A method for predicting the acid-etched surface topography

Abstract

The present invention discloses a method for predicting the acid-etched surface topography, belonging to the field of predicting the acid-etched surface topography. The method includes fitting the relationship between the mineral content and the height reduction of the flat end face, namely the prediction model for the height reduction of the flat rock surface; establishing a prediction model for the height reduction of the rough fracture surface; integrating the prediction model for the height reduction of the flat rock surface and the prediction model for the height reduction of the rough fracture surface to obtain a prediction model for the acid-etched surface topography of carbonate rocks; determining the initial random parameters based on the mineral content of the carbonate rock to be measured; according to the initial random parameters, using the diamond-square algorithm to calculate and according to the concavity-convex coefficient of the carbonate rock to be measured, using the prediction model for the acid-etched surface topography to predict the height reduction of the carbonate rock to be measured. The present invention solves the problems that the existing methods often ignore the change of the wall surface topography with time under the acid-etching action and the influence of the non-uniform etching caused by different rock minerals and different original surface topographies on the surface topography.

Description

Technical Field

[0001] The present invention belongs to the field of prediction of acid-etched surface topography, and particularly relates to a method for predicting acid-etched surface topography. Background Art

[0002] With the continuous development of oil and gas exploration technologies, more and more deep carbonate rock oil and gas reservoirs have been put into development. Such reservoirs are buried relatively deep, the formation closure stress is large, and the artificial fractures after acid fracturing are prone to closure and cannot maintain long-term conductivity. And the acid-etched surface topography determines the conductivity of the fractures.

[0003] At present, the distribution of carbonate rocks in sedimentary rocks has accounted for 20% of the total area, and the total oil and gas production is about 60% from carbonate rocks. There are already multiple carbonate rock oil and gas-bearing basins mainly composed of large and extra-large oil and gas fields. With the increasing demand for oil and natural gas, as well as the improvement of exploration and development technical levels, carbonate rock reservoirs have become the key research objects of current exploration and development. However, they still face problems such as deep burial, low porosity and permeability, strong formation heterogeneity, and great development difficulty. It is necessary to implement stimulation measures to achieve efficient exploitation of oil and gas resources.

[0004] Regarding the stimulation technology for carbonate rock reservoirs, acid fracturing is still the most widely used and effective means. At present, the development of acid fracturing technology mainly reflects in three aspects: mechanism research, acid fluid system, and construction technology. At present, a large number of studies on acid-rock reaction of carbonate rocks have been carried out by using methods such as experimental research, theoretical research, and numerical simulation, including: analysis of acid-etching micro-mechanism, macro-mechanism, acid-rock reaction experiment, evaluation of acid fluid system, characterization of acid-etched fracture topography and fracture wall topography, calculation of acid-etching effective action distance, acid-etched fracture conductivity, establishment of acid-rock reaction kinetic theory model, etc. During the acid fracturing stimulation process of carbonate rock reservoirs, the acid fluid will form an acid-etched wall surface under the etching action on the surface of the fracturing fracture, and the change of the acid-etched wall surface topography will have an important impact on the conductivity under high closure stress. The existing research on acid-rock reaction of rough fracture wall topography still needs to be improved, often ignoring the change of wall surface topography with time under acid-etching action and the influence of non-uniform etching caused by different rock minerals and different original surface topographies on the surface topography. Summary of the Invention

[0005] Aiming at the above deficiencies in the prior art, the acid-etched surface topography prediction method provided by the present invention solves the problem that the existing methods often ignore the change of wall surface topography with time under acid-etching action and the influence of non-uniform etching caused by different rock minerals and different original surface topographies on the surface topography.

[0006] To achieve the above invention objective, the technical solution adopted by the present invention is as follows: An acid etching surface topography prediction method, comprising the following steps:

[0007] S1. Obtain a number of carbonate rock samples, determine the mineral components and contents of each carbonate rock sample, and make them into standard plunger samples;

[0008] S2. Conduct acid-rock reaction experiments on the flat end faces of each standard plunger sample, and use the multiple linear regression method to fit the relationships between time, flow rate, concentration, calcite content, dolomite content, other mineral contents and the descent heights of each point on the flat end face, that is, the prediction model for the descent height of the flat rock surface;

[0009] S3. Conduct acid fluid flow experiments on the rough fracture surfaces of each standard plunger sample split along the axis, and establish a prediction model for the descent height of the rough fracture surface according to the relationship between the descent height of each point on the rough fracture surface after acid etching and the concavo-convex coefficient of each point on the rough fracture surface;

[0010] S4. Integrate the prediction model for the descent height of the flat rock surface and the prediction model for the descent height of the rough fracture surface to obtain the acid etching surface topography prediction model of the carbonate rock;

[0011] S5. Calculate the three-dimensional fractal dimension of the rough fracture surface before acid etching by using the box dimension method, and use the multiple linear regression method to fit the relationship between the mineral content and the three-dimensional fractal dimension, and perform power function fitting on the three-dimensional fractal dimension and the random parameters in the random fractal theory to obtain the corresponding random numbers under different fractal dimensions;

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

[0013] S7. According to the initial random parameters, use the diamond-square algorithm to generate the elevation data of the rough fracture surface of the carbonate rock to be measured before acid etching, calculate the concavo-convex coefficient of the carbonate rock to be measured according to the elevation data, and use the acid etching surface topography prediction model of the carbonate rock to predict the descent height of each point of the carbonate rock to be measured. Based on the elevation data of the rough fracture surface of the carbonate rock to be measured before acid etching and considering the descent height of each point of the carbonate rock to be measured, obtain the surface topography of the rock sample after acid etching.

[0014] Further, the expression of the prediction model for the descent height of the flat rock surface in step S2 is:

[0015]

[0016] Wherein, H1 is the descending height of the flat rock surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient related to the calcite content after fitting; a3 is the correlation coefficient related to the dolomite content after fitting; a4 is the correlation coefficient related to the content of other minerals 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 rate; t is the time; τ1 is the flow rate index after fitting; τ2 is the concentration index after fitting; τ3 is the time index after fitting.

[0017] Further, the specific steps in step S3 are as follows:

[0018] S301. Split each standard plunger sample along the axis.

[0019] S302. Obtain the 3D point cloud data of the rough crack surface of each standard plunger sample before acid etching through three-dimensional scanning, and obtain the surface average height of each standard plunger sample before the acid fluid flow experiment on the rough crack surface according to the 3D point cloud data of the rough crack surface before acid etching.

[0020] S303. Conduct an acid fluid flow experiment on the rough crack surface of each standard plunger sample, and establish a three-dimensional coordinate system with the surface average height of each standard plunger sample before the acid fluid flow experiment on the crack surface as the reference plane.

[0021] S304. Take the X-axis as a fixed value, obtain the change amount of the Z-axis value of each point on the rough crack surface of each standard plunger sample before and after the acid fluid flow experiment relative to the Y-axis value, and obtain the descending height of each point on the rough crack surface after acid etching.

[0022] S305. Obtain the concavo-convex coefficient of each point on the rough crack surface according to the descending height of each point on the rough crack surface after acid etching and the surface average height of each standard plunger sample before the acid fluid flow experiment on the rough crack surface.

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

[0024] Further, the expression of the prediction model for the descending height of the rough crack surface in step S306 is as follows:

[0025]

[0026] Wherein, H2 is the descending height of the rough crack surface; b1 is the quadratic term coefficient after fitting; is the concavo-convex coefficient of the measuring point on the rough crack surface; Z is the Z-axis value of the measuring point on the rough crack surface before the acid fluid flow experiment on the rough crack surface; is the average surface height before the acid fluid flow experiment on the rough fracture surface for the measurement points; b2 is the coefficient of the first-order term after fitting; b3 is the constant after fitting.

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

[0028]

[0029]

[0030] where, H is the descent height of the measurement point on the carbonate rock; H1 is the descent height of the flat rock surface; H' is the influence coefficient of the concavity and convexity on the descent height; d1 is the quadratic term coefficient of the concavity and convexity coefficient after fitting; d2 is the first-order term coefficient of the concavity and convexity coefficient after fitting; d3 is the constant term of the concavity and convexity coefficient; is the concavity and convexity coefficient of the measurement point on the rough fracture surface; Z is the Z-axis value before the acid fluid flow experiment on the rough fracture surface for the measurement point; is the average surface height before the acid fluid flow experiment on the rough fracture surface for the measurement point; a1 is the comprehensive coefficient after fitting; a2 is the calcite content-related coefficient after fitting; a3 is the dolomite content-related coefficient after fitting; a4 is the other mineral content-related coefficient after fitting; α is the calcite content; β is the dolomite content; θ is the other mineral content; c is the acid fluid concentration; v is the flow rate; t is the time; τ1 is the flow rate exponent after fitting; τ2 is the concentration exponent after fitting; τ3 is the time exponent after fitting; b1 is the quadratic term coefficient after fitting; b2 is the first-order term coefficient after fitting; b3 is the constant after fitting.

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

[0032] D = k1 + k2x + k3y

[0033]

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

[0035] Further, in the rhombus-square algorithm in step S7, a roughness constant K is introduced to constrain the random parameters. After each replacement is completed, the random parameters are reduced to d×2 (-K) as the random parameters for the next replacement.

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

[0037] Figure 1 is a flowchart of the method of the present invention.

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

[0039] Figure 3 is a schematic diagram showing the influence of various factors on the descent height of the acid-etched surface in the embodiment of the present invention.

[0040] Figure 4 is a schematic diagram of the rock sample used in the acid fluid flow experiment on the fracture surface in the embodiment of the present invention. DETAILED DESCRIPTION OF THE INVENTION

[0041] The following describes the specific embodiments of the present invention to facilitate those skilled in the art of the present technology to understand the present invention. However, it should be clear that the present invention is not limited to the scope of the specific embodiments. For those of ordinary skill in the art of the present technology, as long as various changes are within the spirit and scope of the present invention defined and determined by the appended claims, these changes are obvious, and all inventions created using the concept of the present invention are within the scope of protection.

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

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

[0044] S2. Conduct acid-rock reaction experiments on the flat end faces of each standard plunger sample, and use the multiple linear regression method to fit the relationships between time, flow rate, concentration, calcite content, dolomite content, other mineral content and the descent height of each point on the flat end face, that is, the prediction model for the descent height of the flat rock surface;

[0045] S3. Conduct acid fluid flow experiments on each standard plunger sample split axially. Based on the relationship between the drop height at each point on the rough fracture surface after acid etching and the concavity coefficient at each point on the rough fracture surface, establish a prediction model for the drop height of the rough fracture surface.

[0046] S4. Integrate the prediction model for the drop height of the flat rock surface and the prediction model for the drop height of the rough fracture surface to obtain a prediction model for the acid-etched surface morphology of carbonate rocks.

[0047] S5. Calculate the three-dimensional fractal dimension of the rough fracture surface before acid etching using the box dimension method, fit the relationship between the mineral content and the three-dimensional fractal dimension using the multiple linear regression method, and perform power function fitting on the three-dimensional fractal dimension and the random parameters in the random fractal theory to obtain the corresponding random numbers at different fractal dimensions.

[0048] S6. Determine the 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 the initial random parameters based on the three-dimensional fractal dimension of the carbonate rock to be tested and the corresponding random numbers at different fractal dimensions.

[0049] S7. According to the initial random parameters, use the diamond-square algorithm to generate the elevation data of the rough fracture surface before acid etching of the carbonate rock to be tested, calculate the concavity coefficient of the carbonate rock to be tested based on the elevation data, and use the prediction model for the acid-etched surface morphology of carbonate rocks to predict the drop height at each point of the carbonate rock to be tested based on the concavity coefficient of the carbonate rock to be tested. Considering the drop height at each point of the carbonate rock to be tested based on the elevation data of the rough fracture surface before acid etching of the carbonate rock to be tested, obtain the surface morphology of the rock sample after acid etching.

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

[0051]

[0052] Among them, H1 is the drop height of the flat rock surface; a1 is the fitted comprehensive coefficient; a2 is the correlation coefficient related to the calcite content after fitting; a3 is the correlation coefficient related to the dolomite content after fitting; a4 is the correlation coefficient related to the content of other minerals 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 rate; t is the time; τ1 is the flow rate index after fitting; τ2 is the concentration index after fitting; τ3 is the time index after fitting.

[0053] In this embodiment, the rock samples used for the acid-rock reaction experiment are as Figure 2 shown. The acid-rock reaction experiment is 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 is used to explore the relationship between the mineral content and the drop height.

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

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

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

[0057] In this embodiment, the prediction model for the height reduction of the flat rock surface obtained 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] Specifically in step S3:

[0061] S301. Split each standard plunger sample along the axial direction;

[0062] S302. Respectively obtain the 3D point cloud data of the rough crack surface of each standard plunger sample before acid etching through three-dimensional scanning, and obtain the surface average height of each standard plunger sample before the acid solution flow experiment on the rough crack surface according to the 3D point cloud data of the rough crack surface before acid etching;

[0063] S303. Conduct an acid solution flow experiment on the rough crack surface of each standard plunger sample, and establish a three-dimensional coordinate system with the surface average height of each standard plunger sample before the acid solution flow experiment on the crack surface as the reference plane;

[0064] S304. Take the X-axis as a fixed value, obtain the change amount of the Z-axis value of each point of each standard plunger sample before and after the acid solution flow experiment on the rough crack surface relative to the Y-axis value, and obtain the height reduction of each point on the rough crack surface after acid etching;

[0065] ​S305. Obtain the concavity and convexity coefficients of each point on the rough fracture surface based on the drop height of each point on the rough fracture surface after acid etching and the average surface height before the acid fluid flow experiment on each standard plunger sample.

[0066] S306. Establish a prediction model for the drop height of the rough fracture surface based on the relationship between the drop height of each point on the rough fracture surface after acid etching and the concavity and convexity coefficients of each point on the rough fracture surface.

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

[0068]

[0069] where, H2 is the drop height of the rough fracture surface; b1 is the quadratic term coefficient after fitting; is the concavity and convexity coefficient of the measurement point on the rough fracture surface; Z is the Z-axis value before the acid fluid flow experiment on the measurement point on the rough fracture surface; is the average surface height before the acid fluid flow experiment on the measurement point on the rough fracture surface; b2 is the linear term coefficient after fitting; b3 is the constant after fitting.

[0070] In this embodiment, the rock samples for the acid fluid flow experiment on the fracture surface are as Figure 4 shown.

[0071] The prediction model for the drop height of the rough fracture surface of dolomite considering only the concavity and convexity coefficient is:

[0072]

[0073] The prediction model for the drop height of the rough fracture surface of limestone considering only the concavity and convexity coefficient is:

[0074]

[0075] The prediction model for the drop height of the rough fracture surface of intermediate rock considering only the concavity and convexity coefficient is:

[0076]

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

[0078]

[0079]

[0080] Wherein, H is the descent height of the measurement point on the carbonate rock; H1 is the descent height of the flat rock surface; H' is the influence coefficient of the concavity-convex degree on the descent height; d1 is the quadratic term coefficient of the concavity-convex coefficient after fitting; d2 is the linear term coefficient of the concavity-convex coefficient after fitting; d3 is the constant term of the concavity-convex coefficient after fitting; is the concavity-convex coefficient of the measurement point on the rough fracture surface; Z is the Z-axis value of the measurement point on the rough fracture surface before the acid fluid flow experiment on the rough fracture surface; is the average surface height of the measurement point on the rough fracture surface before the acid fluid flow experiment on the rough fracture surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient related to the calcite content after fitting; a3 is the correlation coefficient related to the dolomite content after fitting; a4 is the correlation coefficient related to the content of other minerals after fitting; α is the calcite content; β is the dolomite content; θ is the content of other minerals; c is the acid fluid concentration; v is the flow velocity; t is the time; τ1 is the flow velocity exponent after fitting; τ2 is the concentration exponent after fitting; τ3 is the time exponent 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 concavity-convex degree on the descent height is specifically the ratio of the descent height prediction model of the rough fracture surface considering only the concavity-convex coefficient to the descent height of the rock surface of the smooth surface.

[0082] The descent height of the rock surface of the smooth surface, that is, when the concavity-convex coefficient is 0, is the value obtained by using the descent height prediction model of the rough fracture surface considering only the concavity-convex coefficient.

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

[0084] D = k1 + k2x + k3y

[0085]

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

[0087] In the step S7, the roughness constant K is introduced in the rhombus-square algorithm to constrain the random parameter. After each replacement is completed, the random parameter is reduced to d×2 (-K) as the random parameter for the next replacement.

Claims

1. A method for predicting the acid-etched surface topography, characterized in that It includes the following steps: S1. Obtain several carbonate rock samples, determine the mineral composition and content of each carbonate rock sample, and make them into standard plunger samples; S2. Conduct acid-rock reaction experiments on the flat end faces of each standard plunger sample, and use the multiple linear regression method to fit the relationships between time, flow rate, concentration, calcite content, dolomite content, other mineral content and the descending height of each point on the flat end face, that is, the prediction model for the descending height of the flat rock surface; S3. Conduct acid fluid flow experiments on the rough fracture surfaces of each standard plunger sample split along the axis, and establish a prediction model for the descending height of the rough fracture surface according to the relationship between the descending height of each point on the rough fracture surface after acid etching and the concavo-convex coefficient of each point on the rough fracture surface; S4. Integrate the prediction model for the descending height of the flat rock surface and the prediction model for the descending height of the rough fracture surface to obtain a prediction model for the acid-etched surface morphology of carbonate rocks; S5. Calculate the three-dimensional fractal dimension of the rough fracture surface before acid etching by the box dimension method, use the multiple linear regression method to fit the relationship between mineral content and the three-dimensional fractal dimension, and perform power function fitting on the three-dimensional fractal dimension and the random parameters in the random fractal theory to obtain the corresponding random numbers under different fractal dimensions; S6. Determine the 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 the initial random parameters based on the three-dimensional fractal dimension of the carbonate rock to be tested and the corresponding random numbers under different fractal dimensions; S7. According to the initial random parameters, use the diamond-square algorithm to generate the elevation data of the rough fracture surface before acid etching of the carbonate rock to be tested, calculate the concavo-convex coefficient of the carbonate rock to be tested according to the elevation data, and use the prediction model for the acid-etched surface morphology of carbonate rocks to predict the descending height of each point of the carbonate rock to be tested. Based on the elevation data of the rough fracture surface before acid etching of the carbonate rock to be tested and considering the descending height of each point of the carbonate rock to be tested, obtain the surface morphology of the rock sample after acid etching.

2. The acid etching surface topography prediction method according to claim 1, characterized in that The expression of the prediction model for the descending height of the flat rock surface in step S2 is: Where, H1 is the descending height of the flat rock surface; a1 is the fitted comprehensive coefficient; a2 is the fitted calcite content-related coefficient; a3 is the fitted dolomite content-related coefficient; a4 is the fitted other mineral content-related coefficient; α is the calcite content; β is the dolomite content; θ is the other mineral content; c is the acid fluid concentration; v is the flow rate; t is the time; τ1 is the fitted flow rate exponent; τ2 is the fitted concentration exponent; τ3 is the fitted time exponent.

3. The acid etching surface topography prediction method according to claim 1, wherein Specifically in step S3: S301. Split each standard plunger sample along the axis; S302. Respectively obtain the 3D point cloud data of the rough fracture surface before acid etching of each standard plunger sample through three-dimensional scanning, and obtain the surface average height of each standard plunger sample before the acid fluid flow experiment on the rough fracture surface according to the 3D point cloud data of the rough fracture surface before acid etching; S303. Conduct acid fluid flow experiments on each standard plunger sample, and establish a three-dimensional coordinate system with the surface average height of each standard plunger sample before the acid fluid flow experiment on the fractured surface as the reference plane; S304. Take the X-axis as a fixed value, obtain the change amount of the Z-axis value of each point relative to the Y-axis value of each standard plunger sample before and after the acid fluid flow experiment on the rough fractured surface, and obtain the descent height of each point on the acid-etched rough fractured surface; S305. According to the descent height of each point on the acid-etched rough fractured surface and the surface average height of each standard plunger sample before the acid fluid flow experiment on the rough fractured surface, obtain the concavo-convex coefficient of each point on the rough fractured surface; S306. According to the relationship between the descent height of each point on the acid-etched rough fractured surface and the concavo-convex coefficient of each point on the rough fractured surface, establish a prediction model for the descent height of the rough fractured surface.

4. The acid etching surface topography prediction method according to claim 3, characterized in that The expression of the prediction model for the descent height of the rough fractured surface in step S306 is: Among them, H2 is the descending height of the rough fracture surface; b1 is the quadratic coefficient after fitting; is the concavo-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 fluid flow experiment on the rough fracture surface; is the average surface height of the measuring point on the rough fracture surface before the acid fluid flow experiment on the rough fracture surface; b2 is the linear coefficient after fitting; b3 is the constant after fitting.

5. The acid etching surface morphology prediction method according to claim 1, characterized in that, Step S4 is specifically based on the prediction model for the descent height of the rough fractured surface, obtain the influence coefficient of the concavo-convex degree on the descent height, and integrate the prediction model for the descent height of the flat rock surface and the prediction model for the descent height of the rough fractured surface based on the influence coefficient of the concavo-convex degree on the descent height to obtain the prediction model for the acid-etched surface morphology of carbonate rocks: Wherein, H is the descending height of the measurement point on the carbonate rock; H1 is the descending height of the flat rock surface; H' is the influence coefficient of the unevenness on the descending height; d1 is the quadratic term coefficient of the unevenness coefficient after fitting; d2 is the linear term coefficient of the unevenness coefficient after fitting; d3 is the constant term of the unevenness coefficient after fitting; is the unevenness coefficient of the measurement point on the rough fracture surface; Z is the Z-axis value of the measurement point on the rough fracture surface before the acid fluid flow experiment on the rough fracture surface; is the average surface height of the measurement point on the rough fracture surface before the acid fluid flow experiment on the rough fracture surface; a1 is the comprehensive coefficient after fitting; a2 is the correlation coefficient related to the calcite content after fitting; a3 is the correlation coefficient related to the dolomite content after fitting; a4 is the correlation coefficient related to the content of other minerals after fitting; α is the calcite content; β is the dolomite content; θ is the content of other minerals; c is the acid fluid concentration; v is the flow velocity; t is the time; τ1 is the flow 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 acid etching surface topography prediction method according to claim 1, characterized in that, The relationships between the mineral content and the three-dimensional fractal dimension and the expressions of the corresponding random numbers under different fractal dimensions in step S5 are: D = k1 + k2x + k3y Where, D is the three-dimensional fractal dimension; k1 is a constant; k2 is the carbonate rock coefficient; x is the maximum carbonate rock content in the 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 the natural constant; z1 is the three-dimensional fractal dimension coefficient; z2 is a constant.

7. The acid etching surface topography prediction method according to claim 1, characterized in that In the rhombus-square algorithm in step S7, a roughness constant K is introduced to constrain the random parameters. After each replacement is completed, the random parameters are reduced to d×2 (-K) as the random parameters for the next replacement.

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