Method for predicting evolution process of flow conductivity of artificial fracture by considering dynamic crustal stress field

Abstract

The invention belongs to the technical field of oil development hydraulic fracturing, and discloses an artificial fracture flow conductivity evolution process prediction method considering a dynamic crustal stress field, and the method specifically comprises the following steps: constructing an oil reservoir geologic model; constructing an oil reservoir geomechanical model; carrying out a proppant long-term conductivity test experiment to obtain a proppant conductivity chart; and based on the constructed oil reservoir geologic model and the constructed geomechanical model, combining proppant flow conductivity chart data, and utilizing a fluid-solid coupling oil reservoir simulation technology to predict a reservoir stress field and a pore pressure field in the fracturing production process, so as to predict fracture flow conductivity in different production stages. According to the method, the fracture flow conductivity change under the production dynamic state can be more comprehensively represented, so that a support is provided for fracture flow conductivity optimization of a fractured well and proppant type selection.

Description

Technical Field

[0001] The invention belongs to the technical field of hydraulic fracturing in oil development, and specifically relates to the field of optimization of fracture conductivity during hydraulic fracturing, and in particular to a method for predicting the evolution process of artificial fracture conductivity taking into account a dynamic geostress field. Background Art

[0002] As the main technical means for the development of unconventional oil and gas resources, hydraulic fracturing technology plays an important role in the development of tight oil and gas, shale oil and gas and other oil and gas resources. Selecting a reasonable fracture conductivity is of great significance to improving the effect of hydraulic fracturing and reducing the cost of hydraulic fracturing. At present, the optimization of fracture conductivity during hydraulic fracturing is mainly carried out by taking the single well production as the objective function and optimizing the fracture conductivity by comparing the production under different fracture conductivity. Since this method sets the hydraulic fracture conductivity as a static value, it does not consider the problems such as insufficient fluid supply capacity of oil and gas wells caused by changes in stress field and pore pressure field during fracturing and oil well production, resulting in the designed fracture conductivity being greater than the actual production demand in the middle and late stages of production, resulting in a waste of fracture conductivity (the greater the conductivity, the more proppant is required, and the more production input costs). Therefore, it is of great significance to reduce the cost of hydraulic fracturing and improve the effect of hydraulic fracturing by establishing a numerical simulation prediction model for the geostress field and pore pressure field during the whole life cycle of fracturing-production, quantitatively characterizing the changing law of the stress field in the fracturing-production process and predicting the fracture conductivity of the hydraulic fractures that meet the production needs.

[0003] At present, there is no established hydraulic fracturing numerical simulation model that can accurately predict the reservoir geostress field and pore pressure field during the entire life cycle of fracturing and production, so it is difficult to optimize the dynamic conductivity of hydraulic fracturing fractures.

[0004] Chinese patent CN110410054A discloses a method for predicting the spatiotemporal conductivity of fracturing cracks in coalbed methane wells, comprising the following steps: S1, collecting reservoir rock related parameters of the target coalbed methane well; S2, calibrating the spatial distribution of the global conductivity of the propping cracks at the initial moment after fracturing; S3, establishing a mechanism expression for the influence of coal powder deposition, proppant embedding and time on the conductivity of proppant-filled cracks; S4, according to the established conductivity change mechanism model, spatially discretizing the global conductivity calibrated in step S2 to obtain the conductivity value of each discrete unit at different times; S5, taking the calculation result of step S4 as the starting point, repeating steps S3 to S4 until the global conductivity is calculated; the prediction method of this patent can effectively predict the spatiotemporal conductivity of the global proppant-filled cracks. However, when calculating the effective stress on the fracture wall, the method of this patent uses a fixed value for the minimum horizontal principal stress. It also does not consider setting the hydraulic fracture conductivity to a static value. It does not consider the insufficient fluid supply capacity of the oil and gas wells caused by changes in the stress field and pore pressure field during fracturing and oil well production. It does not conform to the actual evolution process of geostress in engineering practice, and cannot accurately predict the fracture conductivity. It will cause the designed fracture conductivity to be greater than the actual production demand in the middle and late stages of production, thereby resulting in a waste of fracture conductivity.

[0005] Chinese patent CN114482971A discloses a method and application for optimizing proppant for fracturing transformation of unconventional reservoirs. The optimization method includes: using reservoir numerical simulation software to obtain the demand for conductivity to achieve full utilization of reserves under different fracture spacings, using actual well fracturing construction curves to obtain formation closure stress values, and combining actual fracturing well production curves to obtain effective closure stress change values, and then conducting long-term conductivity tests under simulated actual stress conditions to obtain proppant fracture conductivity values ​​under different proppant combination conditions, and finally using fracturing software to simulate and obtain parameters such as proppant type, particle size, and addition that can meet the demand for full utilization of the reservoir; the method of this patent can guide the optimization of proppant under different transformation modes of unconventional reservoirs and improve the fracturing transformation effect of unconventional reservoirs. However, the method of this patent obtains the construction data of wells that have been subjected to fracturing transformation in the study block, obtains the effective closure stress parameters of the reservoir in the study block through fracturing construction data analysis, and then applies the parameters to the experimental simulation process. This method has a prediction lag, which affects the prediction accuracy, and cannot accurately characterize and predict the changes in fracture conductivity under the production dynamics of the entire life cycle. In addition, it is difficult to obtain basic experimental data and the optimization cost is high. Summary of the invention

[0006] The present invention aims to solve the problems of unclear dynamic evolution laws of reservoir stress field and pore pressure field and unclear law of change of dynamic conductivity of hydraulic fractures in the production process of existing fracturing production wells, and to provide a prediction method for the evolution process of artificial fracture conductivity taking into account the dynamic geostress field. The method is based on the geological model of the reservoir before fracturing, and by constructing a fluid-solid coupling fracturing-production integrated numerical simulation model, the change law of reservoir geostress field in the whole process of fracturing-production is obtained, and the change law of fracture conductivity caused by stress field change in the whole life cycle is obtained. The method can more comprehensively characterize the change of fracture conductivity under production dynamics, thereby providing support for the optimization of fracture conductivity of fracturing wells and the selection of proppants.

[0007] In order to achieve the above technical objectives, the present invention adopts the following technical solutions:

[0008] A method for predicting the evolution process of artificial fracture conductivity considering a dynamic geostress field, the method specifically comprising the following steps:

[0009] Step S1: constructing a reservoir geological model;

[0010] Step S2: constructing a reservoir geomechanical model based on the constructed reservoir geological model;

[0011] Step S3: conducting a proppant long-term conductivity test experiment to obtain a conductivity chart under different proppant type combination conditions;

[0012] Step S4: Based on the reservoir geological model constructed in step S1, the geomechanical model constructed in step S2 and the proppant conductivity map obtained in step S3, the reservoir stress field and pore pressure field of the fracturing production process are predicted by using fluid-solid coupling reservoir simulation technology, and the fracture conductivity at different production stages is predicted.

[0013] Furthermore, the step S1 specifically includes:

[0014] Based on core observation, CT scanning and logging curve interpretation, a reservoir geological model is constructed, and refined core description and micro-macro test analysis are carried out to clarify the distribution patterns of reservoir porosity, permeability, oil saturation, temperature and pressure systems.

[0015] Furthermore, the step S2 specifically includes:

[0016] Based on the finite element theory, the rock physical parameters obtained from rock mechanics experiments, combined with the reservoir in-situ stress parameters, and on the basis of the reservoir geological model, the initial distribution law of the reservoir three-dimensional stress field is obtained through in-situ stress balance calculation to construct the reservoir geomechanics model.

[0017] Furthermore, in step S2, the rock physical parameters include Young's modulus and Poisson's ratio.

[0018] Furthermore, in step S2, the reservoir geostress parameters include maximum horizontal principal geostress, minimum horizontal principal geostress and vertical stress.

[0019] Furthermore, the step S3 specifically includes:

[0020] Carry out long-term conductivity test experiments under different proppant types, mesh sizes, and sand concentrations to obtain conductivity charts under different proppant type combinations.

[0021] Furthermore, the step S4 specifically includes:

[0022] Based on the reservoir geological model constructed in step S1, the reservoir geomechanical model constructed in step S2 and the proppant conductivity map obtained in step S3, according to the rock effective stress calculation principle, the seepage mechanics equation and the elastic mechanics equation are coupled and solved, the pore pressure field is solved by the seepage equation, the reservoir stress field is solved by the elastic mechanics equation, and the effective stress acting on the proppant is solved by the reservoir stress field, and the fracture conductivity is further solved.

[0023] Furthermore, the method further comprises the following steps:

[0024] Step S5: Based on the proppant conductivity chart obtained in step S3 and the variation pattern of fracture conductivity at different production stages predicted in step S4, and taking oil production as the target, the proppant type, mesh size and sand concentration corresponding to the reasonable fracture conductivity at different production stages are optimized.

[0025] Compared with the prior art, the beneficial effects of the present invention are:

[0026] (1) The present invention constructs a high-precision reservoir geological model and a reservoir geomechanical model, based on the reservoir geological model before fracturing, and constructs a fluid-solid coupling fracturing-production integrated numerical simulation model to obtain the change law of the reservoir stress field during the whole process of fracturing-production, and obtains the change law of the fracture conductivity caused by the change of the stress field during the whole life cycle. It can more comprehensively and accurately characterize the change of fracture conductivity under production dynamics, thereby providing theoretical support for the optimization of fracture conductivity of fracturing wells and the selection of proppants;

[0027] (2) The present invention predicts fracture conductivity at different production stages based on fluid-solid coupling reservoir simulation technology, obtains the variation law of reservoir stress field and pore pressure field during the fracturing production process, and optimizes reasonable fracture conductivity at different production stages. This research result is of great significance for the optimization of reasonable fracture conductivity during hydraulic fracturing, the optimization of hydraulic fracturing sand addition amount, and the selection of fracturing proppant type. It plays an important role in promoting the use of quartz sand to replace ceramsite in deep oil and gas hydraulic fracturing, and is an important technical guarantee for the efficient development of unconventional oil and gas resources, with good economic efficiency.

[0028] (3) In the early stage of oil field development, the method of the present invention can be used to quickly establish a set of methods for optimizing the conductivity of fracturing fractures. Combined with field practice, it can quickly improve the understanding of the stress field characteristics of the target block reservoir and the characteristics of the formation pressure change during the production process, and finally obtain engineering parameters that meet the conductivity requirements. The method can be promoted and applied in the entire production area to be built, thereby guiding the entire production area to quickly establish differentiated fracturing plans, optimize the fracturing process and key parameters in a targeted manner, and make construction operations more convenient and efficient. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 It is a flow chart of a method for predicting the evolution process of artificial fracture conductivity considering a dynamic geostress field in an embodiment of the present invention;

[0030] Figure 2 A flow chart of selecting proppants to match the conductivity of artificial fractures in an embodiment of the present invention;

[0031] Figure 3 A high-precision three-dimensional reservoir geological model diagram of a typical well in an embodiment of the present invention;

[0032] Figure 4 A high-precision three-dimensional reservoir geomechanical model diagram of a typical well in an embodiment of the present invention;

[0033] Figure 5 This is a graph showing the flow conductivity of 20 / 40 mesh ceramsite at different sand concentrations in an embodiment of the present invention;

[0034] Figure 6 This is a graph showing the flow conductivity of 30 / 50 mesh ceramsite at different sand concentrations in an embodiment of the present invention;

[0035] Figure 7 This is a graph showing the flow conductivity of 40 / 70 mesh ceramsite at different sand concentrations in an embodiment of the present invention;

[0036] Figure 8 Graph showing the flow conductivity of 20 / 40 mesh quartz sand at different sand concentrations in an embodiment of the present invention;

[0037] Fig. 9This is a graph of the flow conductivity of 30 / 50 mesh quartz sand at different sand concentrations in an embodiment of the present invention;

[0038] Fig.10 Graph showing the flow conductivity of 40 / 70 mesh quartz sand at different sand concentrations in an embodiment of the present invention;

[0039] Fig.11 It is a diagram showing changes in stress field during the entire life cycle of typical reservoir fracturing-production in an embodiment of the present invention;

[0040] Fig.12 It is a comparison chart of reservoir recovery degree under different conductivity in the embodiment of the present invention. DETAILED DESCRIPTION

[0041] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0042] Example 1

[0043] The embodiment of the present invention provides a method for predicting the evolution process of artificial fracture conductivity considering the dynamic geostress field. Figure 1 As shown, the method specifically comprises the following steps:

[0044] Step S1: constructing a reservoir geological model;

[0045] Based on core observation, CT scanning and logging curve interpretation, we build reservoir geological models, conduct refined core description and micro-macro test analysis, and clarify the distribution patterns of reservoir porosity, permeability, oil saturation, temperature and pressure systems;

[0046] Step S2: constructing a reservoir geomechanical model;

[0047] Based on the finite element theory, the rock physical parameters such as Young's modulus and Poisson's ratio obtained from rock mechanics experiments, combined with reservoir geostress parameters such as maximum horizontal principal geostress, minimum horizontal principal geostress and vertical stress, and on the basis of the reservoir geological model, the initial distribution law of the reservoir three-dimensional stress field is obtained through geostress balance calculation to construct the reservoir geomechanics model;

[0048] Step S3: conducting a long-term conductivity test experiment under different proppant types, mesh sizes, and sand concentrations to obtain a conductivity chart under different proppant type combination conditions;

[0049] Step S4: Based on the reservoir geological model constructed in step S1, the geomechanical model constructed in step S2 and the proppant conductivity chart obtained in step S3, the reservoir stress field and pore pressure field in the fracturing production process are predicted by using fluid-solid coupling reservoir simulation technology, and the fracture conductivity in different production stages is predicted; specifically:

[0050] Based on the reservoir geological model constructed in step S1, the reservoir geomechanical model constructed in step S2 and the proppant conductivity map obtained in step S3, according to the rock effective stress calculation principle, the seepage mechanics equation and the elastic mechanics equation are coupled and solved, the pore pressure field is solved by the seepage equation, the reservoir stress field is solved by the elastic mechanics equation, the effective stress acting on the proppant is solved by the reservoir stress field, and the fracture conductivity is further solved;

[0051] Step S5: Based on the proppant conductivity chart obtained in step S3 and the variation pattern of fracture conductivity at different production stages predicted in step S4, and with oil production as the target, the proppant type, mesh size and sand concentration corresponding to reasonable fracture conductivity at different production stages are selected.

[0052] Example 2

[0053] The present invention applies the method in Example 1 to a tight sandstone reservoir to analyze the demand for proppant conductivity, and proposes a method for matching formation conductivity with cumulative oil production or recovery as the research objective function, while combining the results of indoor proppant conductivity experiments to select proppant. The detailed method and the flowchart of matching artificial fracture conductivity to select proppant are shown in the figure. Figure 2 shown.

[0054] Step S1: constructing a reservoir geological model;

[0055] The required grid parameters are directly extracted from the roughened geological model; the parameters required in the model building process include: reservoir depth, porosity, permeability, net-to-gross ratio, saturation, pore pressure and other parameters.

[0056] The high-precision reservoir geological model established Figure 3 shown.

[0057] Step S2: constructing a reservoir geomechanical model;

[0058] On the basis of the reservoir geological model, rock physical parameters and reservoir geostress parameters are added to the model, mainly including maximum horizontal principal geostress, minimum horizontal principal geostress, vertical stress, Young's modulus, Poisson's ratio and other parameters, to construct a reservoir geomechanical model.

[0059] The typical well 3D reservoir geomechanical model established is as follows Figure 4 shown.

[0060] Step S3: conducting long-term conductivity test experiments of different proppant types, mesh sizes and sand concentrations to form a proppant conductivity chart;

[0061] Six commonly used proppants were sampled on site, including two types: quartz sand and expanded clay, with particle sizes of 20 / 40 mesh, 30 / 50 mesh and 40 / 70 mesh, for performance testing of proppant conductivity.

[0062] The effective conductivity of fractures is the key to evaluating the success of hydraulic fracturing. Indoor experiments were conducted with different types of proppants and different sand concentrations. The conductivity changed with the increase of closure pressure. After a large number of experiments, a conductivity chart was made to facilitate the selection of proppant type and sand concentration.

[0063] The concentration of sand in the experiment was (4.5kg / m 2 ~12.5kg / m 2 ), increase 0.5kg / m each time 2 The experimental temperature simulates the formation temperature of 60℃, and the fluid is distilled water. Due to the strength problem of ceramsite and quartz sand, the closing pressure during the test is different.

[0064] The final experimental results are as follows Figure 5-10 The corresponding data is shown in Table 1-6 below:

[0065] Table 1 Flow conductivity of 20 / 40 mesh ceramsite

[0066]

[0067] Table 2 Flow conductivity of 30 / 50 mesh ceramsite

[0068]

[0069]

[0070] Table 3 Flow conductivity of 40 / 70 mesh ceramsite

[0071]

[0072]

[0073] Table 4 Flow conductivity of 20 / 40 mesh quartz sand

[0074]

[0075]

[0076] Table 5 Flow conductivity of 30 / 50 mesh quartz sand

[0077]

[0078] Table 6 Flow conductivity of 40 / 70 mesh quartz sand

[0079]

[0080]

[0081] Step S4: Based on the reservoir geological model constructed in step S1, the geomechanical model constructed in step S2 and the key data in the proppant conductivity chart obtained in step S3, the fluid-solid coupling reservoir simulation technology is used to predict the reservoir stress field and pore pressure field during the fracturing production process, and predict the fracture conductivity at different production stages.

[0082] The simulation results of stress field changes during the whole life cycle of reservoir fracturing and production are as follows: Fig.11 As shown in Figure 2. Combined with the change law of stress field, the recovery degree under different conductivity in different mining time periods is predicted and analyzed. As the conductivity of reservoir proppant increases, the increase in recovery degree gradually decreases. Selecting different conductivity to simulate the recovery degree at different times is shown in Figure 2. Fig.12 As shown, when the conductivity is greater than 30 mD·m, it is basically at a critical point, and the preferred value of the long-term conductivity is 30 mD·m.

[0083] Step S5: Based on the proppant conductivity chart in step S3 and the reasonable fracture conductivity variation law in different production stages in step S4, with oil production as the target, the engineering parameters for fracturing in this area are matched and optimized as follows: the proppant type is quartz sand, the mesh size is 40 / 70 mesh, and the sand concentration is 7.5 kg / m 2 .

[0084] The above description is only an embodiment of the present invention and is not intended to limit the present invention. Any modification, equivalent replacement and improvement made within the application scope of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting the evolution of artificial fracture conductivity considering dynamic geostress field, characterized in that: The method specifically comprises the following steps: Step S1: constructing a reservoir geological model; Step S2: constructing a reservoir geomechanical model based on the constructed reservoir geological model; Step S3: conducting a proppant long-term conductivity test experiment to obtain a conductivity chart under different proppant type combination conditions; Step S4: Based on the reservoir geological model constructed in step S1, the geomechanical model constructed in step S2 and the proppant conductivity map obtained in step S3, the reservoir stress field and pore pressure field of the fracturing production process are predicted by using fluid-solid coupling reservoir simulation technology, and the fracture conductivity at different production stages is predicted.

2. The method according to claim 1, characterized in that The step S1 specifically includes: Based on core observation, CT scanning and logging curve interpretation, a reservoir geological model is constructed, and refined core description and micro-macro test analysis are carried out to clarify the distribution patterns of reservoir porosity, permeability, oil saturation, temperature and pressure systems.

3. The method according to claim 1, characterized in that The step S2 specifically includes: Based on the finite element theory, the rock physical parameters obtained from rock mechanics experiments, combined with the reservoir in-situ stress parameters, and on the basis of the reservoir geological model, the initial distribution law of the reservoir three-dimensional stress field is obtained through in-situ stress balance calculation to construct the reservoir geomechanics model.

4. The method according to claim 3, characterized in that In step S2, the rock physical parameters include Young's modulus and Poisson's ratio.

5. The method according to claim 3, characterized in that: In step S2, the reservoir in-situ stress parameters include the maximum horizontal principal in-situ stress, the minimum horizontal principal in-situ stress and the vertical stress.

6. The method according to claim 1, characterized in that The step S3 specifically includes: Carry out long-term conductivity test experiments under different proppant types, mesh sizes, and sand concentrations to obtain conductivity charts under different proppant type combinations.

7. The method according to claim 1, characterized in that The step S4 specifically includes: Based on the reservoir geological model constructed in step S1, the reservoir geomechanical model constructed in step S2 and the proppant conductivity map obtained in step S3, according to the rock effective stress calculation principle, the seepage mechanics equation and the elastic mechanics equation are coupled and solved, the pore pressure field is solved by the seepage equation, the reservoir stress field is solved by the elastic mechanics equation, and the effective stress acting on the proppant is solved by the reservoir stress field, and the fracture conductivity is further solved.

8. The method according to claim 1, characterized in that The following steps are also included: Step S5: Based on the proppant conductivity chart obtained in step S3 and the variation pattern of fracture conductivity at different production stages predicted in step S4, and taking oil production as the target, the proppant type, mesh size and sand concentration corresponding to the reasonable fracture conductivity at different production stages are optimized.

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