Acid etching surface topography prediction method

Abstract

The invention discloses an acid etching surface topography prediction method, and belongs to the field of acid etching surface topography prediction.The method comprises the steps that the relation between the mineral content and the flat end face descending height is fitted, and a flat rock surface descending height prediction model is obtained; establishing a descending height prediction model of the rough fracture surface; the flat rock surface descending height prediction model and the rough fracture surface descending height prediction model are integrated, and a carbonate rock acid etching surface appearance prediction model is obtained; determining initial random parameters based on the mineral content of the carbonate rock to be detected; and according to the initial random parameters, calculating by using a rhombus-square algorithm, and according to a concave-convex coefficient of the to-be-measured carbonate rock, predicting the descending height of the to-be-measured carbonate rock by using the acid etching surface topography prediction model. The method solves the problem that an existing method often neglects the change condition of the wall surface morphology along with time under the action of acid etching, the difference of rock minerals and the influence of non-uniform etching generated by different original surface morphologies on the surface morphology.

Description

Technical Field

[0001] The invention belongs to the field of acid-etched surface morphology prediction, and in particular relates to an acid-etched surface morphology prediction method. Background Art

[0002] With the continuous development of oil and gas exploration technology, more and more deep carbonate oil and gas reservoirs are being developed. This type of reservoir is buried deep, the formation closure stress is large, and the artificial fractures after acid fracturing are prone to closure and cannot maintain long-term conductivity. The acid-etched surface morphology determines the conductivity of the fracture.

[0003] At present, the distribution of carbonate rocks in sedimentary rocks has accounted for 20% of the total area, and about 60% of the total oil and gas production comes from carbonate rocks. There are many carbonate oil and gas basins dominated by large and super-large oil and gas fields. With the growing demand for oil and natural gas and the improvement of exploration and development technology, carbonate reservoirs have become the focus of current exploration and development research, but they still face problems such as deep burial, low porosity and permeability, strong formation heterogeneity, and great development difficulties. Production-increasing measures need to be implemented to achieve efficient exploitation of oil and gas resources.

[0004] Acid fracturing is still the most widely used and effective means for the transformation of carbonate reservoirs. At present, the development of acid fracturing technology is mainly reflected in three aspects: mechanism research, acid system, and construction technology. At present, a large number of studies have been conducted on the acid-rock reaction of carbonate rocks using experimental research, theoretical research, and numerical simulation methods, including: acid corrosion micro-mechanism, macro-mechanism analysis, acid-rock reaction experiment, acid system evaluation, characteristic description of acid corrosion crack morphology and crack wall morphology, calculation of effective acid corrosion distance, acid corrosion crack conductivity, and establishment of acid-rock reaction kinetics theoretical model. In the process of acid fracturing and production enhancement of carbonate reservoirs, acid will form an acid corrosion wall under the etching effect of the acid on the surface of the fracture, and the change of the acid corrosion wall morphology will have an important impact on the conductivity under high closure stress. The existing research on the acid-rock reaction of rough fracture wall morphology still needs to be improved, and the change of wall morphology over time after acid corrosion and the influence of non-uniform etching caused by different rock minerals and different original surface morphologies on the surface morphology are often ignored. Summary of the invention

[0005] In view of the above-mentioned deficiencies in the prior art, the present invention provides an acid-etched surface morphology prediction method which solves the problem that the prior methods often ignore the change of the wall morphology over time after acid etching and the influence of non-uniform etching caused by different rock minerals and different original surface morphologies on the surface morphology.

[0006] In order to achieve the above-mentioned object of the invention, the technical solution adopted by the present invention is: a method for predicting the morphology of an acid-etched surface, comprising the following steps:

[0007] S1. Obtain several carbonate rock samples, determine the mineral composition and content of each carbonate rock sample, and make a standard plunger sample;

[0008] S2. Conducting an acid-rock reaction experiment on the flat end surface of each standard plunger sample, and using a multivariate linear regression method to fit the relationship between time, flow rate, concentration, calcite content, dolomite content, other mineral content and the drop height of each point on the flat end surface, i.e., a flat rock surface drop height prediction model;

[0009] S3. Performing acid flow experiments on the rough crack surface of each standard plunger sample split along the axial direction, and establishing a rough crack surface drop height prediction model based on the relationship between the drop height of each point on the rough crack surface after acid etching and the concave-convex coefficient of each point on the rough crack surface;

[0010] S4, integrating the flat rock surface drop height prediction model and the rough fracture surface drop height prediction model to obtain the carbonate rock acid etched surface morphology prediction model;

[0011] S5. The three-dimensional fractal dimension of the rough fracture surface before acid etching is calculated by using the box dimension method, and the relationship between the mineral content and the three-dimensional fractal dimension is fitted by using the multivariate linear regression method, and the three-dimensional fractal dimension and the random parameters in the random fractal theory are fitted with a power function to obtain the corresponding random numbers under different fractal dimensions;

[0012] S6. Determine a carbonate rock to be tested, determine the three-dimensional fractal dimension of the carbonate rock to be tested based on the mineral content of the carbonate rock to be tested, and determine an initial random parameter based on the three-dimensional fractal dimension of the carbonate rock to be tested and the random numbers corresponding to different fractal dimensions;

[0013] S7. According to the initial random parameters, the diamond-square algorithm is used to generate the elevation data of the rough fracture surface of the carbonate rock to be tested before acid etching, the concave-convex coefficient of the carbonate rock to be tested is calculated according to the elevation data, and according to the concave-convex coefficient of the carbonate rock to be tested, the acid-etched surface morphology prediction model of the carbonate rock is used to predict the drop height of each point of the carbonate rock to be tested, based on the elevation data of the rough fracture surface of the carbonate rock to be tested before acid etching, considering the drop height of each point of the carbonate rock to be tested, the surface morphology of the rock sample after acid etching is obtained.

[0014] Furthermore, the expression of the prediction model for the leveling rock surface drop height in step S2 is:

[0015]

[0016] Among them, H1 is the descending height of the flat rock surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient of calcite content after fitting; a3 is the correlation coefficient of dolomite content after fitting; a4 is the correlation coefficient of other mineral contents after fitting; α is the calcite content; β is the dolomite content; θ is the content of other minerals; c is the acid concentration; v is the flow velocity; t is the time; τ1 is the velocity index after fitting; τ2 is the concentration index after fitting; τ3 is the time index after fitting.

[0017] Furthermore, the step S3 specifically includes:

[0018] S301, splitting each standard plunger sample along the axial direction;

[0019] S302, respectively obtaining 3D point cloud data of the rough crack surface of each standard plunger sample before acid etching by three-dimensional scanning, and obtaining the average surface height of the rough crack surface of each standard plunger sample before acid flow test based on the 3D point cloud data of the rough crack surface before acid etching;

[0020] S303, performing an acid flow test on a rough fracture surface of each standard plunger sample, and establishing a three-dimensional coordinate system using the average surface height of each standard plunger sample before the acid flow test on the fracture surface as a reference plane;

[0021] S304, taking the X-axis as a fixed value, obtaining the change of the Z-axis value of each point relative to the Y-axis value before and after the acid flow test on the rough fracture surface of each standard plunger sample, and obtaining the descending height of each point on the rough fracture surface after acid etching;

[0022] S305, obtaining the concavo-convex coefficient of each point on the rough crack surface according to the drop height of each point on the rough crack surface after acid etching and the average surface height of each standard plunger sample before the acid flow test on the rough crack surface;

[0023] S306. Establish a prediction model for the drop height of the rough crack surface based on the relationship between the drop height of each point on the rough crack surface after acid etching and the concave-convex coefficient of each point on the rough crack surface.

[0024] Furthermore, the expression of the drop height prediction model of the rough crack surface in step S306 is:

[0025]

[0026] Where H2 is the drop height of the rough crack surface; b1 is the quadratic term coefficient after fitting; is the concave-convex coefficient of the measuring point on the rough fracture surface; Z is the Z-axis value of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; is the average surface height of the measuring point on the rough fracture surface before the acid flow experiment on the rough fracture surface; b2 is the linear coefficient after fitting; b3 is the constant after fitting.

[0027] Furthermore, step S4 is specifically to obtain the influence coefficient of the degree of concavity and convexity on the descent height based on the descent height prediction model of the rough fracture surface, and integrate the descent height prediction model of the flat rock surface and the descent height prediction model of the rough fracture surface based on the influence coefficient of the degree of concavity and convexity on the descent height to obtain the acid-etched surface morphology prediction model of carbonate rock:

[0028]

[0029]

[0030] Among them, H is the drop height of the measuring point on the carbonate rock; H1 is the drop height of the flat rock surface; H' is the influence coefficient of the concave-convex degree on the drop height; d1 is the quadratic term coefficient of the concave-convex coefficient after fitting; d2 is the linear term coefficient of the concave-convex coefficient after fitting; d3 is the constant term of the concave-convex coefficient after fitting; is the concave-convex coefficient of the measuring point on the rough fracture surface; Z is the Z-axis value of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; is the average surface height of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient of calcite content after fitting; a3 is the correlation coefficient of dolomite content after fitting; a4 is the correlation coefficient of other mineral contents after fitting; α is the calcite content; β is the dolomite content; θ is the content of other minerals; c is the acid concentration; v is the flow velocity; t is the time; τ1 is the velocity index after fitting; τ2 is the concentration index after fitting; τ3 is the time index after fitting; b1 is the quadratic term coefficient after fitting; b2 is the linear term coefficient after fitting; b3 is the constant after fitting.

[0031] Furthermore, the relationship between the mineral content and the three-dimensional fractal dimension in step S5 and the expressions of the corresponding random numbers under different fractal dimensions are respectively:

[0032] D=k1+k2x+k3y

[0033]

[0034] Among them, D is the three-dimensional fractal dimension; k1 is a constant; k2 is the carbonate rock coefficient; x is the largest carbonate rock content between calcite content and dolomite content; k3 is the non-carbonate rock coefficient; y is the content of other minerals; d is a random parameter; e is a natural constant; z1 is the three-dimensional fractal dimension coefficient; z2 is a constant.

[0035] Furthermore, in step S7, a roughness constant K is introduced into the diamond-square algorithm to constrain the random parameters. After each replacement, the random parameters are reduced to d×2 (-K) As the random parameter for the next replacement.

[0036] The beneficial effects of the present invention are as follows: the present invention reveals the evolution mechanism and related laws of the acid-etched surface drop height and morphology of carbonate rocks, establishes an acid-etched surface drop height prediction model, and a morphology prediction model can be established on this basis, providing guidance for the prediction of acid fracturing construction effects and parameter optimization of deep carbonate rocks. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 The figure is a flow chart of the method of the present invention.

[0038] Figure 2 Schematic diagram of a rock sample used in an acid-rock reaction experiment in an embodiment of the present invention.

[0039] Figure 3 Schematic diagram of the influence of various factors on the height reduction of the acid-etched surface in an embodiment of the present invention.

[0040] Figure 4 Schematic diagram of a rock sample used for a fracture surface acid flow experiment in an embodiment of the present invention. DETAILED DESCRIPTION

[0041] The specific implementation modes of the present invention are described below so that those skilled in the art can understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific implementation modes. For those of ordinary skill in the art, as long as various changes are within the spirit and scope of the present invention as defined and determined by the attached claims, these changes are obvious, and all inventions and creations utilizing the concept of the present invention are protected.

[0042] like Figure 1 As shown, in one embodiment of the present invention, a method for predicting the morphology of an acid-etched surface comprises the following steps:

[0043] S1. Obtain several carbonate rock samples, determine the mineral composition and content of each carbonate rock sample, and make a standard plunger sample;

[0044] S2. Conducting an acid-rock reaction experiment on the flat end surface of each standard plunger sample, and using a multivariate linear regression method to fit the relationship between time, flow rate, concentration, calcite content, dolomite content, other mineral content and the drop height of each point on the flat end surface, i.e., a flat rock surface drop height prediction model;

[0045] S3. Performing acid flow experiments on the rough crack surface of each standard plunger sample split along the axial direction, and establishing a rough crack surface drop height prediction model based on the relationship between the drop height of each point on the rough crack surface after acid etching and the concave-convex coefficient of each point on the rough crack surface;

[0046] S4, integrating the flat rock surface drop height prediction model and the rough fracture surface drop height prediction model to obtain the carbonate rock acid etched surface morphology prediction model;

[0047] S5. The three-dimensional fractal dimension of the rough fracture surface before acid etching is calculated by using the box dimension method, and the relationship between the mineral content and the three-dimensional fractal dimension is fitted by using the multivariate linear regression method, and the three-dimensional fractal dimension and the random parameters in the random fractal theory are fitted with a power function to obtain the corresponding random numbers under different fractal dimensions;

[0048] S6. Determine a carbonate rock to be tested, determine the three-dimensional fractal dimension of the carbonate rock to be tested based on the mineral content of the carbonate rock to be tested, and determine an initial random parameter based on the three-dimensional fractal dimension of the carbonate rock to be tested and the random numbers corresponding to different fractal dimensions;

[0049] S7. According to the initial random parameters, the diamond-square algorithm is used to generate the elevation data of the rough fracture surface of the carbonate rock to be tested before acid etching, the concave-convex coefficient of the carbonate rock to be tested is calculated according to the elevation data, and according to the concave-convex coefficient of the carbonate rock to be tested, the acid-etched surface morphology prediction model of the carbonate rock is used to predict the drop height of each point of the carbonate rock to be tested, based on the elevation data of the rough fracture surface of the carbonate rock to be tested before acid etching, considering the drop height of each point of the carbonate rock to be tested, the surface morphology of the rock sample after acid etching is obtained.

[0050] The expression of the prediction model for the leveling rock surface drop height in step S2 is:

[0051]

[0052] Among them, H1 is the descending height of the flat rock surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient of calcite content after fitting; a3 is the correlation coefficient of dolomite content after fitting; a4 is the correlation coefficient of other mineral contents after fitting; α is the calcite content; β is the dolomite content; θ is the content of other minerals; c is the acid concentration; v is the flow velocity; t is the time; τ1 is the velocity index after fitting; τ2 is the concentration index after fitting; τ3 is the time index after fitting.

[0053] In this embodiment, the rock sample used in the acid-rock reaction experiment is as follows: Figure 2 The acid-rock reaction experiment was used to explore the influence of different factors (mineral content, time, flow rate, concentration) on the surface morphology, and the handheld laser-induced breakdown spectroscopy was used to explore the relationship between mineral content and descent height.

[0054] like Figure 3 As shown in the figure, it is a schematic diagram of the influence of various factors on the drop height of the acid-etched surface. It can be seen that the 20% concentration acid has a better etching effect on the rock, the etching depth is more obvious, and the drop height is also higher. The 10% concentration acid has a more uniform etching effect, with only a few acid-etched depressions on the rock surface, and the drop height is 0.4 times that of the 20% concentration.

[0055] Comparing different flow rates, the higher the flow rate, the higher the H + The faster the rate of convective mass transfer, the faster the acid-rock reaction rate, the higher the descent height, and the more obvious the acid corrosion morphology.

[0056] The fitting degree between the reaction rate and the drop height reaches 0.899. The faster the acid-rock reaction rate, the greater the drop height, and the greater the difference in the surface acid etching morphology.

[0057] In this embodiment, the obtained flat rock surface drop height prediction model is:

[0058] H1=3.390×10 -7 v 0.5 c 1.015 (0.670α+0.425β-0.522θ)t 1.019

[0059] In this embodiment, the content of other minerals = 1 - the content of dolomite - the content of calcite.

[0060] The step S3 specifically includes:

[0061] S301, splitting each standard plunger sample along the axial direction;

[0062] S302, respectively obtaining 3D point cloud data of the rough crack surface of each standard plunger sample before acid etching by three-dimensional scanning, and obtaining the average surface height of the rough crack surface of each standard plunger sample before acid flow test based on the 3D point cloud data of the rough crack surface before acid etching;

[0063] S303, performing an acid flow test on a rough fracture surface of each standard plunger sample, and establishing a three-dimensional coordinate system using the average surface height of each standard plunger sample before the acid flow test on the fracture surface as a reference plane;

[0064] S304, taking the X-axis as a fixed value, obtaining the change of the Z-axis value of each point relative to the Y-axis value before and after the acid flow test on the rough fracture surface of each standard plunger sample, and obtaining the descending height of each point on the rough fracture surface after acid etching;

[0065] S305, obtaining the concavo-convex coefficient of each point on the rough crack surface according to the drop height of each point on the rough crack surface after acid etching and the average surface height of each standard plunger sample before the acid flow test on the rough crack surface;

[0066] S306. Establish a prediction model for the drop height of the rough crack surface based on the relationship between the drop height of each point on the rough crack surface after acid etching and the concave-convex coefficient of each point on the rough crack surface.

[0067] The expression of the drop height prediction model of the rough crack surface in step S306 is:

[0068]

[0069] Where H2 is the drop height of the rough crack surface; b1 is the quadratic term coefficient after fitting; is the concave-convex coefficient of the measuring point on the rough fracture surface; Z is the Z-axis value of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; is the average surface height of the measuring point on the rough fracture surface before the acid flow experiment on the rough fracture surface; b2 is the linear coefficient after fitting; b3 is the constant after fitting.

[0070] In this embodiment, the rock sample used for the acid flow experiment on the fracture surface is as follows: Figure 4 shown.

[0071] The obtained prediction model of the drop height of the rough fracture surface of dolomite considering only the concave-convex coefficient is:

[0072]

[0073] The obtained prediction model of the drop height of the rough fracture surface of limestone considering only the concave-convex coefficient is:

[0074]

[0075] The obtained prediction model of the drop height of the rough fracture surface of the intermediate rock considering only the concave-convex coefficient is:

[0076]

[0077] The step S4 is specifically to obtain the influence coefficient of the degree of concavity and convexity on the descent height based on the descent height prediction model of the rough fracture surface, and integrate the descent height prediction model of the flat rock surface and the descent height prediction model of the rough fracture surface based on the influence coefficient of the degree of concavity and convexity on the descent height to obtain the acid-etched surface morphology prediction model of carbonate rock:

[0078]

[0079]

[0080] Among them, H is the drop height of the measuring point on the carbonate rock; H1 is the drop height of the flat rock surface; H' is the influence coefficient of the concave-convex degree on the drop height; d1 is the quadratic term coefficient of the concave-convex coefficient after fitting; d2 is the linear term coefficient of the concave-convex coefficient after fitting; d3 is the constant term of the concave-convex coefficient after fitting; is the concave-convex coefficient of the measuring point on the rough fracture surface; Z is the Z-axis value of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; is the average surface height of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient of calcite content after fitting; a3 is the correlation coefficient of dolomite content after fitting; a4 is the correlation coefficient of other mineral contents after fitting; α is the calcite content; β is the dolomite content; θ is the content of other minerals; c is the acid concentration; v is the flow velocity; t is the time; τ1 is the velocity index after fitting; τ2 is the concentration index after fitting; τ3 is the time index after fitting; b1 is the quadratic term coefficient after fitting; b2 is the linear term coefficient after fitting; b3 is the constant after fitting.

[0081] In this embodiment, the influence coefficient of the concave-convex degree on the drop height is specifically the ratio of the drop height prediction model of the rough fracture surface considering only the concave-convex coefficient to the drop height of the rock surface with a smooth surface.

[0082] The drop height of the rock surface on a smooth surface, that is, when the concavity and convexity coefficient is 0, is the value obtained by using the drop height prediction model of the rough fracture surface that only considers the concavity and convexity coefficient.

[0083] The relationship between the mineral content and the three-dimensional fractal dimension in step S5 and the expressions of the corresponding random numbers under different fractal dimensions are:

[0084] D=k1+k2x+k3y

[0085]

[0086] Among them, D is the three-dimensional fractal dimension; k1 is a constant; k2 is the carbonate rock coefficient; x is the largest carbonate rock content between calcite content and dolomite content; k3 is the non-carbonate rock coefficient; y is the content of other minerals; d is a random parameter; e is a natural constant; z1 is the three-dimensional fractal dimension coefficient; z2 is a constant.

[0087] In step S7, the roughness constant K is introduced into the diamond-square algorithm to constrain the random parameters. After each replacement, the random parameters are reduced to d×2 (-K) As the random parameter for the next replacement.

Claims

1. A method for predicting acid-etched surface morphology, characterized in that: The following steps are involved: S1. Obtain several carbonate rock samples, determine the mineral composition and content of each carbonate rock sample, and make a standard plunger sample; S2. Conducting an acid-rock reaction experiment on the flat end surface of each standard plunger sample, and using a multivariate linear regression method to fit the relationship between time, flow rate, concentration, calcite content, dolomite content, other mineral content and the drop height of each point on the flat end surface, i.e., a flat rock surface drop height prediction model; S3. Performing acid flow experiments on the rough crack surface of each standard plunger sample split along the axial direction, and establishing a rough crack surface drop height prediction model based on the relationship between the drop height of each point on the rough crack surface after acid etching and the concave-convex coefficient of each point on the rough crack surface; S4, integrating the flat rock surface drop height prediction model and the rough fracture surface drop height prediction model to obtain the carbonate rock acid etched surface morphology prediction model; S5. The three-dimensional fractal dimension of the rough fracture surface before acid etching is calculated by using the box dimension method, and the relationship between the mineral content and the three-dimensional fractal dimension is fitted by using the multivariate linear regression method, and the three-dimensional fractal dimension and the random parameters in the random fractal theory are fitted with a power function to obtain the corresponding random numbers under different fractal dimensions; S6. Determine a carbonate rock to be tested, determine the three-dimensional fractal dimension of the carbonate rock to be tested based on the mineral content of the carbonate rock to be tested, and determine an initial random parameter based on the three-dimensional fractal dimension of the carbonate rock to be tested and the random numbers corresponding to different fractal dimensions; S7. According to the initial random parameters, the diamond-square algorithm is used to generate the elevation data of the rough fracture surface of the carbonate rock to be tested before acid etching, the concave-convex coefficient of the carbonate rock to be tested is calculated according to the elevation data, and according to the concave-convex coefficient of the carbonate rock to be tested, the acid-etched surface morphology prediction model of the carbonate rock is used to predict the drop height of each point of the carbonate rock to be tested, based on the elevation data of the rough fracture surface of the carbonate rock to be tested before acid etching, considering the drop height of each point of the carbonate rock to be tested, the surface morphology of the rock sample after acid etching is obtained.

2. The method for predicting the acid-etched surface morphology according to claim 1, characterized in that: The expression of the prediction model for the leveling rock surface drop height in step S2 is: Among them, H1 is the descending height of the flat rock surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient of calcite content after fitting; a3 is the correlation coefficient of dolomite content after fitting; a4 is the correlation coefficient of other mineral contents after fitting; α is the calcite content; β is the dolomite content; θ is the content of other minerals; c is the acid concentration; v is the flow velocity; t is the time; τ1 is the velocity index after fitting; τ2 is the concentration index after fitting; τ3 is the time index after fitting.

3. The method for predicting the acid-etched surface morphology according to claim 1, characterized in that: The step S3 specifically includes: S301, splitting each standard plunger sample along the axial direction; S302, respectively obtaining 3D point cloud data of the rough crack surface of each standard plunger sample before acid etching by three-dimensional scanning, and obtaining the average surface height of the rough crack surface of each standard plunger sample before acid flow test based on the 3D point cloud data of the rough crack surface before acid etching; S303, performing an acid flow test on a rough fracture surface of each standard plunger sample, and establishing a three-dimensional coordinate system using the average surface height of each standard plunger sample before the acid flow test on the fracture surface as a reference plane; S304, taking the X-axis as a fixed value, obtaining the change of the Z-axis value of each point relative to the Y-axis value before and after the acid flow test on the rough fracture surface of each standard plunger sample, and obtaining the descending height of each point on the rough fracture surface after acid etching; S305, obtaining the concavo-convex coefficient of each point on the rough crack surface according to the drop height of each point on the rough crack surface after acid etching and the average surface height of each standard plunger sample before the acid flow test on the rough crack surface; S306. Establish a prediction model for the drop height of the rough crack surface based on the relationship between the drop height of each point on the rough crack surface after acid etching and the concave-convex coefficient of each point on the rough crack surface.

4. The method for predicting the acid-etched surface morphology according to claim 3, characterized in that: The expression of the drop height prediction model of the rough crack surface in step S306 is: Among them, H2 is the drop height of the rough crack surface; b1 is the quadratic term coefficient after fitting; is the concave-convex coefficient of the measuring point on the rough fracture surface; Z is the Z-axis value of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; is the average surface height of the measuring point on the rough fracture surface before the acid flow experiment on the rough fracture surface; b2 is the linear coefficient after fitting; b3 is the constant after fitting.

5. The method for predicting the acid-etched surface morphology according to claim 1, characterized in that: The step S4 is specifically to obtain the influence coefficient of the degree of concavity and convexity on the descent height based on the descent height prediction model of the rough fracture surface, and integrate the descent height prediction model of the flat rock surface and the descent height prediction model of the rough fracture surface based on the influence coefficient of the degree of concavity and convexity on the descent height to obtain the acid-etched surface morphology prediction model of carbonate rock: Among them, H is the drop height of the measuring point on the carbonate rock; H1 is the drop height of the flat rock surface; H' is the influence coefficient of the concave-convex degree on the drop height; d1 is the quadratic term coefficient of the concave-convex coefficient after fitting; d2 is the linear term coefficient of the concave-convex coefficient after fitting; d3 is the constant term of the concave-convex coefficient after fitting; is the concave-convex coefficient of the measuring point on the rough fracture surface; Z is the Z-axis value of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; is the average surface height of the measuring point on the rough fracture surface before the acid flow test on the rough fracture surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient of calcite content after fitting; a3 is the correlation coefficient of dolomite content after fitting; a4 is the correlation coefficient of other mineral contents after fitting; α is the calcite content; β is the dolomite content; θ is the content of other minerals; c is the acid concentration; v is the flow velocity; t is the time; τ1 is the velocity index after fitting; τ2 is the concentration index after fitting; τ3 is the time index after fitting; b1 is the quadratic term coefficient after fitting; b2 is the linear term coefficient after fitting; b3 is the constant after fitting.

6. The method for predicting the acid-etched surface morphology according to claim 1, characterized in that: The relationship between the mineral content and the three-dimensional fractal dimension in step S5 and the expressions of the corresponding random numbers under different fractal dimensions are: D=k1+k2x+k3y Among them, D is the three-dimensional fractal dimension; k1 is a constant; k2 is the carbonate rock coefficient; x is the largest carbonate rock content between calcite content and dolomite content; k3 is the non-carbonate rock coefficient; y is the content of other minerals; d is a random parameter; e is a natural constant; z1 is the three-dimensional fractal dimension coefficient; z2 is a constant.

7. The method for predicting the acid-etched surface morphology according to claim 1, characterized in that: In step S7, the roughness constant K is introduced into the diamond-square algorithm to constrain the random parameters. After each replacement, the random parameters are reduced to d×2 (-K) As the random parameter for the next replacement.

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