Method for predicting acid-etched surface morphology

Abstract

This invention provides a method for predicting the morphology of acid-etched surfaces. [Solution] The prediction method involves fitting the relationship between mineral content and the descent height of a flat surface (i.e., including a descent height prediction model for flat rock surfaces). A descent height prediction model for rough fracture surfaces is established. The descent height prediction model for flat rock surfaces and the descent height prediction model for rough fracture surfaces are aligned to obtain a prediction model for the acid etching surface morphology of carbonate rocks. Initial random parameters are determined based on the mineral content of carbonate rocks awaiting measurement. Based on the initial random parameters, the descent height of carbonate rocks awaiting measurement is predicted using the acid etching surface morphology prediction model, calculated using a rhombic-square algorithm and based on the surface roughness coefficient of carbonate rocks awaiting measurement.

Description

Technical Field

[0001] The present invention belongs to the field of predicting the acid etching surface morphology, and particularly relates to a method for predicting the acid etching surface morphology.

Background Art

[0002] With the development of oil and natural gas exploration technologies, an increasing number of deep carbonate rock oil and natural gas reservoirs are being developed. This type of reservoir is 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. The morphology of the acid etching surface determines the conductivity of the fractures.

[0003] Currently, the distribution of carbonate rocks in sedimentary rocks accounts for 20% of the total area, and approximately 60% of the total oil and natural gas production comes from carbonate rocks. There are many carbonate rock oil and natural gas basins mainly composed of large and extra-large oil and natural gas fields. With the increasing demand for oil and natural gas and the improvement of exploration and development technology levels, carbonate rock reservoirs are currently the focus of exploration and development research. However, they face problems such as deep burial, low pore permeability, strong formation heterogeneity, and difficult development. Without implementing production increase measures, high-efficiency extraction of oil and natural gas resources cannot be achieved.

[0004] Acid pressure remains the most widely applied and relatively effective method for the modification of carbonate rock reservoirs. Currently, the development of acid pressure technology is mainly embodied in three aspects: mechanism research, acid-liquid systems, and construction techniques. Extensive research is being conducted on carbonate rock-acid rock reactions using experimental, theoretical, and numerical simulation methods, including micro-mechanism and macro-mechanism analysis of acid etching, acid rock reaction experiments, acid-liquid system evaluation, characteristic description of acid etching crack morphology and crack wall morphology, calculation of the effective action distance of acid etching, flow guidance capacity of acid etching cracks, and construction of theoretical models of acid rock reaction dynamics. In the acid pressure modification process of carbonate rock reservoirs, the acid solution forms acid-etched walls under etching action on the fracture crack surface, and the morphological changes of the acid-etched walls have a significant impact on the flow guidance capacity under high closure stress. Conventional acid-rock reaction studies on rough crack wall morphology still need improvement. They often ignore the changes in wall morphology over time under acid etching, and simultaneously neglect the influence of heterogeneous etching due to differences in rock minerals and primitive surface morphology on the surface morphology. [Overview of the project]

[0005] In response to the aforementioned shortcomings of the prior art, the acid etching surface morphology prediction method provided by the present invention solves the problems of conventional methods, which often ignore the situation in which the wall morphology changes over time due to the acid etching process, and the influence of non-uniform etching due to differences in rock minerals and differences in primitive surface morphology on the surface morphology.

[0006] To achieve the objective of the above invention, the technical solution employed by the present invention is a method for predicting the morphology of an acid-etched surface, and includes the following process. S1. Obtain multiple carbonate rock samples, determine the mineral composition and content of each carbonate rock sample, and prepare a standard plunger sample. S2. Acid rock reaction experiments were performed on the flat end faces of each standard plunger sample, and the relationship between time, flow velocity, concentration, calcite content, dolomite content, other mineral content, and the depth of falloff at each point on the flat end face was fitted using a multivariate linear regression method, thus creating a predictive model for the depth of falloff at the flat rock surface. In S3, an acid flow experiment was performed on each standard plunger sample that cracked along the axial direction. Based on the relationship between the elevation descent at each point on the rough crack surface after acid etching and the roughness coefficient at each point on the rough crack surface, a model for predicting the elevation descent at the rough crack surface was constructed. S4. The prediction models for the depth of falloff on flat rock surfaces and the prediction models for the depth of falloff on rough fracture surfaces are combined to obtain a prediction model for the acid-etched surface morphology of carbonate rocks. In S5, the three-dimensional fractal dimension of the rough crack surface before acid etching is calculated using the box dimension method, the relationship between mineral content and the three-dimensional fractal dimension is fitted using a multivariate linear regression method, and the three-dimensional fractal dimension and the random parameter in random fractal theory are power function fitted to obtain corresponding random numbers for different fractal dimensions. S6. Determine the carbonate rocks awaiting measurement, determine the three-dimensional fractal dimension of the carbonate rocks awaiting measurement based on their mineral content, and determine the initial random parameters based on the three-dimensional fractal dimension of the carbonate rocks awaiting measurement, using random numbers corresponding to different fractal dimensions. In S7, using initial random parameters, elevation data of the rough fracture surface of the carbonate rock awaiting measurement before acid etching is generated using a rhombic-square algorithm. The surface roughness coefficient of the carbonate rock awaiting measurement is calculated from the elevation data, and the descent altitude at each point of the carbonate rock awaiting measurement is predicted using a predictive model for the acid-etched surface morphology of the carbonate rock based on the surface roughness coefficient of the carbonate rock awaiting measurement. The surface morphology of the rock sample after acid etching is obtained by considering the descent altitude at each point of the carbonate rock awaiting measurement, based on the elevation data of the rough fracture surface of the carbonate rock awaiting measurement before acid etching.

[0007] Furthermore, the expression for the model predicting the descent height of the flattened rock surface in step S2 is as follows:

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[0008] Furthermore, the aforementioned process S3 is specifically as follows: S301, each standard plunger sample is divided along the axial direction. In S302, 3D point cloud data of the rough crack surface of each standard plunger sample before acid etching is obtained by three-dimensional scanning, and the average surface hardness of the rough crack surface of each standard plunger sample before acid flow experiment is obtained from the 3D point cloud data of the rough crack surface before acid etching. S303. Acid flow experiments are performed on the rough crack surface of each standard plunger sample, and a three-dimensional coordinate system is established using the average surface hardness of the crack surface of each standard plunger sample before the acid flow experiment as the reference plane. In S304, with the X-axis set to a constant value, the change in the Z-axis value relative to the Y-axis value at each point before and after the acid flow experiment on the rough crack surface of each standard plunger sample is obtained, and the elevation drop at each point on the rough crack surface after acid etching is obtained. In S305, the surface roughness coefficient for each point on the rough crack surface is obtained by comparing the elevation drop at each point on the rough crack surface after acid etching with the average surface elevation of the rough crack surface of each standard plunger sample before the acid flow experiment. S306. A predictive model for the descent height of a rough crack surface is established by examining the relationship between the descent height at each point on the rough crack surface after acid etching and the roughness coefficient at each point on the rough crack surface.

[0009] Furthermore, the expression for the prediction model of the descent height of the rough crack surface in step S306 is as follows:

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[0010] Furthermore, step S4 specifically obtains an influence coefficient of the degree of roughness on the descent height based on a descent height prediction model for a rough fracture surface, and then reconciles the descent height prediction model for a flat rock surface with the descent height prediction model for a rough fracture surface based on the influence coefficient of the degree of roughness on the descent height to obtain a descent height prediction model for the acid etching surface morphology of carbonate rock.

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[0011] Furthermore, the relationship between the mineral content and the three-dimensional fractal dimension in step S5, and the expression formulas of the corresponding random numbers for different fractal dimensions are respectively as follows.

Equation

[0012] Furthermore, a roughness constant K is introduced into the rhombus-square algorithm in step S7 to constrain the random parameters. After each replacement is completed, the random parameters are reduced to d×2 as the random parameters for the next replacement. (-K) for reduction.

[0013] The beneficial effects of the present invention are as follows. The present invention discloses the descent height and the morphological evolution mechanism and related laws of the etched surface of carbonate rock, establishes a descent height prediction model for the etched surface, and on this basis, establishes a morphological prediction model, providing guiding significance for the prediction of the acid fracturing construction effect and parameter optimization of deep carbonate rock.

Brief Description of the Drawings

[0014] [Figure 1] It is a method flowchart of the present invention. [Figure 2] It is a schematic diagram of the rock sample used in the acid-rock reaction experiment in the embodiment of the present invention. [Figure 3] It is a schematic diagram of the influence of each element on the descent height of the etched surface in the embodiment of the present invention. [Figure 4] It is a schematic diagram of the rock sample used in the fracture surface acid fluid flow experiment in the embodiment of the present invention.

Modes for Carrying Out the Invention

[0015] To help those skilled in the art understand the present invention, specific embodiments of the present invention will be described below. However, the present invention is not limited to the scope of these specific embodiments. To an ordinary person skilled in the art, these variations will be obvious as long as they fall within the spirit and scope of the invention as defined and limited by the appended claims. It should be made clear that all inventive creations utilizing the concept of the present invention are subject to protection.

[0016] As shown in Figure 1, in one embodiment of the present invention, the method for predicting the acid-etched surface morphology includes the following steps: S1. Obtain multiple carbonate rock samples, determine the mineral composition and content of each carbonate rock sample, and prepare a standard plunger sample. S2. Acid rock reaction experiments were performed on the flat end faces of each standard plunger sample, and the relationship between time, flow velocity, concentration, calcite content, dolomite content, other mineral content, and the depth of falloff at each point on the flat end face was fitted using a multivariate linear regression method, thus creating a predictive model for the depth of falloff at the flat rock surface. In S3, an acid flow experiment was performed on each standard plunger sample that cracked along the axial direction. Based on the relationship between the descent height at each point on the rough crack surface after acid etching and the roughness coefficient at each point on the rough crack surface, a model for predicting the descent height of the rough crack surface was constructed. S4. The prediction models for the depth of falloff on flat rock surfaces and the prediction models for the depth of falloff on rough fracture surfaces are combined to obtain a prediction model for the acid-etched surface morphology of carbonate rocks. In S5, the three-dimensional fractal dimension of the rough crack surface before acid etching is calculated using the box dimension method, the relationship between mineral content and the three-dimensional fractal dimension is fitted using a multivariate linear regression method, and the three-dimensional fractal dimension and the random parameter in random fractal theory are power function fitted to obtain corresponding random numbers for different fractal dimensions. S6. Determine the carbonate rocks awaiting measurement, determine the three-dimensional fractal dimension of the carbonate rocks awaiting measurement based on their mineral content, and determine the initial random parameters based on the three-dimensional fractal dimension of the carbonate rocks awaiting measurement, using random numbers corresponding to different fractal dimensions. In S7, using initial random parameters, elevation data of the rough fracture surface of the carbonate rock awaiting measurement before acid etching is generated using a rhombic-square algorithm. The surface roughness coefficient of the carbonate rock awaiting measurement is calculated from the elevation data, and the descent altitude at each point of the carbonate rock awaiting measurement is predicted using a predictive model for the acid-etched surface morphology of the carbonate rock based on the surface roughness coefficient of the carbonate rock awaiting measurement. The surface morphology of the rock sample after acid etching is obtained by considering the descent altitude at each point of the carbonate rock awaiting measurement, based on the elevation data of the rough fracture surface of the carbonate rock awaiting measurement before acid etching.

[0017] The expression for the model predicting the descent height on a flat rock surface in step S2 is as follows:

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[0018] In this embodiment, the rock samples used in the acid rock reaction experiment are shown in Figure 2. The acid rock reaction experiment was used to investigate the effects of different factors (mineral content, time, flow rate, and concentration) on surface morphology, and the relationship between mineral content and descent altitude was investigated using a handheld laser-guided fracture spectrum.

[0019] As shown in Figure 3, the influence of each factor on the depth of the acid-etched surface is schematically illustrated. It was found that the etching effect of the 20% concentration acid solution on the rock was better, the etching depth was more pronounced, and the depth of the etching was higher. The etching with the 10% concentration acid solution was relatively average, with only a few acid-etched depressions on the rock surface, and the depth of etching was 0.4 times that of the 20% concentration.

[0020] When comparing different flow velocities, the higher the flow velocity, the higher the H +The rate of convective mass transfer is faster, the reaction rate of acid rocks is faster, the descent altitude is higher, and the acid etching morphology is more pronounced.

[0021] The degree of fitting between reaction rate and descent altitude reaches 0.899, meaning that if the reaction rate of the acid rock is fast, the descent altitude increases accordingly, and the differences in surface acid etching morphology become more pronounced.

[0022] In this embodiment, the obtained model for predicting the descent height of a flat rock surface is as follows:

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[0023] In this embodiment, the content of other minerals is equal to 1 - dolomite content - calcite content.

[0024] The aforementioned process S3 is specifically as follows: S301, each standard plunger sample is divided along the axial direction. In S302, 3D point cloud data of the rough crack surface of each standard plunger sample before acid etching is obtained by three-dimensional scanning, and the average surface hardness of the rough crack surface of each standard plunger sample before acid flow experiment is obtained from the 3D point cloud data of the rough crack surface before acid etching. S303. Acid flow experiments are performed on the rough crack surface of each standard plunger sample, and a three-dimensional coordinate system is established using the average surface hardness of the crack surface of each standard plunger sample before the acid flow experiment as the reference plane. In S304, with the X-axis set to a constant value, the change in the Z-axis value relative to the Y-axis value at each point before and after the acid flow experiment on the rough crack surface of each standard plunger sample is obtained, and the elevation drop at each point on the rough crack surface after acid etching is obtained. In S305, the surface roughness coefficient for each point on the rough crack surface is obtained by comparing the elevation drop at each point on the rough crack surface after acid etching with the average surface elevation of the rough crack surface of each standard plunger sample before the acid flow experiment. S306. A predictive model for the descent height of a rough crack surface is established by examining the relationship between the descent height at each point on the rough crack surface after acid etching and the roughness coefficient at each point on the rough crack surface.

[0025] The expression for the model predicting the descent height of the rough crack surface in the process S306 is as follows:

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[0026] In this embodiment, the rock sample used for the acid flow experiment on the fracture surface is shown in Figure 4. The following is a model for predicting the descent height of a rough crack surface, considering only the roughness coefficient of the obtained dolomite.

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[0027] In this embodiment, the coefficient of influence of the degree of roughness on the descent height is specifically the ratio value of the descent height prediction model for a rough fracture surface that considers only the coefficient of roughness with respect to the descent height of a smooth rock surface.

[0028] The rock surface descent depth for a smooth surface, i.e., when the roughness coefficient is 0, is a value obtained by a rough fracture surface descent depth prediction model that considers only the roughness coefficient.

[0029] The relationship between the mineral content in step S5 and the three-dimensional fractal dimension, and the corresponding random number expressions for different fractal dimensions, are given by the following equations.

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[0030] In step S7, a roughness constant K is introduced into the rhombus-square algorithm to constrain the random parameter, and after each permutation is completed, the random parameter is used as the random parameter for the next permutation in d×2 steps. (-K) It is reduced to this.

Claims

1. A method for predicting the morphology of an acid-etched surface, comprising the following process: S1. Obtain multiple carbonate rock samples, determine the mineral composition and content of each carbonate rock sample, and process each carbonate rock sample into a three-dimensional sample having an axial direction and divisible along that axial direction to produce a standard plunger sample. S2. Acid rock reaction experiments are performed on the flat end face formed at one end of each standard plunger sample. The relationship between the drop in surface position (as the amount of surface position decrease before and after acid etching) at multiple measurement points set on the flat end face, and time, flow velocity, concentration, calcite content, dolomite content, and other mineral content is fitted using multivariate linear regression to generate a predictive model for the drop in flat rock surface for flat end faces, which predicts the drop in the flat end face. S3. An acid flow experiment is performed on the rough crack surface obtained by dividing each standard plunger sample along the axial direction, and a rough crack surface descent height prediction model is constructed that predicts the descent height on the rough crack surface based on the relationship between the descent height at multiple measurement points set on the rough crack surface and the roughness coefficient of the rough crack surface. S4. By applying the coefficient of influence of the unevenness coefficient on the descent height, obtained based on the descent height prediction model for the rough fracture surface, to the descent height prediction model for the flat rock surface for the flat end face, a prediction model for the acid etching surface morphology of carbonate rock is obtained. S5. Using the box dimension method, the three-dimensional fractal dimension of the rough crack surface before acid etching is calculated. Using a multivariate linear regression method, the relationship between mineral content and the three-dimensional fractal dimension is fitted. The three-dimensional fractal dimension and the random parameter in random fractal theory are fitted using a power function to obtain corresponding random numbers for different fractal dimensions. S6. Determine the carbonate rock awaiting measurement, determine the three-dimensional fractal dimension of the carbonate rock awaiting measurement based on its mineral content, and determine the initial random parameters based on the three-dimensional fractal dimension of the carbonate rock awaiting measurement and the corresponding random numbers for the different fractal dimensions. S7. A method for predicting the surface morphology of an acid-etched carbonate rock, characterized by generating elevation data of the rough fracture surface of the carbonate rock before acid etching using a rhombic-square algorithm based on the initial random parameters, calculating the surface roughness coefficient of the carbonate rock before measurement using the elevation data, predicting the altitude descent of each point of the carbonate rock before measurement using a prediction model of the acid-etched surface morphology of the carbonate rock based on the surface roughness coefficient of the carbonate rock before measurement, and reflecting the altitude descent in the elevation data before acid etching to obtain the surface morphology as three-dimensional elevation data of the rough fracture surface of the carbonate rock after acid etching.

2. The method for predicting the acid etching surface morphology according to claim 1, characterized in that the expression for the prediction model of the height of the falloff to the flat rock surface in step S2 is as follows. [Math 1]

3. The process S3 described above is specifically as follows: S301, Each standard plunger sample is divided along the axial direction, S302, 3D point cloud data of the rough crack surface of each standard plunger sample before acid etching is obtained by three-dimensional scanning, and the average surface hardness of the rough crack surface of each standard plunger sample before acid flow experiment is obtained from the 3D point cloud data of the rough crack surface before acid etching. S303, Acid flow experiments were performed on the rough crack surface of each standard plunger sample, and a three-dimensional coordinate system was established using the average surface hardness of the crack surface of each standard plunger sample before the acid flow experiment as the reference plane. In S304, with the X-axis set to a constant value, the change in the Z-axis value relative to the Y-axis value at each point before and after the acid flow experiment on the rough crack surface of each standard plunger sample is obtained, and the elevation drop at each point on the rough crack surface after acid etching is obtained. S305, the surface roughness coefficient of each point on the rough crack surface is obtained by using the elevation drop of each point on the rough crack surface after acid etching and the average surface elevation of the rough crack surface of each standard plunger sample before the acid flow experiment. S306, A method for predicting the morphology of an acid-etched surface according to claim 1, characterized in that a model for predicting the height of descent of the rough crack surface is established based on the relationship between the height of descent of each point on the rough crack surface after acid etching and the coefficient of unevenness of each point on the rough crack surface.

4. The method for predicting the morphology of an acid-etched surface according to claim 3, characterized in that the expression for the prediction model of the descent height of the rough crack surface in step S306 is as follows. [Math 2]

5. The method for predicting the acid etching surface morphology of carbonate rock according to claim 1, characterized in that step S4 obtains a coefficient of influence of the unevenness coefficient on the descent height based on the descent height prediction model for the rough fracture surface, and applies the said influence coefficient to the prediction model of the descent height of the flat rock surface for the flat end face, thereby obtaining a prediction model of the acid etching surface morphology of the carbonate rock. [Math 3]

6. The method for predicting the morphology of an acid-etched surface according to claim 1, characterized in that the relationship between the mineral content in step S5 and the three-dimensional fractal dimension, and the expression for the random numbers corresponding to the different fractal dimensions are as follows. [Math 4]

7. In step S7, the roughness constant K is introduced into the rhombus-square algorithm to constrain the random parameter, and after each permutation is completed, the random parameter is used as the random parameter for the next permutation d × 2 (-K) A method for predicting the acid etching surface morphology according to claim 1, characterized in that it reduces the size to the specified size.

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