding y-value, choosing the two points that deviate most from the center point; the average of these two points is then used. This represents the ordinate value of the main crack corresponding to the initial x-position, i.e. The difference between these two points represents the degree to which the secondary crack deviates from the primary crack, i.e., the range R = y. max -y min The range R is calculated sequentially for each x position.

[0033] 5) Calculate the length len(x) of the main crack and sum(R) the range R of y at each x position. Calculate the crack complexity. The complexity of fractures generated during fracturing in fractured wells is evaluated.

[0034] The process of calculating the length len(x) of the main crack is as follows: by selecting the initial x-coordinate... Based on the two-dimensional Euclidean distance formula Where x1 is the x-coordinate of the first position x, x2 is the x-coordinate of the second position x, y1 is the y-coordinate of the first position x, y2 is the y-coordinate of the second position x, and d is the distance between the two points. The distances corresponding to any two adjacent x positions are calculated in turn, and these distances are summed to calculate sum(d), which is the length len(x) corresponding to the main crack.

[0035] Simultaneously, the range R calculated in 4) is summed. The degree to which secondary fractures deviate from the main fracture per unit length is calculated, which is the required fracture complexity ε. Generally, the greater the fracture complexity, the greater the number of secondary fractures per unit length, proving that hydraulic fracturing is effective.

[0036] The fracture complexity ε can be used to characterize the complexity of the fracture, that is, the degree to which the secondary fractures in the fracture morphology deviate from the main fracture. The greater the fracture complexity, the more obvious the branch fractures are generated during the fracturing process, and the more likely complex fracture networks are to be formed, increasing the drainage area and the volume of the fracturing. The less complex the fracture, the smaller the proportion of branch fractures generated during the fracturing process, and the more obvious single planar fractures are formed. This hydraulic fracturing fracture morphology reduces the drainage area and the volume of the fracturing.

[0037] The following describes a specific application scenario corresponding to the implementation of this invention. The example data comes from the numerical simulation results of hydraulic fracturing in a certain well. Figure 2 The grayscale values ​​represent the fracture morphology regions after hydraulic fracturing, where black areas are non-fractured areas and white areas are fractured areas. Table 1 shows the evaluation method used in this embodiment of the invention, targeting... Figure 2 The morphology of the processed two-dimensional hydraulic fractures is analyzed and evaluated to assess the complexity of the fractures.

[0038] Table 1

[0039]

[0040]

[0041] Table 1 presents the experimental method for determining the complexity of a hydraulic fracture under a certain fracture morphology. As can be seen from Table 1, the fracture complexity calculated in the example is 0.701, which means that the cumulative extension length of the secondary fracture is 0.701 times the length of the main fracture. To a certain extent, this can characterize the complexity of the fracture.

[0042] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the methods and core ideas of this invention. At the same time, those skilled in the art can make similar extensions based on the ideas of this invention without departing from the connotation of the specification. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. An experimental method for quantitatively evaluating crack complexity based on crack morphology, characterized in that, The method includes: Step 1: Obtain two-dimensional images of hydraulic fracturing fracture morphology; Step 2: Perform grayscale processing on the image; For the obtained two-dimensional hydraulic fracturing fracture morphology images, the non-fractured areas are processed into black and the fractured areas are processed into white, according to the different RGB color modes corresponding to the fractured and non-fractured areas. Step 3: Discretize the image pixels and extract the specific pixel coordinates of the crack area; Step 4: Locate the main crack and calculate each The different locations Range of values That is The degree of dispersion of the numerical values ​​corresponds to the extent to which secondary fractures deviate from the primary fracture, where , express The position corresponds to The maximum value of a number. express The position corresponds to The minimum value of a numerical value; Step 5: Calculate the length of the main crack And for each Location corresponding Range of values Summation Calculate the complexity of the crack The complexity of the fractures generated by hydraulic fracturing was evaluated. In step 4, the location of the main crack is searched, and each... The different locations Range of values That is The degree of dispersion of numerical values ​​specifically includes: For the extracted pixel coordinate values, select an initial... Location, for its corresponding different For numerical values, select the two points that deviate most from the center point, and calculate the average of these two points. Indicates the initial The ordinate value of the main crack corresponding to the location, i.e. The difference between these two points represents the degree to which the secondary crack deviates from the main crack, i.e., the range. For each one in turn Position range calculate; In step 5, the length of the main crack is calculated. The process is as follows: By selecting an initial x and y coordinates of the location Based on the two-dimensional Euclidean distance formula ,in For the first position The corresponding x-coordinate value, For the second position The corresponding x-coordinate value, where This is the y-coordinate value corresponding to the first position. For the second position The corresponding ordinate value, Given the distance between two points, calculate the distance between any two adjacent points in sequence. The distances corresponding to the locations are calculated, and these distances are summed. That is, the length corresponding to the main crack. .

2. The experimental method for quantitatively evaluating crack complexity based on crack morphology according to claim 1, characterized in that, Step 3 specifically includes: based on the size of the pixel values ​​of the image after grayscale processing, i.e. the corresponding image dimensions, the image is discretized into points with a pixel value of 1, the white crack area with RGB (255, 255, 255) values ​​is extracted, and then the specific pixel coordinate values ​​of the crack area are extracted.

3. The experimental method for quantitatively evaluating crack complexity based on crack morphology according to claim 1, characterized in that: Crack complexity It can be used to characterize the complexity of a fracture, that is, the degree to which the secondary fractures corresponding to the fracture morphology deviate from the main fracture. The greater the complexity of the fracture, the more obvious the branch fractures are generated during the fracturing process. The less complex the fracture, the smaller the proportion of branch fractures generated during the fracturing process.

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