Complex lithologic carbonate reservoir reconstruction process determination method

Abstract

The invention discloses a complex lithologic carbonate reservoir reconstruction process determination method, which comprises the following steps: S1, based on the rock mineral composition of a target reservoir, establishing a rock-mineral non-uniform distribution mathematical model; s2, constructing an acid etching hydraulic fracture model with complex lithologic distribution on the basis of non-uniform distribution characteristics of rocks and ores, and calculating the width distribution of fractures after acid etching; s3, establishing a complex lithologic reservoir flow conductivity calculation model based on the acid etching crack width distribution and the experimental test flow conductivity result, and calculating the crack flow conductivity under the reservoir closed pressure condition; and S4, calculating the flow conductivity standard-reaching rate based on the fracture flow conductivity under the reservoir closing pressure, and selecting a reservoir transformation process. According to the method, the influence of the complex rock and ore characteristics of the reservoir on the conductivity of the acid-etched fracture can be considered, and the conductivity obtained through calculation better conforms to the actual working condition, so that the complex lithologic carbonate reservoir transformation process is more accurately determined, and the reservoir transformation effect is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of oil and gas exploitation and geothermal exploitation, and in particular to a method for determining a complex lithology carbonate reservoir transformation process. Background Art

[0002] Carbonate reservoirs are important storage sites for oil and gas resources and geothermal resources. Acid fracturing is a technical tool for increasing oil and gas production in carbonate reservoirs and efficiently exploiting geothermal resources (Guo Jianchun, Gou Bo, Lu Dengyun, et al. Progress and Prospect of Acid Fracturing in Deep Carbonate Reservoirs [J]. Drilling and Production Technology, 2024, 47(2): 121-129; Wang Wei, Fu Hao, Xing Linxiao, et al. Fracture Extension Behavior of Carbonate Geothermal Reservoir Rock Mass Based on Extended Finite Element Method [J]. Earth Science, 2021, 46(10): 3509-3519). The conductivity of fractures formed by acid fracturing technology is the key to affecting the effect of reservoir transformation. Commonly used process methods for constructing conductivity include acid fracturing and hydraulic sand fracturing. Acid fracturing uses acid to non-uniformly dissolve carbonate minerals (calcite, dolomite), and under the action of reservoir closure pressure, relies on the self-supporting effect of non-uniform dissolution cracks to form high-conductivity channels; while hydraulic sand fracturing injects proppants (such as ceramsite or quartz sand) into hydraulic fractures to form high-conductivity channels by relying on the supporting effect of sand-filled fractures. For carbonate reservoirs, based on field experience, acid fracturing is usually used for reservoirs with high conductivity of acid-etched fractures, and acid fracturing + sand fracturing composite transformation or hydraulic sand fracturing is used for reservoirs with relatively poor conductivity of acid-etched fractures.

[0003] The lithology of deep carbonate rocks is relatively complex, usually composed of calcite, dolomite, and non-acid-soluble minerals such as quartz and talc (Wang Qi, Yang Tianwei, Liu Yongzhen, et al. Complex carbonate rock lithology identification based on random forest algorithm [J]. Journal of Engineering Geophysics, 2020, 17(5): 550-558.). The conductivity formed when the acid flows and reacts in hydraulic fractures composed of multiple minerals is difficult to predict, which increases the difficulty of selecting the reconstruction technology for complex lithology carbonate reservoirs and is not conducive to the efficient exploitation of oil, gas and geothermal energy in complex lithology carbonate reservoirs. Summary of the invention

[0004] In view of the above problems, the present invention aims to provide a method for determining the reconstruction process of complex lithology carbonate reservoirs.

[0005] The technical solution of the present invention is as follows:

[0006] A method for determining a complex lithology carbonate reservoir reconstruction process is provided, comprising the following steps:

[0007] S1: Based on the rock and mineral composition of the target reservoir, a mathematical model of rock and mineral heterogeneity distribution is established;

[0008] S11: Based on the downhole core mineral analysis of the target reservoir, obtain the reservoir rock and mineral heterogeneity distribution parameters;

[0009] S12: Based on the reservoir rock and mineral non-uniform distribution parameters obtained in step S11, a Gaussian filter function is used to generate reservoir mineral distribution;

[0010] S2: Based on the non-uniform distribution characteristics of rock and minerals in S1, an acid-etched hydraulic fracture model with complex lithology distribution is constructed to calculate the distribution of fracture width after acid etching;

[0011] S21: Acquire acid fluid and reservoir parameters and determine a physical region for numerical simulation of acid fracturing, divide the region into a grid system consisting of a finite number of structured grids with length Δx and width Δz, and load the acquired parameters into the corresponding grids;

[0012] S22: establishing a dynamic equation for the flow of acid in rough fractures, and calculating the pressure of each grid and the acid flow rate during the acid fracturing process according to the dynamic equation;

[0013] S23: constructing a concentration variation equation of acid solution flowing in a rough fracture, and calculating the acid solution concentration of each grid during the acid fracturing process according to the concentration variation equation;

[0014] S24: updating the seam width of each grid according to the acid concentration of each grid;

[0015] S25: using the seam width obtained in step S24 as the initial seam width b of the next time step, repeating steps S22-S24 until the acid fracturing construction is completed, and obtaining the acid etched seam width distribution at the end of the acid fracturing construction;

[0016] S26: Calculate the ideal seam width w according to the acid-etched seam width distribution at the end of the acid fracturing construction in step S25 i ;

[0017] S3: Based on the S2 acid etching fracture width distribution and experimental test conductivity results, a calculation model for the conductivity of complex lithology reservoirs was established to calculate the fracture conductivity under reservoir closure pressure conditions;

[0018] S4: Calculate the conductivity compliance rate based on the fracture conductivity under the closure pressure condition of S3 reservoir and select the reservoir transformation process.

[0019] Preferably, in step S21, the initial fracture width distribution in the reservoir parameters is generated using the Box-Muller method.

[0020] Preferably, in step S22, the kinetic equation includes:

[0021] The mass conservation equation:

[0022]

[0023] Where: is the Nabla operator; b is the crack width, cm; v is the flow rate of the acid, cm / s; v l is the acid loss rate along the crack width, cm / s;

[0024] Darcy's Law:

[0025]

[0026] Where: v x 、v z are the flow rates of the acid along the length and height of the fracture, cm / s; K is the fracture permeability, D; μ is the viscosity of the acid, mPa·s; P is the pressure, atm; x and z are the fracture length and height, cm, respectively.

[0027] Preferably, the filtration rate of the acid solution along the seam width is calculated by the following formula:

[0028]

[0029] w r =2v l ·(tt p ) (5)

[0030] Where: k matrix is the permeability of the rock matrix, D; P e is the reservoir pressure, atm; w r is the thickness of the acid loss zone, cm; t is the acid injection time, s; t p is the time for the acid to reach the grid, s.

[0031] Preferably, in step S23, the concentration change equation is:

[0032]

[0033] Where: is the Nabla operator; is the average concentration of the acid solution, g / cm 3 ; b is the crack width, cm; v is the flow rate of the acid, cm / s; v l is the acid loss rate along the crack width, cm / s; R s is the acid-rock reaction rate, g / (cm 2 ·s); t is the acid injection time, s; R is the gas molar constant, J / (mol·℃); T is the temperature, ℃.

[0034] Preferably, in step S24, the seam width of each grid is updated by the following formula:

[0035]

[0036] Where: β is the solubility of the acid, dimensionless; ρ r is the rock density, g / cm 3 ; is the rock porosity, dimensionless; η is the proportion of lost acid participating in the acid-rock reaction, dimensionless.

[0037] Preferably, in step S26, the ideal seam width is calculated by the following formula:

[0038]

[0039] Where: w i is the ideal crack width, cm; N and M are the number of grids divided along the crack length and crack height, respectively, dimensionless; b is the acid-etched crack width after acid fracturing, cm; Δx and Δz are the step lengths of the grid along the crack length and crack height, respectively, cm; S is the total crack surface area, cm 2 .

[0040] Preferably, in step S3, the calculation model of the conductivity of the complex lithology reservoir is:

[0041]

[0042]

[0043] Where: F is the conductivity of the fracture, D·cm; F0 is the conductivity of the fracture when the closing pressure is 0MPa, D·cm; f calcite 、f dolomite 、f non are the contents of calcite, dolomite and non-reactive minerals, dimensionless; A is the conductivity attenuation coefficient, dimensionless; σ c is the closing pressure, MPa; F 0M is the initial conductivity of the fracture when only calcite and dolomite are considered, D·cm; w i is the ideal seam width, cm; ae, gs, t', u, v', wz, BE and GH are all fitting parameters.

[0044] Preferably, in step S4, the flow conductivity reaching standard rate is calculated by the following formula:

[0045]

[0046] Where: α is the compliance rate of acid-etched fracture conductivity, %; Fs is the conductivity of acid-etched fracture designed to achieve the predetermined production, D cm;

[0047] The specific selection method for selecting the reservoir transformation process according to the conductivity reaching standard rate is:

[0048] If α≥70%, acid fracturing process is recommended;

[0049] If 30<α<70%, a composite reconstruction process combining acid fracturing and hydraulic sand fracturing is recommended;

[0050] If α≤30%, hydraulic sand fracturing is recommended.

[0051] The beneficial effects of the present invention are:

[0052] The present invention can take into account the influence of complex rock and mineral characteristics of the reservoir on the conductivity of acid-etched fractures, and the calculated conductivity is more in line with the actual situation of carbonate rocks with complex mineral composition, so as to more accurately determine the transformation process of complex lithology carbonate reservoirs and improve the reservoir transformation effect. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0054] Figure 1 A mineral distribution map generated for a specific embodiment;

[0055] Figure 2 The figure is a curve diagram showing the change of flow conductivity with closing pressure in a specific embodiment. DETAILED DESCRIPTION

[0056] The present invention is further described below in conjunction with the accompanying drawings and embodiments. It should be noted that, in the absence of conflict, the embodiments in this application and the technical features in the embodiments can be combined with each other. It should be noted that, unless otherwise specified, all technical and scientific terms used in this application have the same meanings as those generally understood by those of ordinary skill in the art to which this application belongs. The words "including" or "comprising" and the like used in the disclosure of the present invention mean that the elements or objects appearing before the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects.

[0057] The present invention provides a method for determining a complex lithology carbonate reservoir reconstruction process, comprising the following steps:

[0058] S1: Based on the rock and mineral composition of the target reservoir, a mathematical model of rock and mineral heterogeneity distribution is established;

[0059] S11: Based on the downhole core mineral analysis of the target reservoir, obtain the reservoir rock and mineral heterogeneity distribution parameters;

[0060] In a specific embodiment, the reservoir rock mineral heterogeneity distribution parameters include the proportions of dolomite, calcite and non-acid soluble minerals (respectively f dolomite 、f calcite 、f non ), mineral lateral σ x and the longitudinal distribution standard deviation σ y ,in:

[0061]

[0062] Where: N x 、N y The minerals are continuously distributed along the length and height of the fracture;

[0063] In a specific embodiment, the proportions of dolomite, calcite and non-acid soluble minerals are obtained by performing an X-ray fluorescence experiment or an energy spectrum analysis experiment on downhole cores;

[0064] S12: Based on the reservoir rock and mineral non-uniform distribution parameters obtained in step S11, a Gaussian filter function is used to generate reservoir mineral distribution;

[0065] In a specific embodiment, it is assumed that the mineral composition of the core is the same as the actual reservoir situation, and the reservoir mineral distribution generated by Gaussian filtering is specifically:

[0066] First, a mineral distribution model that satisfies the rock and mineral composition is randomly generated. Then, according to the standard deviation of the horizontal and vertical distribution of the core minerals, the Gaussian kernel radius r is selected, and the Gaussian kernel element is calculated by the following formula:

[0067]

[0068] The randomly generated mineral distribution is then corrected according to the following formula and the generated Gaussian kernel to obtain the final mineral distribution:

[0069]

[0070] S2: Based on the non-uniform distribution characteristics of rock and minerals in S1, an acid-etched hydraulic fracture model with complex lithology distribution is constructed to calculate the distribution of fracture width after acid etching;

[0071] S21: Acquire acid fluid and reservoir parameters and determine a physical region for numerical simulation of acid fracturing, divide the region into a grid system consisting of a finite number of structured grids with length Δx and width Δz, and load the acquired parameters into the corresponding grids;

[0072] In a specific embodiment, the reservoir parameters are obtained by combining phenomena logging, mud logging and other data with experimental test results of downhole cores, and the acid fluid parameters are obtained by experimental test results of acid fluids. The physical region is determined by determining the regional boundaries and shapes of the carbonate rocks composed of complex minerals actually acid-fractured based on geological data.

[0073] In a specific embodiment, the reservoir parameters include rock porosity, rock matrix permeability, rock density, reservoir temperature, and initial hydraulic fracture opening; the basic parameters of the acid include acid viscosity and acid concentration.

[0074] In a specific embodiment, the initial opening of the hydraulic fracture is randomly generated by the Box-Muller method according to the average value and the standard deviation, specifically:

[0075] First, randomly generate random numbers U1 and U2 that obey the uniform distribution of 0-1, and convert them into random numbers Z0 and Z1 that obey the Gaussian distribution with a mean of 0 and a variance of 1 by the following formula;

[0076]

[0077] Then, based on the following formula, the random numbers are transformed into the mean μ a , a random number X from a Gaussian distribution with variance σ:

[0078] X=μ a +σZ (19)

[0079] It should be noted that the method for loading the initial fracture width distribution and reservoir mineral distribution in the above embodiment is only a preferred specific embodiment method of the present invention, and other methods for loading the parameters in the prior art may also be applicable to the present invention.

[0080] S22: establishing a dynamic equation for the flow of acid in rough fractures, and calculating the pressure of each grid and the acid flow rate during the acid fracturing process according to the dynamic equation;

[0081] In a specific embodiment, the kinetic equation includes:

[0082] The mass conservation equation:

[0083]

[0084] Where: is the Nabla operator; b is the crack width, cm; v is the flow rate of the acid, cm / s; v l is the acid loss rate along the crack width, cm / s;

[0085] Darcy's Law:

[0086]

[0087] Where: v x 、v z are the flow rates of the acid along the length and height of the fracture, cm / s; K is the fracture permeability, D; μ is the viscosity of the acid, mPa·s; P is the pressure, atm; x and z are the fracture length and height, cm, respectively.

[0088] In a specific embodiment, the filtration rate of the acid solution along the seam width is calculated by the following formula:

[0089]

[0090] w r =2v l ·(tt p ) (5)

[0091] Where: k matrix is the permeability of the rock matrix, D; P e is the reservoir pressure, atm; w r is the thickness of the acid loss zone, cm; t is the acid injection time, s; t p is the time for the acid to reach the grid, s.

[0092] S23: constructing a concentration variation equation of acid solution flowing in a rough fracture, and calculating the acid solution concentration of each grid during the acid fracturing process according to the concentration variation equation;

[0093] In a specific embodiment, the concentration change equation is:

[0094]

[0095] Where: is the Nabla operator; is the average concentration of the acid solution, g / cm 3 ; b is the crack width, cm; v is the flow rate of the acid, cm / s; v l is the acid loss rate along the crack width, cm / s; R s is the acid-rock reaction rate, g / (cm 2 ·s); t is the acid injection time, s; R is the gas molar constant, J / (mol·℃); T is the temperature, ℃.

[0096] S24: updating the seam width of each grid according to the acid concentration of each grid;

[0097] In a specific embodiment, the seam width of each grid is updated by the following formula:

[0098]

[0099] Where: β is the solubility of the acid (i.e. the mass of rock dissolved by unit mass of acid), dimensionless; ρ r is the rock density, g / cm 3 ; is the rock porosity, dimensionless; η is the proportion of lost acid participating in the acid-rock reaction, dimensionless.

[0100] S25: using the seam width obtained in step S24 as the initial seam width b of the next time step, repeating steps S22-S24 until the acid fracturing construction is completed, and obtaining the acid etched seam width distribution at the end of the acid fracturing construction;

[0101] In a specific embodiment, the state parameters at the end of the acid fracturing construction include the pressure distribution inside the crack, the flow rate distribution of the acid solution, the concentration distribution of the acid solution, the acid etch crack width distribution (i.e., the morphology of the acid etch crack), the effective action distance of the acid solution, etc.

[0102] S26: Calculate the ideal seam width w according to the acid-etched seam width distribution at the end of the acid fracturing construction in step S25 i ;

[0103] In a specific embodiment, the ideal seam width is calculated by the following formula:

[0104]

[0105] Where: w i is the ideal crack width, cm; N and M are the number of grids divided along the crack length and crack height, respectively, dimensionless; b is the acid-etched crack width after acid fracturing, cm; Δx and Δz are the step lengths of the grid along the crack length and crack height, respectively, cm; S is the total crack surface area, cm 2 .

[0106] S3: Based on the S2 acid etching fracture width distribution and experimental test conductivity results, a calculation model for the conductivity of complex lithology reservoirs was established to calculate the fracture conductivity under reservoir closure pressure conditions;

[0107] In a specific embodiment, the calculation model of the conductivity of the complex lithology reservoir is:

[0108]

[0109] Where: F is the conductivity of the fracture, D·cm; F0 is the conductivity of the fracture when the closing pressure is 0MPa, D·cm; f calcite 、f dolomite 、f non are the contents of calcite, dolomite and non-reactive minerals, dimensionless; A is the conductivity attenuation coefficient, dimensionless; σ c is the closing pressure, MPa; F0M is the initial conductivity of the fracture when only calcite and dolomite are considered, D·cm; w i is the ideal seam width, cm; ae, gs, t', u, v', wz, BE and GH are all fitting parameters.

[0110] It should be noted that the fitting parameters are obtained by fitting the test results of the conductivity capacity experiment of the core.

[0111] S4: Calculate the conductivity compliance rate based on the fracture conductivity under the closure pressure condition of S3 reservoir and select the reservoir transformation process.

[0112] In a specific embodiment, the flow conductivity compliance rate is calculated by the following formula:

[0113]

[0114] Where: α is the compliance rate of acid-etched fracture conductivity, %; Fs is the conductivity of acid-etched fracture designed to achieve the predetermined production, D cm;

[0115] The specific selection method for selecting the reservoir transformation process according to the conductivity reaching standard rate is as follows:

[0116] If α≥70%, acid fracturing process is recommended;

[0117] If 30<α<70%, a composite reconstruction process combining acid fracturing and hydraulic sand fracturing is recommended;

[0118] If α≤30%, hydraulic sand fracturing is recommended.

[0119] In a specific embodiment, taking Well X in the eastern Sichuan Basin as an example, the method of the present invention is used to optimize the reservoir transformation process.

[0120] In this example, the mineral composition of the Maokou Formation in Well X is shown in Table 1:

[0121] Table 1 Mineral composition of the Maokou Formation in Well X

[0122]

[0123]

[0124] The physical model area used in the calculation is set to be 100m in length and 50m in height. The area is divided into rectangular grids with a grid step size of Δx=Δz=0.1cm. The model boundaries of this embodiment are: the inner boundary has a constant flow rate and a constant acid concentration, the outer boundary has a constant pressure, the upper and lower boundaries are closed, and the acid injection time is 20min.

[0125] The ratio of reservoir dolomite, calcite and non-acid soluble minerals is set to 0.04:0.70:0.26, and the mineral distribution of the fracture surface is generated by the Gaussian filter function. Figure 1 As shown in the figure, the initial hydraulic fracture width mean is set to 0.015 cm, the standard deviation is set to 0.010 cm, and the initial hydraulic fracture width distribution is generated by the Box-Muller method.

[0126] According to the test results of the downhole core conductivity of Well X, the fitting parameters of equations (10) to (13) are shown in Table 2:

[0127] Table 2 Fitting parameter results of the flow conductivity calculation model

[0128] Fitting parameters Fitted values Fitting parameters Fitted values Fitting parameters Fitted values a 2.950 m 4.550 x 0.950 b -0.621 n 5.630 y -1.010 c 5.862 o 0.700 z 2.900 d 1.240 p -4.310 B 4.350 e 2.500 q -0.020 C 11.400 g 2.145 r 0.630 D 27.650 h 0.168 s 1.080 E 16.440 i -0.263 t' -0.120 G 6.420 j 0.460 u 0.110 H 0.082 k 1.833 v' 0.240 l 11.950 w 0.840

[0129] The conductivity calculation model established is used to calculate the variation of the conductivity of the case fracture with the closing pressure. Figure 2 As shown. According to the closure pressure of Well X of 30 MPa, the corresponding fracture conductivity is calculated by equations (10)-(13) to be 5.14 D·cm; the fracture conductivity that meets the production capacity demand is 10 D·cm, and the conductivity compliance rate calculated by equation (14) is 51.4%, so it is recommended to adopt the acid fracturing + hydraulic sand fracturing composite transformation process.

[0130] Well X uses pre-fracturing acid fracturing combined with large-scale variable viscosity slippery water carrying sand fracturing to construct an acid-etched fracture + support fracture system to maintain the conductivity of the acid-etched fracture. Specifically, Well X uses weak gel fracturing fluid and gelled acid as pre-fluid, uses variable viscosity slippery water carrying sand to support fractures, and has injected a total of 1610.0m of weak gel fracturing fluid. 3 、Gelative acid 1480.0m 3 、Slippery water 21505.5m 3 , 1826.4t of ceramsite proppant. Microseismic data show that the average length of the stimulated fractures is 249-314m, the near-well permeability is 0.02mD, the far-well permeability is 0.01mD, and the open-flow rate is 12.46×10 4 m 3 / d, realizing for the first time the composite transformation of a long horizontal section of a dense limestone reservoir with a high content of non-acid soluble minerals, with a significant production increase effect.

[0131] In summary, the present invention can determine the conductivity of carbonate reservoirs with complex lithology, and then select targeted transformation processes to improve the transformation effect. Compared with the prior art, the present invention has significant progress.

[0132] The above description is only a preferred embodiment of the present invention and does not constitute any form of limitation to the present invention. Although the present invention has been disclosed as a preferred embodiment as above, it is not intended to limit the present invention. Any technician familiar with the profession can make some changes or modifications to equivalent embodiments of equivalent changes using the technical contents disclosed above without departing from the scope of the technical solution of the present invention. However, any simple modification, equivalent change and modification made to the above embodiments based on the technical essence of the present invention without departing from the content of the technical solution of the present invention still falls within the scope of the technical solution of the present invention.

Claims

1. A method for determining a complex lithology carbonate reservoir reconstruction process, characterized in that: The following steps are involved: S1: Based on the rock and mineral composition of the target reservoir, a mathematical model of rock and mineral heterogeneity distribution is established; S11: Based on the downhole core mineral analysis of the target reservoir, obtain the reservoir rock and mineral heterogeneity distribution parameters; S12: Based on the reservoir rock and mineral non-uniform distribution parameters obtained in step S11, a Gaussian filter function is used to generate reservoir mineral distribution; S2: Based on the non-uniform distribution characteristics of rock and minerals in S1, an acid-etched hydraulic fracture model with complex lithology distribution is constructed to calculate the distribution of fracture width after acid etching; S21: Acquire acid fluid and reservoir parameters and determine a physical region for numerical simulation of acid fracturing, divide the region into a grid system consisting of a finite number of structured grids with length Δx and width Δz, and load the acquired parameters into the corresponding grids; S22: establishing a dynamic equation for the flow of acid in rough fractures, and calculating the pressure of each grid and the acid flow rate during the acid fracturing process according to the dynamic equation; S23: constructing a concentration variation equation of acid solution flowing in a rough fracture, and calculating the acid solution concentration of each grid during the acid fracturing process according to the concentration variation equation; S24: updating the seam width of each grid according to the acid concentration of each grid; S25: using the seam width obtained in step S24 as the initial seam width b of the next time step, repeating steps S22-S24 until the acid fracturing construction is completed, and obtaining the acid etched seam width distribution at the end of the acid fracturing construction; S26: Calculate the ideal seam width w according to the acid-etched seam width distribution at the end of the acid fracturing construction in step S25 i ; S3: Based on the S2 acid etching fracture width distribution and experimental test conductivity results, a calculation model for the conductivity of complex lithology reservoirs was established to calculate the fracture conductivity under reservoir closure pressure conditions; S4: Calculate the conductivity compliance rate based on the fracture conductivity under the closure pressure condition of S3 reservoir and select the reservoir transformation process.

2. The complex lithology carbonate reservoir reconstruction process determination method according to claim 1 is characterized in that: In step S21, the initial fracture width distribution in the reservoir parameters is generated using the Box-Muller method.

3. The complex lithology carbonate reservoir reconstruction process determination method according to claim 1, characterized in that: In step S22, the kinetic equation includes: The mass conservation equation: Where: is the Nabla operator; b is the crack width, cm; v is the flow rate of the acid, cm / s; v l is the acid loss rate along the crack width, cm / s; Darcy's Law: Where: v x 、v z are the flow rates of the acid along the length and height of the fracture, cm / s; K is the fracture permeability, D; μ is the viscosity of the acid, mPa·s; P is the pressure, atm; x and z are the fracture length and height, cm, respectively.

4. The complex lithology carbonate reservoir reconstruction process determination method according to claim 3 is characterized in that: The acid loss rate along the seam width is calculated by the following formula: w r =2v l ·(t-t p ) (5) Where: k matrix is the permeability of the rock matrix, D; P e is the reservoir pressure, atm; w r is the thickness of the acid loss zone, cm; t is the acid injection time, s; t p is the time for the acid to reach the grid, s.

5. The complex lithology carbonate reservoir reconstruction process determination method according to claim 1, characterized in that: In step S23, the concentration change equation is: Where: is the Nabla operator; is the average concentration of the acid solution, g / cm 3 ; b is the crack width, cm; v is the flow rate of the acid, cm / s; v l is the acid loss rate along the crack width, cm / s; R s is the acid-rock reaction rate, g / (cm 2 ·s); t is the acid injection time, s; R is the gas molar constant, J / (mol·℃); T is the temperature, ℃.

6. The complex lithology carbonate reservoir reconstruction process determination method according to claim 5, characterized in that: In step S24, the seam width of each grid is updated by the following formula: Where: β is the solubility of the acid, dimensionless; ρ r is the rock density, g / cm 3 ; is the rock porosity, dimensionless; η is the proportion of lost acid participating in the acid-rock reaction, dimensionless.

7. The complex lithology carbonate reservoir reconstruction process determination method according to claim 1, characterized in that: In step S26, the ideal seam width is calculated by the following formula: Where: w i is the ideal crack width, cm; N and M are the number of grids divided along the crack length and crack height, respectively, dimensionless; b is the acid-etched crack width after acid fracturing, cm; Δx and Δz are the step lengths of the grid along the crack length and crack height, respectively, cm; S is the total crack surface area, cm 2 .

8. The complex lithology carbonate reservoir reconstruction process determination method according to claim 1, characterized in that: In step S3, the calculation model of the conductivity of the complex lithology reservoir is: Where: F is the conductivity of the fracture, D·cm; F0 is the conductivity of the fracture when the closing pressure is 0MPa, D·cm; f calcite 、f dolomite 、f non are the contents of calcite, dolomite and non-reactive minerals, dimensionless; A is the conductivity attenuation coefficient, dimensionless; σ c is the closing pressure, MPa; F 0M is the initial conductivity of the fracture when only calcite and dolomite are considered, D·cm; w i is the ideal seam width, cm; ae, gs, t', u, v', wz, BE and GH are all fitting parameters.

9. The complex lithology carbonate reservoir reconstruction process determination method according to claim 1, characterized in that: In step S4, the flow conductivity reaching standard rate is calculated by the following formula: Where: α is the compliance rate of acid-etched fracture conductivity, %; Fs is the conductivity of acid-etched fracture designed to achieve the predetermined production, D cm; The specific selection method for selecting the reservoir transformation process according to the conductivity reaching standard rate is as follows: If α≥70%, acid fracturing process is recommended; If 30<α<70%, a composite reconstruction process combining acid fracturing and hydraulic sand fracturing is recommended; If α≤30%, hydraulic sand fracturing is recommended.

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