Method for simulating and calculating fracturing soaking imbibition and replacement efficiency and optimal soaking time prediction model

Abstract

The invention relates to the technical field of petroleum engineering, in particular to a method for simulating and calculating fracturing soaking imbibition replacement efficiency and an optimal soaking time prediction model. Comprising the steps of extraction of geological and fluid basic characteristics of shale oil reservoirs, molecular dynamics simulation, establishment of a microscopic model, machine learning and the like, orthogonal design is carried out on key factors such as reservoir physical properties, fluid physical properties and fracturing construction parameters, systematic molecular dynamics simulation is carried out, and simulation results are analyzed and discussed. The law of the shale oil imbibition and replacement efficiency of the surfactant added into the fracturing fluid is obtained, then an imbibition prediction model combining molecular simulation and machine learning is established, and then the shale oil reservoir fracturing optimal soaking time is obtained; according to the method, the experiment frequency and cost can be reduced, and the development efficiency and economic benefits of shale oil reservoirs can be effectively improved.

Description

Technical Field

[0001] The invention relates to the technical field of petroleum engineering, and in particular to a method for simulating and calculating the efficiency of fracturing well soaking and absorption displacement, and a model for predicting the optimal well soaking time. Background Art

[0002] Before the shale oil fracturing horizontal well is put into production, it is often shut down first: on the one hand, it is necessary to shut down passively due to the pipeline laying work; on the other hand, considering the formation energy replenishment and oil-water replacement, the shut down is actively extended. Water injection or active water injection huff and puff development is an important technical measure for shale oil reservoirs to replenish formation energy and exert capillary imbibition to replace crude oil. It has been successfully applied in oil fields and mines. The mine practice shows that the production time after shutting down is greatly advanced, but the production increase effect varies greatly among wells. If the shut down time is too short, the imbibition oil recovery effect cannot be fully exerted, and the imbibition oil replacement cannot be fully carried out. If the shut down time is too long, it will affect the economic benefits of the oil well. At the same time, under different reservoir conditions and imbibition systems, the time for imbibition to reach equilibrium is different, and the shut down time is different. Therefore, in the process of imbibition oil recovery in oil fields and mines, it is particularly important to determine the efficiency of imbibition replacement and the reasonable shut down time.

[0003] The commonly used methods include empirical formula method, analytical method, and combination of numerical simulation and theoretical derivation;

[0004] The empirical formula method is a method of solving problems by summarizing and generalizing experience. In determining the soaking time, although the empirical formula method can improve work efficiency and the accuracy of problem solving, its limitations are also very obvious.

[0005] Lack of universal applicability: Empirical formulas are often based on past experience and may be obtained by optimizing the post-fracturing shut-in time and production data of shale oil wells that have undergone fracturing operations based on the unique geological characteristics and reservoir types of the target block; however, different reservoirs and geological conditions may require different shut-in times; therefore, an empirical formula based on specific conditions may not be fully applicable to other situations, especially when faced with new or complex reservoirs; Subjectivity and one-sidedness: The empirical formula method may be subjective and one-sided; this is because the establishment of empirical formulas often relies on personal experience, observation and judgment, which may be affected by personal knowledge, skills and experience levels; in addition, empirical formulas may only reflect some situations and factors, and cannot fully consider all variables that may affect the shut-in time.

[0006] Ignoring dynamic changes: Determining the soaking time is a dynamic process, which is affected by many factors, including reservoir conditions, injection pressure, formation permeability, etc. These dynamic changes are crucial to determining the optimal soaking time, and the empirical formula method often cannot accurately capture these changes.

[0007] Lack of theoretical support: Compared with formulas based on physical principles or mathematical models, empirical formulas often lack theoretical support; this means that their accuracy and reliability may be more difficult to verify and evaluate; in the absence of sufficient theoretical support, the use of empirical formulas to determine the soaking time may bring certain risks.

[0008] Although the analytical method can provide a relatively accurate and systematic calculation method for determining the soaking time, its limitations cannot be ignored.

[0009] Simplified assumptions: Analytical methods usually need to be based on a series of simplified assumptions, such as assuming that the reservoir is homogeneous and isotropic, or that the fracture distribution is regular; these assumptions may not match the actual complex reservoir conditions, resulting in deviations in the calculation results.

[0010] Nonlinearity and coupling effects: The actual soaking process may involve a variety of complex nonlinear effects and coupling effects, such as temperature changes, pressure changes, chemical reactions, etc. The analytical method may not be able to fully consider these factors.

[0011] Limitations of model applicability: Models established by analytical methods may be applicable under certain conditions but may not be applicable under other conditions; for example, for unconventional oil and gas reservoirs, such as shale gas or tight oil, traditional analytical methods may require further adjustment and optimization.

[0012] Computational complexity: For complex fracture networks and heterogeneous reservoirs, analytical methods may require complex mathematical derivations and calculations, which may be very difficult or even infeasible in practice.

[0013] Although the combination of numerical simulation and theoretical derivation has many advantages in determining the soaking time, and can more comprehensively consider various influencing factors and provide more accurate prediction results, it also has certain limitations.

[0014] Traditional numerical simulation methods may encounter difficulties when dealing with complex nonlinear relationships, resulting in a decrease in the accuracy of simulation results; theoretical derivations are usually based on a series of simplified assumptions to establish mathematical models; these assumptions may not fully reflect the complexity of the actual situation, thus affecting the prediction accuracy of the soaking time. Summary of the invention

[0015] In order to make up for the deficiencies of the prior art, the present invention provides a method for simulating and calculating the efficiency of fracturing well soaking and an optimal well soaking time prediction model to overcome the deficiencies of the prior art. The technical solution adopted by the present invention to solve the technical problems is:

[0016] The method for simulating and calculating the efficiency of fracturing well imbibition displacement includes the following steps:

[0017] S1: Understand basic characteristics: Understand the basic characteristics of shale reservoirs, including rock mineral composition, shale oil density, viscosity, and crude oil occurrence in micro-nano pore throat systems;

[0018] S2: Molecular dynamics simulation: A mixed level orthogonal table was used to arrange the molecular dynamics simulation, and three factors, mineral composition, pressure, and surfactant concentration, were selected;

[0019] S3: Establishing microscopic model: Using Materials Studio to establish a microscopic model of rock wall-oil phase-displacement phase;

[0020] S4: Data comparison: The imbibition efficiency of different simulation results is compared to obtain the effects of different reservoir types, pressures, and surfactant concentrations on the imbibition replacement efficiency during the soaking period, so as to analyze the optimal soaking pressure, corresponding soaking time, and surfactant mass fraction data to maximize the imbibition replacement efficiency.

[0021] Preferably, the mineral components in S2 are represented by dolomite, calcite and quartz, the pressures are 10 MPa, 20 MPa and 30 MPa respectively, and the surfactant is sodium dodecyl sulfate (SDBS). Based on this, an orthogonal design is performed to obtain 25 schemes for molecular dynamics simulation.

[0022] Preferably, the surfactant is sodium dodecyl sulfate (SDBS) with a concentration range of 0.1%-0.5%.

[0023] Preferably, the establishment of the microscopic model in S3 includes the following:

[0024] Geometry optimization to ensure that the model's geometry reaches the theoretical minimum energy state;

[0025] Relaxation, by adjusting the simulation conditions, makes the system reach dynamic stability;

[0026] Molecular dynamics simulation, which simulates the movement and interaction of molecules to make the system reach thermodynamic equilibrium;

[0027] The data obtained after the simulated equilibrium was analyzed, and the ratio of the total concentration of oil phase molecules outside the initial oil phase range to the total concentration of initial oil phase molecules was used to characterize the imbibition efficiency. The calculation formula is:

[0028]

[0029] Where η is the recovery factor; N total is the total amount of oil molecules in the initial oil phase before imbibition; N residual It is the residual oil molecular weight within the initial oil phase after imbibition.

[0030] The optimal soaking time prediction model includes a parameter input module, an orthogonal design simulation module, a parameter output module, a database and a prediction module;

[0031] The parameter input module is used to input shale oil reservoir reservoir parameters, fluid parameters, and production parameters;

[0032] The orthogonal design simulation module is used to select key factors affecting the efficiency of imbibition replacement during the well soaking period to conduct orthogonal design simulation experiments based on the parameters input by the parameter input module;

[0033] The parameter output module is used to output the relative concentration, interaction energy parameters, and displacement efficiency parameters generated by the molecular dynamics simulation of the simulation experiment of the orthogonal design simulation module;

[0034] The database is used to store and record data of the parameter input module, the orthogonal design simulation module, and the parameter output module;

[0035] The database also includes using a machine learning method to take the imbibition replacement efficiency of the fracturing fluid on the crude oil as the core target, and save and record the data between the corresponding influencing factors and the imbibition replacement efficiency;

[0036] The prediction module is used to construct a prediction model for determining the optimal soaking time after the parameters are input into the input module, based on the comparison and screening of the database information, so as to maximize the imbibition replacement efficiency of crude oil.

[0037] The beneficial effects of the present invention are as follows:

[0038] 1. The method for simulating and calculating the fracturing well soaking efficiency and the optimal soaking time prediction model described in the present invention, through orthogonal design of key factors such as reservoir properties, fluid properties, production parameters, etc., a systematic molecular dynamics simulation is carried out, and the data generated by the simulation are discussed and analyzed to obtain the imbibition displacement efficiency of the surfactant, and then machine learning is used to establish and train the prediction model, and then the optimal soaking time of horizontal wells in shale oil reservoir fracturing is obtained, which can reduce the number of experiments and costs, and can effectively improve the development efficiency and economic benefits of shale oil reservoirs. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The present invention will be further described below in conjunction with the accompanying drawings.

[0040] Figure 1 is a flow chart of the method of the present invention;

[0041] Figure 2 is the orthogonal design table of the present invention;

[0042] Figure 3 It is a molecular structure diagram of the system of the present invention. DETAILED DESCRIPTION

[0043] In order to make the technical means, creative features, objectives and effects achieved by the present invention easy to understand, the present invention is further explained below in conjunction with specific implementation methods.

[0044] like Figure 1 As shown, the present invention proposes a method for simulating and calculating the efficiency of fracturing well soaking and absorption displacement, which includes the following steps:

[0045] (1) First, we need to understand the basic characteristics of shale oil reservoirs.

[0046] The data for this application is taken from the post-pressure data analysis of the Yingxiongling shale reservoir.

[0047] The rock mineral composition in the Yingxiongling area is complex, with obvious mixed characteristics overall. The main minerals are calcite and dolomite, followed by clay, quartz and feldspar, etc. Among them, the clay mineral composition is mainly illite, accounting for 62.6%. The density of Yingxiongling shale oil is between 0.78g / cm3-0.86g / cm3, and the fluidity is good (viscosity is 4.86mPa·s).

[0048] The occurrence states of crude oil in micro-nano pore throat systems are diverse. Among them, the heavy and highly polar hydrocarbon components in the pores are mostly in an adsorbed state and are basically immobile; the light, small molecular weight, and weakly polar hydrocarbon components are mainly in a free state and are easy to flow.

[0049] According to data published by the National Institute of Standards and Technology (NIST), the physical properties of n-decane (C10H22) are consistent with the average density and viscosity of Yingxiongling crude oil, and the light oil component of Yingxiongling accounts for a large proportion. Therefore, this application selects n-decane as a representative substance of Yingxiongling crude oil for research.

[0050] (2) A mixed level orthogonal table was used to arrange the molecular dynamics simulation. Three factors, mineral composition, pressure, and surfactant concentration, were selected. Dolomite, calcite, and quartz were selected as representatives of the mineral composition. The pressures were 10 MPa, 20 MPa, and 30 MPa, respectively. The concentration of the surfactant sodium dodecyl sulfate (SDBS) was in the range of 0.1% to 0.5%. Based on this, an orthogonal design was performed to obtain 25 schemes for molecular dynamics simulation. The design schemes are as follows: Figure 2 shown.

[0051] (3) Use Materials Studio to create Figure 3The microscopic model of rock wall-oil phase-displacement phase shown in the figure has three key steps in its simulation process: first, geometry optimization to ensure that the geometry of the model reaches the theoretical minimum energy state; second, relaxation to achieve dynamic stability of the system through simulation under certain conditions; and finally, molecular dynamics simulation to achieve thermodynamic equilibrium of the system through simulating the movement and interaction of molecules.

[0052] The data obtained after the simulated equilibrium was analyzed, and the ratio of the total concentration of oil phase molecules outside the initial oil phase range to the total concentration of initial oil phase molecules was used to characterize the imbibition efficiency. The calculation formula is:

[0053]

[0054] Where η is the recovery factor; N total is the total amount of oil molecules in the initial oil phase before imbibition; N residual It is the residual oil molecular weight within the initial oil phase after imbibition.

[0055] The imbibition efficiency of different simulation results was compared to obtain the effects of different reservoir types, pressures, and surfactant concentrations on the imbibition displacement efficiency during the soaking period. Among them, the migration distance of alkanes in different pores is calcite>montmorillonite>quartz, so the imbibition displacement capacity is best in calcite pores. The greater the pressure, the greater the displacement efficiency. Surfactants can effectively improve the rheology of crude oil. With the increase of surfactant content, the oil displacement rate will show an increasing trend. When the surfactant mass fraction is 0.3%, the oil displacement rate reaches the maximum value. Continuing to increase the surfactant content, the oil displacement rate remains basically stable.

[0056] Therefore, by comprehensively analyzing the imbibition replacement efficiency based on reservoir type, well-insulating pressure, and surfactant mass fraction, it was found that in pores containing a large amount of calcite, the imbibition replacement efficiency is maximum when the well-insulating pressure is 30 MPa, the corresponding well-insulating time is about 17-21 days, and the surfactant mass fraction is 0.3%. Combined with the actual production situation of Yingxiongling, the simulation results are in good agreement.

[0057] The optimal soaking time prediction model includes a parameter input module, an orthogonal design simulation module, a parameter output module, a database and a prediction module;

[0058] The parameter input module is used to input data such as shale oil reservoir reservoir parameters, fluid parameters, production parameters, etc.

[0059] The orthogonal design simulation module is used to select key factors affecting the efficiency of imbibition replacement during the well soaking period to conduct orthogonal design simulation experiments based on the parameters input by the parameter input module;

[0060] The parameter output module is used to output data such as relative concentration, interaction energy parameters, and displacement efficiency parameters generated by the molecular dynamics simulation of the simulation experiment of the orthogonal design simulation module;

[0061] The database is used to store and record data of the parameter input module, the orthogonal design simulation module, and the parameter output module;

[0062] The database also includes using a machine learning method to take the imbibition replacement efficiency of the fracturing fluid on the crude oil as the core target, and save and record the data between the corresponding influencing factors and the imbibition replacement efficiency;

[0063] The prediction module is used to construct a prediction model for determining the optimal well-clogging time according to the comparison and screening of database information after the parameters are input into the input module, so as to maximize the imbibition replacement efficiency of crude oil. With the continuous entry and prediction of data, more and more database information is available. After the parameters are input into the module at a later stage, the imbibition replacement efficiency of crude oil and the well-clogging data can be directly retrieved from the database. The operation is simple and convenient, and the prediction is accurate.

[0064] The present invention first clarifies the reservoir properties, fluid properties, and production characteristics of the shale oil reservoir to provide key parameters for subsequent simulations; then, using an orthogonal design table, a number of factors such as crude oil composition, rock properties, nanopore size, pressure, and surfactant concentration are comprehensively considered to perform a systematic molecular dynamics simulation to obtain the imbibition replacement efficiency under different systems; finally, machine learning technology is used to determine the optimal shut-in time for fracturing horizontal wells in shale oil reservoirs by establishing and training a prediction model; the number of experiments and costs are reduced, providing an effective solution for optimizing the shut-in time, thereby increasing crude oil recovery and reducing resource waste.

[0065] The above shows and describes the basic principles, main features and advantages of the present invention. It should be understood by those skilled in the art that the present invention is not limited to the above embodiments. The above embodiments and descriptions are only for explaining the principles of the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, which fall within the scope of the present invention. The scope of protection of the present invention is defined by the attached claims and their equivalents.

Claims

1. A method for simulating and calculating the efficiency of fracturing well soaking and absorption displacement, characterized in that: The steps include: S1: Understand basic characteristics: Understand the basic characteristics of shale reservoirs, including rock mineral composition, shale oil density, viscosity, and crude oil occurrence in micro-nano pore throat systems; S2: Molecular dynamics simulation: A mixed level orthogonal table was used to arrange the molecular dynamics simulation, and three factors, mineral composition, pressure, and surfactant concentration, were selected; S3: Establishing microscopic model: Using Materials Studio to establish a microscopic model of rock wall-oil phase-displacement phase; S4: Data comparison: The imbibition efficiency of different simulation results is compared to obtain the effects of different reservoir types, pressures, and surfactant concentrations on the imbibition replacement efficiency during the soaking period, so as to analyze the optimal soaking pressure, corresponding soaking time, and surfactant mass fraction data to maximize the imbibition replacement efficiency.

2. The method for simulating and calculating the efficiency of fracturing well soaking according to claim 1, characterized in that: The mineral components in S2 are represented by dolomite, calcite, and quartz, the pressures are 10 MPa, 20 MPa, and 30 MPa, respectively, and the surfactant is sodium dodecyl sulfate (SDBS). Based on this, an orthogonal design is performed to obtain 25 schemes for molecular dynamics simulation.

3. The method for simulating and calculating the efficiency of fracturing well soaking according to claim 2, characterized in that: The surfactant is sodium dodecyl sulfate (SDBS) with a concentration range of 0.1%-0.5%.

4. The method for simulating and calculating the efficiency of fracturing well soaking according to claim 1, characterized in that: The establishment of the microscopic model described in S3 includes the following: Geometry optimization to ensure that the model's geometry reaches the theoretical minimum energy state; Relaxation, by adjusting the simulation conditions, makes the system reach dynamic stability; Molecular dynamics simulation, which simulates the movement and interaction of molecules to make the system reach thermodynamic equilibrium; The data obtained after the simulated equilibrium was analyzed, and the ratio of the total concentration of oil phase molecules outside the initial oil phase range to the total concentration of initial oil phase molecules was used to characterize the imbibition efficiency. The calculation formula is: Where η is the recovery factor; N total is the total amount of oil molecules in the initial oil phase before imbibition; N residual It is the residual oil molecular weight within the initial oil phase after imbibition.

5. The optimal shut-in time prediction model is applicable to the method for simulating and calculating the fracturing shut-in efficiency of imbibition and displacement as described in any one of claims 1 to 4, characterized in that: It includes parameter input module, orthogonal design simulation module, parameter output module, database and prediction module; The parameter input module is used to input shale oil reservoir reservoir parameters, fluid parameters, and production parameters; The orthogonal design simulation module is used to select key factors affecting the efficiency of imbibition replacement during the well soaking period to conduct orthogonal design simulation experiments based on the parameters input by the parameter input module; The parameter output module is used to output the relative concentration, interaction energy parameters, and displacement efficiency parameters generated by the molecular dynamics simulation of the simulation experiment of the orthogonal design simulation module; The database is used to store and record data of the parameter input module, the orthogonal design simulation module, and the parameter output module; The database also includes using a machine learning method to take the imbibition replacement efficiency of the fracturing fluid on the crude oil as the core target, and save and record the data between the corresponding influencing factors and the imbibition replacement efficiency; The prediction module is used to construct a prediction model for determining the optimal soaking time after the parameters are input into the input module, based on the comparison and screening of the database information, so as to maximize the imbibition replacement efficiency of crude oil.

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