A method for constructing high conductivity fractures for increasing production in carbonate reservoirs

Abstract

The invention discloses a method for constructing high conductivity of fractures in carbonate reservoir production increase and transformation, which belongs to the field of oil and gas field production increase and transformation, and the specific steps include: S1 determining the lithology of the target layer; S2 preliminarily selecting a rock strengthener for in-situ modification of the fracture wall; S3 carrying out an in-situ modification experiment of the fracture wall with the rock strengthener under the reservoir environment; S4 selecting the rock strengthener based on the experimental results; S5 constructing a mathematical model of factors such as rock embedding strength and rock strengthener; S6 constructing a mathematical model of factors such as fracture conductivity and fracture wall rock embedding strength; S7 inversely designing parameters such as rock strengthener according to the fracture conductivity that should be achieved in the reservoir production increase and transformation. The invention starts with the rock mechanics factors that affect the fracture conductivity, and through the underground in-situ modification of the fracture wall mechanical properties, the fracture can maintain high conductivity under high closure stress, and the effect is better.

Description

Technical Field

[0001] The invention relates to a fracturing or acid fracturing production-increasing technology in the process of oil and gas field development, and in particular to a method for constructing high conductivity fractures for production-increasing and transformation of carbonate reservoirs, belonging to the field of production-increasing and transformation of oil and gas fields. Background Art

[0002] Carbonate oil and gas reserves account for about 70% of the world's total oil and gas reserves, and carbonate oil and gas production accounts for about 60% of the world's total oil and gas production. Carbonate oil and gas reservoirs are the main force of oil and gas reserves and production, and have broad prospects for exploitation. Deep and ultra-deep carbonate oil and gas reservoirs have gradually become the successor blocks of oil and gas production. In addition, as a typical lithology, carbonate rock reservoirs are also extremely rich in geothermal resources. Hydraulic fracturing and acid fracturing (abbreviated as acid fracturing) technology is a necessary means for increasing the production of carbonate geothermal and oil and gas reservoirs, and the conductivity of the fracture determines the effect of the increase in production. The ultra-high temperature, ultra-high pressure and ultra-high closure stress of deep and ultra-deep layers lead to low fracture conductivity, which seriously affects the effect of the increase in production.

[0003] Existing studies have shown that geological factors (reservoir temperature, fracture closure stress, and rock embedding strength, etc.) and engineering factors (proppant type and concentration, acid type and concentration, injection fluid volume and displacement, etc.) determine the conductivity of fractures. For acid fracturing, there are currently two methods to improve fracture conductivity: one is to form strong non-uniform etching on the fracture surface, which is achieved by acid optimization, injection parameter optimization, and multi-stage alternating injection; the other is to support acid-etched fractures with proppants, which is achieved by composite sand-added acid fracturing. For hydraulic fracturing, the current method to improve fracture conductivity is mainly to support hydraulic fractures with proppants, which is achieved by proppant optimization, proppant laying concentration and method optimization. These methods all start from the engineering factors that affect fracture conductivity, and have been done very well. The fractures formed have high conductivity under low closure stress, but their conductivity decays very quickly with the increase of closure stress, and their conductivity is extremely low under high closure stress. Therefore, according to the mechanism of reduced conductivity, the present invention starts from the geological factors that affect the conductivity, and enables the acid-etched fractures or sand-added fractures to maintain high conductivity under high closure stress by modifying the mechanical properties of the fracture wall in situ underground.

[0004] The inventors found that the existing patent CN117605454A discloses a new method for improving the conductivity of fractures in carbonate reservoirs. The principle of improving the conductivity of fractures is to chemically strengthen the fracture surface rock. First, a series of potential chemical agents are obtained through theoretical analysis (i.e., step S2). Then, through indoor experiments, the chemical agent S2 is screened with the goal of improving the strength of the fracture surface rock (i.e., step S3), the chemical agent S3 is screened with the goal of damaging the fracture surface rock with low permeability (i.e., step S4), the chemical agent S4 is screened with the goal of quickly strengthening the fracture surface rock (i.e., step S5), and the chemical agent S5 is screened with the goal of increasing the maximum conductivity of the fracture (i.e., step S6). This progressive optimization method may lead to the miss of chemicals that can improve rock strength to a medium to high level but have little damage to rock permeability and can quickly strengthen the rock on the fracture surface. That is, the order of progressive optimization will affect which chemical will be finally determined; it does not take into account the depth of chemical penetration into the rock, that is, how deep it can be strengthened; in addition, it does not consider the actual demand for fracture conductivity for increased production in different reservoirs, and the constructed fracture conductivity is likely to be too high, resulting in waste. A design method based on this novel principle of improving fracture conductivity has not been established. The Nierode-Kruk relationship is a very classic formula for calculating the conductivity of acid-etched fractures, in which the conductivity of acid-etched fractures is closely positively correlated with the rock embedding strength (Li Yingchuan, Zhong Haiquan. Oil Production Engineering (3rd Edition·Rich Media) [M]. Petroleum Industry Press, 2021: 327-328. Wang Yufang, et al. Study on the variation law of conductivity of carbonate acid-fracturing fractures with fracture length [J]. Journal of Geomechanics, 2015, 21 (04): 546-554.), but there is currently no method to calculate the rock embedding strength. Summary of the invention

[0005] The purpose of the present invention is to address the above-mentioned deficiencies and to propose a method for constructing high conductivity fractures in carbonate reservoirs for production enhancement and transformation. The specific technical scheme is as follows.

[0006] Step S1: Determine that the lithology of the target layer is carbonate rock. Use testing methods (such as X-ray fluorescence spectrometer, X-ray diffractometer, nuclear magnetic resonance spectrometer, electron probe microanalyzer, scanning electron microscope, etc.) to analyze the lithology and specific mineral composition of the target layer rock sample.

[0007] Step S2: Theoretically analyze and preliminarily select rock strengtheners for in-situ modification of fracture walls. Drawing on the basic ideas of diagenesis and dolomitization theory in geology, theoretically analyze rock strengtheners that can directly consolidate rock particles and in-situ transform carbonate minerals, and preliminarily select a series of rock strengtheners based on the requirement that the rock strengtheners can be easily injected into fractures in underground target layers.

[0008] Step S3: In-situ modification experiments of fracture walls are carried out using the rock strengtheners preliminarily selected in step S2 under reservoir environment, and parameters such as hardness, permeability, quality and three-dimensional spatial distribution of pores of rock samples before and after the experiment are tested. Experiments of in-situ modification of fracture walls by rock strengtheners are carried out in an experimental platform for the reaction or consolidation of rock strengtheners with fracture wall rocks and filtration through fracture walls in a real simulated reservoir environment, and permeability testers, micro-Vickers hardness testers, rock CT scanners and analytical balances are used to test the permeability, hardness, three-dimensional spatial distribution of pores reconstructed by CT and quality of original and rock strengthener-treated rock samples.

[0009] Step S4: Based on the experimental results of step S3, select the rock strengthener corresponding to the maximum value of the comprehensive index. Calculation formula:

[0010] (1)

[0011] Where: , , , and are the hardness, permeability, length, mass and treatment time of the rock sample after treatment with rock strengthener, Indicates Experiments, each rock enhancer experiment Second-rate; , ,and They are the hardness, permeability and quality of the original rock sample; is the penetration depth of rock enhancer, obtained by CT reconstruction of pore three-dimensional spatial distribution before and after the experiment; 、 , and are coefficients, .

[0012] Step S5: Use the rock strengthener selected in step S4 to carry out in-situ modification experiments on the crack wall under different factors (surface ratio, rock strengthener concentration, experimental temperature, pressure and time), test the hardness of the rock samples before and after the experiment, calculate the rock embedding strength before and after the experiment, and construct a mathematical model of rock embedding strength and different factors. Rock embedding strength calculation formula:

[0013] (2)

[0014] Where: is the rock embedment strength; is the acceleration due to gravity; is the hardness of the rock sample; It is the relative angle between the indenters of the hardness tester.

[0015] According to the experimental results, a mathematical model of rock embedding strength and different factors was constructed using multivariate nonlinear regression fitting, namely:

[0016] (3)

[0017] Where: is the embedment strength of rock after treatment with rock strengthener; It is the ratio of the reaction area of ​​the rock sample to the volume of the rock enhancer participating in the reaction; is the rock enhancer concentration; , , are the experimental temperature, pressure and time respectively; is the rock embedding strength of the original rock sample.

[0018] If the multivariate nonlinear regression method is unable to fit a clear mathematical model due to the strong nonlinearity of the data, 75% and 25% of the experimental data are used as training sets and test sets respectively, and the data are standardized. Based on machine learning algorithms (such as Gaussian process regression GPR, random forest RF, artificial neural network ANN, etc.), a correlation model between different factors and the embedding strength of rocks treated with rock strengtheners is established, and model training, testing and optimization are carried out. The final model is selected according to evaluation indicators (such as mean absolute error MAE and root mean square error RMSE).

[0019] Step S6: Use the rock strengthener selected in step S4 to conduct fracture conductivity experiments before and after in-situ modification of the fracture wall, obtain fracture wall morphology characteristic parameters and rock embedding strength before and after modification, and construct a mathematical model of fracture conductivity and fracture wall rock embedding strength, fracture morphology, proppant parameters and sand concentration, and effective closure stress. Use laser scanning to obtain the fracture wall morphology, calculate the fracture wall morphology characteristic parameters, root mean square elevation:

[0020] (4)

[0021] Where: is the root mean square elevation; , It is the number of data points on the surface of the rock plate along the X-axis or Y-axis direction; yes x for j,y for k The data points at is the average elevation.

[0022] Roughness coefficient:

[0023] (5)

[0024] Where: is the roughness coefficient; D is the fractal dimension of the crack profile.

[0025] Transverse and longitudinal tortuosity ratio:

[0026] (6)

[0027] Where: and are the lateral and longitudinal tortuosity ratios, respectively; and are the lengths of the curved sides of the horizontal and vertical sections, respectively; and are the horizontal and vertical scanning steps respectively.

[0028] According to the experimental results of fracture conductivity, a mathematical model of fracture conductivity, fracture wall rock embedding strength, fracture morphology, proppant parameters, sand concentration, and effective closure stress was constructed using multivariate nonlinear regression fitting, namely:

[0029] (7)

[0030] Where: is the fracture conductivity; It is the porosity of the proppant placed in the fracture; is the paving concentration, is the effective crack closure stress.

[0031] If the multivariate nonlinear regression method is unable to fit a clear mathematical model due to the strong nonlinearity of the data, 75% and 25% of the experimental data are used as training sets and test sets respectively, and the data are standardized. Based on machine learning algorithms (such as GPR, RF and ANN, etc.), a correlation model between the fracture conductivity and the rock embedding strength of the fracture wall, fracture morphology, proppant parameters and sand concentration, and effective closure stress is established. Model training, testing and optimization are carried out, and the final model is selected according to evaluation indicators (such as MAE and RMSE, etc.).

[0032] Step S7: Based on the productivity model of seepage (matrix)-fracture flow (crack)-free flow (cavity) coupling, the fracture conductivity that should be achieved in carbonate reservoir production enhancement is calculated. Combined with reservoir parameters and fracture closure time, the mathematical model established in steps S6 and S5 is used to inversely design the rock enhancer concentration, fracture morphology, proppant parameters and sand concentration. The productivity model is used to calculate and draw the productivity and fracture conductivity curve to determine the fracture conductivity corresponding to the inflection point where the productivity changes from rapid growth to slow growth. The fracture closure model is used to calculate the fracture closure time.

[0033] In the above calculation method, the same symbols involved in all formulas have the same meaning before and after, and after being marked once, they are applicable to all formulas.

[0034] The process for constructing high conductivity of fractures disclosed in the present invention is as follows: Figure 1 shown.

[0035] The present invention adopts comprehensive index At the same time, the evaluation overcomes the problems caused by the progressive optimization sequence of patent CN117605454A, and the new rock enhancer invasion depth , taking this key indicator into consideration; the present invention determines how much fracture conductivity is needed for increasing production in different reservoirs with production capacity as the goal, and then uses the mathematical model constructed by S5 and S6 to inversely design the rock strengthener concentration, fracture morphology, proppant parameters and sand concentration, etc., to form a clear design method; the present invention provides a specific calculation formula for calculating the rock embedding strength based on rock hardness, and uses multivariate nonlinear regression or machine learning to construct a mathematical model of fracture conductivity and different factors (step S6), and a mathematical model of rock embedding strength and different factors (step S5).

[0036] The benefits of the present invention lie in that: based on the production capacity model, the present invention determines how much fracture conductivity is needed for reservoir production increase, and then designs relevant parameters such as rock strengtheners in combination with the mathematical model constructed by indoor experimental results, thereby constructing a fracture conductivity that meets the reservoir production increase demand; the rock strengthener is used to in-situ modify the mechanical properties of the fracture wall rock, reduce the embedding, crushing and deformation of the fracture wall, so that the fracture can still have high conductivity under high closure stress, and the effect is better.

[0037] Other advantages, objectives and features of the present invention will be embodied in part through the following description, and in part will be understood by those skilled in the art through study and practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0038] Figure 1 Construct a flow chart for improving the high conductivity of fractures in carbonate reservoirs. DETAILED DESCRIPTION

[0039] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0040] A carbonate reservoir was acid-fracturing-induced production enhancement fracture high conductivity construction. The basic parameters are shown in Table 1. The process is as follows: Figure 1 shown.

[0041] Table 1 Basic parameters (8013~8049m)

[0042]

[0043] Step S1: X-ray diffractometer and scanning electron microscope were used to test and analyze to determine that the lithology of the target layer (8013-8049 m) is carbonate rock, with the main components being 92.3% calcite, 5.9% dolomite, 1.6% quartz and 0.2% clay minerals.

[0044] Step S2: Theoretically analyze rock strengtheners that can cement rock particles or convert calcite into harder minerals in situ, and preliminarily select (NH4)2HPO4, Na2HPO4, K2HPO4, Si(OC2H5)4, ZnSO4, ZnCl2, Zn(NO3)2, NaF, Na2O·nSiO2 and multi-stage ionized weak acids (H3PO4, C4H4O4) as single or two-combination rock strengtheners.

[0045] Step S3: An in-situ modification experiment of the fracture wall with a rock strengthener was carried out in a high-temperature and high-pressure reactor at an experimental temperature of 182 °C, a pressure of 87 MPa, and a time of 0.1, 0.2, 0.5, 1, 2, 4, and 8 h. The permeability, hardness, quality, and CT-reconstructed pore space distribution of the original and rock strengthener-treated rock samples were tested using a permeability meter, a micro-Vickers hardness tester, an analytical balance, and a rock CT scanner.

[0046] Step S4: 、 , and The values ​​are 0.58, 0.12, 0.09 and 0.21 respectively. The comprehensive index of Na2HPO4+H3PO4 in a ratio of 30:1 is 7. maximum.

[0047] Step S5: According to the crack size parameters (crack length 150m, crack height 37m, crack width 0.01, 0.007, 0.004 and 0.001m), the surface area ratio is calculated to be 100, 143, 250 and 1000, the concentration of Na2HPO4+H3PO4 is 0.05, 0.1, 0.2, 0.3M, the experimental temperature is 89, 120, 151 and 182℃, the pressure is 87, 115, 143 and 171MPa, the time is 0.1, 1, 2 and 4h, and the Na2HPO4+H3PO4 in-situ modification crack wall experiment under different factors is orthogonally carried out. The hardness of the rock sample before and after the experiment is tested by a micro Vickers hardness tester, and the rock embedding strength before and after the experiment is calculated using formula (2). The model is obtained by multivariate nonlinear regression fitting, and its residual and variance (SSE) are too large. Therefore, a correlation model between the embedded strength of treated rocks and different factors was established using machine learning algorithms (GPR, RF, ANN), and the RF model was selected based on the MAE and RMSE evaluation indicators to describe the and , , , , , relationship.

[0048] Step S6: The target layer is acid-pressed without adding sand. and The fracture conductivity was 0. Acid (single gelling acid, autogenous acid, cross-linking acid and steering acid, two-stage alternating injection in combination, three-stage alternating injection in combination) was first injected to etch the fracture to form acid-etched fractures with different etching morphologies. Na2HPO4+H3PO4 was then injected to in-situ modify the fracture wall. Then, the fracture conductivity experiment before and after in-situ modification of the fracture wall was carried out to obtain the fracture conductivity under different effective closure stresses (5, 10, 20, 30, 40, 50, 60, 70, 80 and 90 MPa). The fracture wall morphology and rock embedding strength before and after modification were obtained using a laser scanner and a micro-Vickers hardness tester. The fracture wall morphology characteristic parameters ( Ra , JRC , e cc and e cl ). The model was obtained by multivariate nonlinear regression fitting, and its residual and SSE were too large. Therefore, a correlation model between fracture conductivity and fracture wall rock embedding strength, fracture wall morphology characteristic parameters and effective closure stress was established based on machine learning algorithms (GPR, RF, ANN). The GPR model was selected based on MAE and RMSE evaluation indicators to describe the fracture conductivity. and , , , , , relationship.

[0049] Step S7: Using the productivity model to calculate the fracture conductivity that should be achieved by reservoir production enhancement is 56 mm 2 ·cm. The crack closure time calculated by the crack closure model is 108min, so The reservoir pressure is 87 MPa, the minimum horizontal principal stress is 143 MPa, and the effective closure stress of the fracture is The target layer is acid-pressed without adding sand. and Based on the acid-etched crack wall morphology characteristic parameters obtained in step S6, the conductivity under different crack wall morphologies is calculated using the acid-etched crack conductivity formula, and the acid-etched crack wall morphology characteristic parameters corresponding to the maximum conductivity are taken, then 1.22, 86.7 is 1.17 and The GPR model established in step S6 is used to inversely design the rock embedding strength of the fracture wall. 2561MPa. Combination temperature 182℃, pressure 87MPa, surface area ratio The rock embedding strength of the original rock sample is 250. The concentration of rock strengthener is designed by inversion using the RF model established in step S5. It is 0.26M.

[0050] The above description is only a preferred embodiment of the present invention and does not limit the present invention in any form. Although the present invention has been disclosed as a preferred embodiment as above, it is not used to limit the present invention. Any technician familiar with this profession can make some changes or modify the technical contents disclosed above into equivalent embodiments 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 according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. A method for constructing high conductivity fractures for increasing production in carbonate reservoirs, characterized in that: The following steps are involved: S1: Determine that the lithology of the target layer is carbonate rock; S2: Theoretical analysis and preliminary selection of rock strengtheners for in-situ modification of fracture walls; S3: In-situ modification experiments on fracture walls were carried out using the rock strengtheners preliminarily selected in S2 under reservoir conditions to test the hardness, permeability, quality and three-dimensional spatial distribution of pores of rock samples before and after the experiment; S4: Based on the experimental results of S3, select the comprehensive index F r The rock enhancer corresponding to the maximum value; Where: HV s , k s , L s 、m s and t i are the hardness, permeability, length, mass and treatment time of the rock sample after treatment with the rock enhancer, the subscript i represents the i-th experiment, and each rock enhancer is tested n times; HV o , k o , and m o are the hardness, permeability and quality of the original rock sample respectively; ΔL is the penetration depth of the rock strengthener, which is obtained by reconstructing the three-dimensional spatial distribution of pores by CT before and after the experiment; χ, γ, ξ and λ are all coefficients, χ+γ+ξ+λ=1; S5: Use the rock strengthener selected in S4 to carry out in-situ modification experiments on the fracture wall under different surface area ratios, rock strengthener concentrations, experimental temperatures, pressures and times, test the hardness of the rock samples before and after the experiment, calculate the rock embedding strength before and after the experiment, and construct a mathematical model of rock embedding strength and surface area ratio, rock strengthener concentration, experimental temperature, pressure and time; It is characterized in that, in step S5, the rock embedding strength calculation formula is: Where: S RE is the rock embedding strength; g is the gravitational acceleration; HV is the rock sample hardness; α is the relative angle of the hardness tester indenter; a mathematical model of rock embedding strength and surface volume ratio, rock strengthener concentration, experimental temperature, pressure and time is constructed using machine learning algorithm or multivariate nonlinear regression, namely: Where: S REs is the embedment strength of rock after treatment with rock strengthener; is the ratio of the rock sample reaction area to the volume of the rock enhancer involved in the reaction; C s is the concentration of rock enhancer; T, P, t are the experimental temperature, pressure and time respectively; S REo is the rock embedding strength of the original rock sample; S6: Use the rock strengthener selected in S4 to carry out fracture conductivity experiments before and after in-situ modification of the fracture wall, obtain the fracture wall morphology characteristic parameters and rock embedding strength before and after modification, and use multivariate nonlinear regression fitting or machine learning algorithm to construct a mathematical model of fracture conductivity and fracture wall rock embedding strength, fracture morphology, proppant parameters and sand concentration, and effective closure stress, that is: kwf=F(S REs ,Ra,JRC,e cc ,he cl ,f p ,C p ,s ce ) (4) Where: kwf is the fracture conductivity; Ra is the root mean square elevation; JRC is the roughness coefficient; ε cc and ε cl are the lateral and longitudinal tortuosity ratios, respectively; φ p C is the porosity of the proppant placed in the fracture; p is the paving concentration, σ ce is the effective crack closure stress; S7: Based on the productivity model, the fracture conductivity that should be achieved in the carbonate reservoir production enhancement transformation is calculated. Combined with the reservoir parameters and fracture closure time, the mathematical model established in S6 and S5 is used to inversely design the rock enhancer concentration, fracture morphology, proppant parameters and sand concentration.

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