Prediction method for the evolution of the conductivity of artificial fractures considering dynamic geostress fields

Abstract

This invention belongs to the field of hydraulic fracturing technology in oil development, and discloses a method for predicting the evolution of the conductivity of artificial fractures considering dynamic geostress fields. The method specifically includes the following steps: constructing a reservoir geological model; constructing a reservoir geomechanical model; conducting long-term proppant conductivity tests to obtain proppant conductivity maps; based on the constructed reservoir geological model and geomechanical model, combined with the proppant conductivity map data, using fluid-structure interaction reservoir simulation technology, predicting the reservoir stress field and pore pressure field during the fracturing production process, and predicting the fracture conductivity at different production stages. This invention can more comprehensively characterize the changes in fracture conductivity under dynamic production conditions, thus providing support for optimizing fracture conductivity in fracturing wells and selecting proppant types.

Description

Technical Field

[0001] This invention belongs to the field of hydraulic fracturing technology in oil development, specifically relating to the optimization of fracture conductivity during hydraulic fracturing, and particularly to a method for predicting the evolution of artificial fracture conductivity considering dynamic geostress fields. Background Technology

[0002] Hydraulic fracturing technology, as a primary technique for unconventional oil and gas resource development, plays a crucial role in the development of tight oil and gas, shale oil and gas, and other similar resources. Selecting an appropriate fracture conductivity is essential for improving hydraulic fracturing effectiveness and reducing costs. Currently, optimizing fracture conductivity during hydraulic fracturing primarily involves using single-well production as the objective function and comparing production rates under different fracture conductivity levels. However, this method sets the hydraulic fracture conductivity as a static value, failing to consider issues such as insufficient fluid supply to the well due to changes in the stress field and pore pressure field during fracturing and well production. This leads to the designed fracture conductivity exceeding actual production needs in the later stages of production, resulting in wasted conductivity (the greater the conductivity, the more proppant is required, and the higher the production costs). Therefore, establishing a numerical simulation and prediction model of the stress field and pore pressure field in the fracturing-production process throughout the entire life cycle, quantitatively characterizing the stress field variation law in the fracturing-production process, and predicting the flow conduction capacity of hydraulic fractures to meet production needs are of great significance for reducing hydraulic fracturing costs and improving hydraulic fracturing effects.

[0003] Currently, there is no established numerical simulation model for hydraulic fracturing that can accurately predict the reservoir in-situ stress field and pore pressure field throughout the entire fracturing-production life cycle, making it difficult to optimize the dynamic conductivity of hydraulic fracturing fractures.

[0004] Chinese patent CN110410054A discloses a method for predicting the spatiotemporal conductivity of fractures in coalbed methane wells, comprising the following steps: S1, collecting relevant parameters of the reservoir rock of the target coalbed methane well; S2, calibrating the spatial distribution of the global conductivity of the propped fracture at the initial moment after fracturing; S3, establishing a mechanistic expression for the influence of coal powder deposition, proppant embedding, and time on the conductivity of proppant-filled fractures; S4, based on 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, using 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 proppant-filled fractures. However, the method in this patent uses a fixed value for the minimum horizontal principal stress when calculating the effective stress on the fracture wall. It also fails to consider setting the hydraulic fracture conductivity to a static value and does not take into account the problems of insufficient fluid supply capacity of oil and gas wells caused by changes in the stress field and pore pressure field during fracturing and oil well production. This does not conform to the actual geostress evolution process in engineering and cannot accurately predict the fracture conductivity. As a result, the designed fracture conductivity will be greater than the actual production demand in the later stages of production, thus wasting the fracture conductivity.

[0005] Chinese patent CN114482971A discloses a method and application for selecting proppant for fracturing stimulation of unconventional reservoirs. The selection method includes: using reservoir numerical simulation software to obtain the required conductivity to achieve full reservoir utilization under different fracture spacings; obtaining the formation closure stress value using actual well fracturing operation curves; simultaneously obtaining the effective closure stress variation value by combining actual fracturing well production curves; then conducting long-term conductivity tests under simulated actual stress conditions to obtain the conductivity value of propped fractures under different proppant combinations; and finally using fracturing software simulation to obtain parameters such as proppant type, particle size, and dosage that can meet the reservoir's full utilization requirements. This patented method can guide the selection of proppants under different stimulation modes in unconventional reservoirs, improving the fracturing stimulation effect of unconventional reservoirs. However, the method of this patent obtains the construction data of the fracturing and stimulation wells in the study block, obtains the effective closure stress parameters of the reservoir in the study block through the analysis of fracturing construction data, and then applies the parameters to the experimental simulation process. This method has a prediction lag, which affects the prediction accuracy. It cannot accurately characterize and predict the changes in fracture conductivity under the dynamic production of the entire life cycle. In addition, it has the problems of difficulty in obtaining basic experimental data and high optimization costs. Summary of the Invention

[0006] This invention aims to address the problems of unclear dynamic evolution of reservoir stress field and pore pressure field during the production process of existing fracturing wells, and unclear dynamic conductivity variation of hydraulically fractured wells. It provides a method for predicting the evolution of artificial fracture conductivity considering dynamic geostress field. Based on the geological model before reservoir fracturing, it constructs a fluid-structure interaction fracturing-production integrated numerical simulation model to obtain the variation law of reservoir geostress field throughout the fracturing-production process, and to obtain the variation law of fracture conductivity caused by stress field changes throughout the entire life cycle. This method can more comprehensively characterize the change of fracture conductivity under production dynamics, thereby providing support for optimizing fracture conductivity and proppant selection in fracturing wells.

[0007] To achieve the above technical objectives, the present invention adopts the following technical solution:

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

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

[0010] Step S2: Based on the constructed reservoir geological model, construct the reservoir geomechanical model;

[0011] Step S3: Conduct long-term proppant conductivity test experiments to obtain conductivity charts under different proppant type combinations;

[0012] 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 during the fracturing production process are predicted using fluid-structure interaction reservoir simulation technology, and the fracture conductivity at different production stages is predicted.

[0013] Further, step S1 specifically includes:

[0014] Based on core observation, CT scans, and well logging curve interpretation, a reservoir geological model was constructed, and refined core description and micro-macro testing and analysis were carried out to clarify the distribution patterns of reservoir porosity, permeability, oil saturation, temperature, and pressure.

[0015] Further, step S2 specifically includes:

[0016] Based on the finite element theory, using the rock physics parameters obtained from rock mechanics experiments and combined with the reservoir geostress parameters, the initial distribution law of the three-dimensional stress field of the reservoir is obtained through geostress equilibrium calculation on the basis of the reservoir geological model, and the reservoir geomechanical model is constructed.

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

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

[0019] Furthermore, step S3 specifically includes:

[0020] Long-term conductivity tests were conducted under different proppant types, mesh sizes, and sand concentrations to obtain conductivity charts under different proppant type combinations.

[0021] Furthermore, 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 chart obtained in step S3, the seepage mechanics equation and the elastic mechanics equation are coupled and solved according to the principle of rock effective stress calculation. 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. The fracture conductivity is then further solved.

[0023] Furthermore, it also includes the following steps:

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

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

[0026] (1) This invention constructs a high-precision reservoir geological model and reservoir geomechanical model. Based on the reservoir pre-fracturing geological model, it constructs a fluid-solid coupling fracturing-production integrated numerical simulation model to obtain the reservoir stress field variation law throughout the fracturing-production process and the fracture conductivity variation law caused by stress field changes throughout the entire life cycle. This can more comprehensively and accurately characterize the fracture conductivity variation under production dynamics, thereby providing theoretical support for the optimization of fracture conductivity and proppant selection in fracturing wells.

[0027] (2) Based on fluid-structure interaction reservoir simulation technology, this invention predicts the fracture conductivity at different production stages, obtains the variation law of reservoir stress field and pore pressure field during the fracturing process, and optimizes the reasonable fracture conductivity at different production stages. The research results are of great significance for optimizing the reasonable fracture conductivity, optimizing the amount of proppant added during hydraulic fracturing, and selecting the type of proppant. It plays an important role in promoting the replacement of ceramsite with quartz sand in deep oil and gas hydraulic fracturing, and provides an important technical guarantee for the efficient development of unconventional oil and gas resources. It is also economically viable.

[0028] (3) In the early stage of oilfield development, the method of this invention can quickly establish a set of fracturing fracture conductivity optimization methods. Combined with field practice, it can quickly improve the understanding of the stress field characteristics of the reservoir in the target block and the formation pressure change characteristics during the production process, and finally obtain the engineering parameters that meet the conductivity requirements. This method can be promoted and applied throughout the entire production area to be built, thereby guiding the entire production area to quickly establish differentiated fracturing schemes, optimize fracturing processes and key parameters in a targeted manner, and make construction operations more convenient and efficient. Attached Figure Description

[0029] Figure 1 This is a flowchart of a method for predicting the evolution of the conductivity of artificial fractures considering a dynamic geostress field, as described in an embodiment of the present invention.

[0030] Figure 2 This is a flowchart illustrating the process of selecting the optimal proppant for matching the flow-guiding capacity of artificial fractures in an embodiment of the present invention;

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

[0032] Figure 4 This is 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 diagram showing the flow conductivity of 20 / 40 mesh ceramsite under different sand concentrations in an embodiment of the present invention.

[0034] Figure 6 This is a diagram 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 diagram showing the flow conductivity of 40 / 70 mesh ceramsite under different sand concentrations in an embodiment of the present invention.

[0036] Figure 8 This is a diagram showing the flow conductivity of 20 / 40 mesh quartz sand under different sand-laying concentrations in an embodiment of the present invention.

[0037] Figure 9This is a diagram showing the flow conductivity of 30 / 50 mesh quartz sand under different sand-laying concentrations in an embodiment of the present invention.

[0038] Figure 10 This is a diagram showing the flow conductivity of 40 / 70 mesh quartz sand under different sand-laying concentrations in an embodiment of the present invention.

[0039] Figure 11 This is a stress field variation diagram of a typical reservoir fracturing-production life cycle in an embodiment of the present invention;

[0040] Figure 12 This is a comparison chart of reservoir recovery rates under different flow conduction capacities in embodiments of the present invention. Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] Example 1

[0043] This invention provides a method for predicting the evolution of the conductivity of artificial fractures considering a dynamic geostress field, such as... Figure 1 As shown, the method specifically includes the following steps:

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

[0045] Based on core observation, CT scans, and well logging curve interpretation, a reservoir geological model was constructed, and refined core description and micro-macro testing and analysis were carried out to clarify the distribution patterns of reservoir porosity, permeability, oil saturation, temperature, and pressure.

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

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

[0048] Step S3: Conduct long-term conductivity tests under different proppant types, mesh sizes, and sand concentrations to obtain conductivity charts under different proppant type combinations.

[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 during the fracturing production process are predicted using fluid-structure interaction reservoir simulation technology, and the fracture conductivity at 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 chart obtained in step S3, the seepage mechanics equation and the elastic mechanics equation are coupled and solved according to the principle of rock effective stress calculation. 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. The fracture conductivity is then further solved.

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

[0052] Example 2

[0053] This invention applies the method from Example 1 to a tight sandstone reservoir for proppant conductivity demand analysis. It proposes a method to match formation conductivity using cumulative oil recovery (CRR) or recovery rate as the objective function, and combines laboratory proppant conductivity experiments to optimize proppant selection. The detailed method and flowchart for matching artificial fracture conductivity and optimizing proppant selection are shown below. Figure 2 As shown.

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

[0055] The required mesh parameters are extracted directly from the coarsened geological model; the parameters required during the model building process include: reservoir depth, porosity, permeability, net-to-gross ratio, saturation, pore pressure, etc.

[0056] The established high-precision reservoir geological model, such as Figure 3 As shown.

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

[0058] Based on the reservoir geological model, rock physical parameters and reservoir stress parameters are added to the model, mainly including parameters such as maximum horizontal principal stress, minimum horizontal principal stress, vertical stress, Young's modulus, and Poisson's ratio, to construct a reservoir geomechanical model.

[0059] The established three-dimensional reservoir geomechanical model of a typical well is as follows: Figure 4 As shown.

[0060] Step S3: Conduct long-term flowability tests on proppant types, mesh sizes, and sand concentrations to generate proppant flowability charts;

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

[0062] Effective conductivity of fractures is key to evaluating the success or failure of fracturing. Indoor experiments were conducted with different types of proppant and different sand concentrations to show how conductivity changes with increasing closure pressure. Through a large number of experiments, conductivity charts were created to facilitate the selection of proppant type and sand concentration.

[0063] The sand concentration in the experiment was 4.5 kg / m³. 2 ~12.5kg / m 2 Each group increases by 0.5 kg / m 2 The experimental temperature simulated the formation temperature at 60℃, and the fluid was distilled water. Due to the strength differences between ceramsite and quartz sand, the closing pressure was different during the test.

[0064] The final experimental results are as follows Figure 5-10 As shown, 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 reservoir stress field and pore pressure field during the fracturing production process are predicted using fluid-structure interaction reservoir simulation technology, and the fracture conductivity at different production stages is predicted.

[0082] The simulation results of stress field changes throughout the entire life cycle of reservoir fracturing and production are as follows: Figure 11 As shown in the figure. Based on the stress field variation law, the recovery degree was predicted and analyzed under different conductivity levels at different mining time periods. The increase in recovery degree gradually decreased with the increase of reservoir proppant conductivity. Simulations of recovery degree at different times using different conductivity levels are shown in the figure. Figure 12 As shown, the flow capacity is basically at the critical point when it is greater than 30 mD·m, and the preferred long-term flow capacity value is 30 mD·m.

[0083] Step S5: Based on the proppant conductivity chart in Step S3 and the reasonable fracture conductivity variation patterns at different production stages in Step S4, with oil production as the target, the optimal engineering parameters for fracturing in this area are: proppant type is quartz sand, mesh size is 40 / 70 mesh, and proppant concentration is 7.5 kg / m³. 2 .

[0084] The above description is merely an embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, and improvements made within the scope of the present invention should be included within the protection scope of the present invention.

Claims

1. A method for predicting the evolution of the conductivity of artificial fractures considering a dynamic geostress field, characterized in that, The method specifically includes the following steps: Step S1: Construct a reservoir geological model; Step S2: Based on the constructed reservoir geological model, construct the reservoir geomechanical model; Step S3: Conduct long-term proppant conductivity test experiments to obtain conductivity charts under different proppant type combinations; 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 during the fracturing production process are predicted using fluid-structure interaction reservoir simulation technology, and the fracture conductivity at different production stages is predicted. Specifically, step S3 includes: Long-term conductivity tests were conducted under different proppant types, mesh sizes, and sand concentrations to obtain conductivity charts under different proppant type combinations. Specifically, step S4 includes: Based on the reservoir geological model constructed in step S1, the reservoir geomechanical model constructed in step S2, and the proppant conductivity chart obtained in step S3, the seepage mechanics equation and the elastic mechanics equation are coupled and solved according to the principle of rock effective stress calculation. 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. The fracture conductivity is then further solved.

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

3. The method according to claim 1, characterized in that, Step S2 specifically includes: Based on the finite element theory, using the rock physics parameters obtained from rock mechanics experiments and combined with the reservoir geostress parameters, the initial distribution law of the three-dimensional stress field of the reservoir is obtained through geostress equilibrium calculation on the basis of the reservoir geological model, and the reservoir geomechanical model is constructed.

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 stress parameters include the maximum horizontal principal stress, the minimum horizontal principal stress, and the vertical stress.

6. The method according to claim 1, characterized in that, It also includes the following steps: Step S5: Based on the proppant conductivity chart obtained in Step S3 and the predicted fracture conductivity variation pattern in Step S4, with oil production as the target, select the appropriate proppant type, mesh size and sand concentration corresponding to the reasonable fracture conductivity in different production stages.

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