Method for simulating calculation of fracturing soak absorption replacement efficiency and optimal soak time prediction model

Abstract

The present application relates to the technical field of petroleum engineering, in particular to a method for simulating and calculating the imbibition displacement efficiency of fracturing soak wells and a prediction model for optimal soak time; including the steps of extracting the basic characteristics of geology and fluid of shale oil reservoir, molecular dynamics simulation, establishing a micro model, machine learning, etc., by orthogonal design of key factors such as reservoir physical properties, fluid physical properties and fracturing construction parameters, systematic molecular dynamics simulation is carried out, the simulation results are analyzed and discussed, the law of the imbibition displacement efficiency of shale oil with the addition of surfactant in fracturing fluid is obtained, then the imbibition prediction model combined with molecular simulation and machine learning is established, and the optimal soak time of shale oil reservoir fracturing is obtained; the present application can reduce the number of experiments and cost, and can effectively improve the development efficiency and economic benefit of shale oil reservoir.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of petroleum engineering, in particular to a method for simulating and calculating the soaking and imbibition displacement efficiency of fracturing and a prediction model for optimal soaking time. BACKGROUND

[0002] Shale oil fracturing horizontal wells often soak before production: on the one hand, due to pipeline laying work, passive soaking is needed; on the other hand, active soaking is considered to supplement the formation energy and oil-water displacement. Water injection or active water injection is an important technical measure to supplement the formation energy and to develop the capillary imbibition displacement of crude oil in shale oil reservoirs, which has been successful in oilfield applications. Field practice shows that the production time after soaking is greatly advanced, but the production increase effect varies greatly among wells. If the soaking time is too short, the imbibition oil recovery cannot be fully played, and the imbibition displacement of crude oil cannot be fully carried out. If the soaking time is too long, it will affect the economic benefit of the oil well. At the same time, under different reservoir conditions and imbibition systems, the time to reach the equilibrium state of imbibition is different, and the soaking time is different. Therefore, in the process of imbibition oil recovery in oilfields, it is particularly important to determine the imbibition displacement efficiency and the reasonable soaking time.

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

[0004] The empirical formula method is a method for solving problems by summarizing and inducing experience. In the determination of 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 formula is often based on past experience and may be based on the unique geological characteristics and reservoir types of the target block, obtained by optimizing the soaking time and production data of shale oil wells after fracturing operation. However, different reservoirs and geological conditions may require different soaking times. Therefore, an empirical formula based on specific conditions may not be completely applicable to other situations, especially when facing new or complex reservoirs. Subjectivity and one-sidedness: empirical formula method may have certain subjectivity and one-sidedness. This is because the establishment of empirical formula often depends on personal experience, observation and judgment, which may be affected by individual knowledge, skills and experience level. In addition, empirical formula may only reflect part of the situation and factors, and cannot fully consider all variables that may affect the soaking time.

[0006] Ignoring dynamic changes: the determination of 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 for 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; without sufficient theoretical support, using empirical formulas to determine soak time may pose certain risks.

[0008] Analytical methods in determining soak time, although can provide a relatively accurate and systematic calculation method, but its limitations can not 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, isotropic, or that the fracture distribution is regular; these assumptions may not match the actual complex reservoir conditions, leading to deviations in the calculation results.

[0010] Nonlinear and coupling effects: Actual soak processes may involve a variety of complex nonlinear effects and coupling effects, such as temperature changes, pressure changes, chemical interactions, etc., which may not be fully considered by analytical methods.

[0011] Model applicability limitations: The 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 need 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 impossible to implement in practice.

[0013] Numerical simulation combined with theoretical derivation in determining soak time has many advantages, can more comprehensively consider various influencing factors, and provide more accurate prediction results, but also has certain limitations.

[0014] Traditional numerical simulation methods may encounter difficulties in dealing with complex nonlinear relationships, leading to a decrease in the accuracy of simulation results; theoretical derivation is usually based on a series of simplified assumptions to establish mathematical models; these assumptions may not fully reflect the complexity of the actual situation, thereby affecting the accuracy of the prediction of soak time. SUMMARY

[0015] In order to make up for the shortcomings of the prior art, the present application provides a method for simulating and calculating the soak efficiency of fracturing soak and a model for predicting the optimal soak time, in order to overcome the shortcomings of the prior art, the technical scheme adopted by the present application to solve its technical problems is:

[0016] The method for simulating and calculating the soak efficiency of fracturing soak comprises the following steps:

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

[0018] S2: Molecular dynamics simulation: using a mixed level orthogonal table to arrange molecular dynamics simulation, selecting mineral composition, pressure, and surfactant concentration as three factors;

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

[0020] S4: Data comparison: comparing the imbibition efficiency of different simulation results, obtaining the influence of different reservoir types, pressure, and surfactant concentration on the imbibition displacement efficiency during the soak period, so as to analyze that the best data of soak pressure, corresponding soak time, and surfactant mass fraction can maximize the imbibition displacement efficiency.

[0021] Preferably, the mineral composition in S2 is selected from dolomite, calcite, and quartz as representatives, the pressure is 10 Mpa, 20 Mpa, and 30 Mpa, respectively, and the surfactant is sodium dodecyl sulfonate (SDBS). According to this, 25 schemes are obtained for molecular dynamics simulation.

[0022] Preferably, the concentration of the surfactant sodium dodecyl sulfonate (SDBS) is in the range of 0.1% to 0.5%.

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

[0024] Geometry optimization to ensure that the geometric structure of the model reaches the theoretical minimum energy state;

[0025] Relaxation to make the system reach dynamic stability by adjusting the simulation conditions;

[0026] Molecular dynamics simulation to make the system reach thermodynamic equilibrium by simulating the motion and interaction of molecules;

[0027] Analyzing the data obtained after simulation equilibrium, taking 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 as the representation of imbibition efficiency, and the calculation formula is:

[0028]

[0029] In the formula, η is the recovery rate; N total is the total amount of oil molecules in the initial oil phase range before imbibition; N residual is the residual oil molecule amount in the initial oil phase range after imbibition.

[0030] The optimal soaking time prediction model comprises 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 for inputting shale reservoir parameters, fluid parameters and production parameters.

[0032] The orthogonal design simulation module is used for selecting key factors affecting the soaking period and the imbibition displacement efficiency as objects of the parameters input by the parameter input module to perform orthogonal design simulation experiments.

[0033] The parameter output module is used for outputting the relative concentration, interaction energy parameters and displacement efficiency parameters generated by the molecular dynamics simulation of the simulation experiments of the orthogonal design simulation module.

[0034] The database is used for saving and recording the data of the parameter input module, the orthogonal design simulation module and the parameter output module.

[0035] The database further comprises using a machine learning method to save and record the data between the corresponding influencing factors and the imbibition displacement efficiency of the fracturing fluid to the crude oil as a core target.

[0036] The prediction module is used for constructing a prediction model for determining the optimal soaking time after comparing and screening the database information according to the input parameters of the input module, so as to maximize the imbibition displacement efficiency of the crude oil.

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

[0038] 1. The simulation calculation fracturing soaking imbibition displacement efficiency method and the optimal soaking time prediction model can reduce the number of experiments and costs, effectively improve the development efficiency and economic benefits of shale reservoirs by performing orthogonal design on key factors such as reservoir properties, fluid properties and production parameters, performing systematic molecular dynamics simulation, discussing and analyzing the generated data to obtain the imbibition displacement efficiency of the surfactant, then establishing and training a prediction model by using machine learning, and then obtaining the optimal soaking time of the shale reservoir fracturing horizontal well. BRIEF DESCRIPTION OF DRAWINGS

[0039] The present application will be further described below with reference to the accompanying drawings.

[0040] Figure 1 is a method flowchart of the present application;

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

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

[0043] In order to make the technical means, creative features, purposes and effects of the present application easy to understand, the present application is further described below in combination with specific embodiments.

[0044] As shown in Figure 1 , the present application proposes a method for simulating calculation of fracturing soak absorption replacement efficiency, which comprises the following steps:

[0045] (1) First, the basic characteristics of shale oil reservoirs should be understood.

[0046] The data source of the present application is taken from the post-press data system analysis of Yingxiongling shale oil reservoir.

[0047] The rock mineral components in Yingxiongling area are complex, and the overall mixing and accumulation characteristics are obvious. 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 0.78 g / cm3-0.86 g / cm3, and the flowability is good (viscosity is 4.86 mPa·s).

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

[0049] According to the 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 proportion of light oil components in Yingxiongling is large, so the present application selects n-decane as a representative substance of Yingxiongling crude oil for research.

[0050] (2) The mixed horizontal orthogonal table is used to arrange the molecular dynamics simulation. Three factors of mineral composition, pressure and surfactant concentration are selected. The mineral composition selects dolomite, calcite and quartz as representatives, the pressure is 10 Mpa, 20 Mpa and 30 Mpa respectively, the surfactant is sodium dodecyl sulfonate (SDBS), and the concentration range is 0.1%-0.5%. According to this, 25 kinds of schemes are obtained to carry out molecular dynamics simulation, and the design scheme is shown in Figure 2 .

[0051] (3) Materials Studio is used to establish as shown in Figure 3The micro model of the rock wall-oil phase-displacement phase shown, the simulation process includes three key steps: firstly, geometry optimization, ensuring that the geometry of the model reaches the theoretical minimum energy state; secondly, relaxation, by simulating under certain conditions, the system reaches dynamic stability; and finally, molecular dynamics simulation, by simulating the motion and interaction of molecules, the system reaches thermodynamic equilibrium.

[0052] The data obtained after the simulation equilibrium is 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 is used to represent the imbibition efficiency, and the calculation formula is:

[0053]

[0054] In the formula, η is the recovery rate; N total is the total amount of oil molecules in the initial oil phase range before imbibition; N residual is the amount of residual oil molecules in the initial oil phase range after imbibition.

[0055] The imbibition efficiency of different simulation results is compared, and the influence of different reservoir types, pressure, and surfactant concentration on the imbibition displacement efficiency during the soak period is obtained, wherein the migration distance of different pore alkanes is in the order of calcite>montmorillonite>quartz, so the imbibition displacement ability in calcite pore is the best, the greater the pressure, the greater the displacement efficiency, the surfactant can effectively improve the rheological property of crude oil, and with the increase of the content of the surfactant, the oil displacement rate will show an increasing trend, and when the mass fraction of the surfactant is 0.3%, the oil displacement rate reaches the maximum value. Continue to increase the content of the surfactant, and the oil displacement rate remains basically stable.

[0056] Therefore, by analyzing the imbibition displacement efficiency of reservoir type, soak pressure, and surfactant mass fraction, it is obtained that in the calcite pore containing a large amount of calcite, the soak pressure is 30Mpa, the corresponding soak time is about 17-21 days, and the mass fraction of the surfactant is 0.3%, the imbibition displacement efficiency is the largest, and combined with the actual production situation of Yingxiongling, the simulation result has good consistency.

[0057] The optimal soak time prediction model comprises 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 shale oil reservoir parameters, fluid parameters, production parameters and other data;

[0059] The orthogonal design simulation module is used to select the key factors affecting the imbibition displacement efficiency during the soak period as the object of the parameter input module, and perform orthogonal design simulation experiment;

[0060] The parameter output module is used for outputting the relative concentration, interaction energy parameter, displacement efficiency parameter and other data generated by the molecular dynamics simulation of the orthogonal design simulation module simulating experiments.

[0061] The database is used for saving and recording the data of the parameter input module, the orthogonal design simulation module and the parameter output module.

[0062] The database further comprises a machine learning method, takes the imbibition displacement efficiency of the fracturing fluid to the crude oil as a core target, and saves and records the data between the corresponding influencing factors and the imbibition displacement efficiency.

[0063] The prediction module is used for constructing a prediction model for determining the optimal soaking time after the input module inputs parameters, and comparing and screening according to the database information, so as to maximize the imbibition displacement efficiency of the crude oil.

[0064] The present application first determines the reservoir properties, fluid properties and production characteristics of shale oil reservoirs, provides key parameters for subsequent simulation, then uses an orthogonal design table, comprehensively considers multiple factors such as crude oil components, rock properties, nanopore size, pressure and surfactant concentration, performs systematic molecular dynamics simulation, obtains imbibition displacement efficiency under different systems, finally adopts machine learning technology, establishes and trains a prediction model, and determines the optimal soaking time of the shale oil reservoir fracturing horizontal well.

[0065] The basic principles, main features and advantages of the present application are shown and described above. It should be understood by those skilled in the art that the present application is not limited by the above examples, and the above examples and descriptions in the specification are only to illustrate the principles of the present application. Without departing from the spirit and scope of the present application, various changes and improvements can be made to the present application, and these changes and improvements all fall within the scope of the present application. The scope of protection of the present application is defined by the appended claims and their equivalents.

Claims

1. A method for simulating the efficiency of the imbibition displacement during the fracturing soak period, characterized in that, It comprises the following steps: S1: understanding basic characteristics: understanding the basic characteristics of shale oil reservoirs, including rock mineral composition, shale oil crude oil density, viscosity, and micro-nano pore throat system of crude oil occurrence state; S2: molecular dynamics simulation: using a mixed horizontal orthogonal table to arrange the molecular dynamics simulation, selecting mineral composition, pressure, and surfactant concentration as three factors; wherein the mineral composition selects dolomite, calcite, and quartz as representatives, the pressure is 10 MPa, 20 MPa, and 30 MPa, respectively, and the surfactant is sodium dodecyl sulfonate (SDBS); according to this, 25 schemes are obtained for molecular dynamics simulation; S3: establishing a micro model: using Materials Studio to establish a micro model of rock wall-oil phase-displacement phase; S4: data comparison: comparing the imbibition efficiency of different simulation results, obtaining the influence of different reservoir types, pressure, and surfactant concentration on the imbibition displacement efficiency during the soak period, and analyzing the best case of soak pressure, corresponding soak time, and surfactant mass fraction data to maximize the imbibition displacement efficiency.

2. The method of analog computation of soak imbibition displacement efficiency of a frac soak according to claim 1, wherein: The concentration range of the surfactant sodium dodecyl sulfonate (SDBS) is 0.1%-0.5%.

3. The method of analog computation of soak imbibition displacement efficiency of a frac soak, as claimed in claim 1, wherein: The micro model in S3 comprises the following: Geometric optimization to ensure that the geometric structure of the model reaches the theoretical minimum energy state; Relaxation to make the system reach dynamic stability by adjusting the simulation conditions; Molecular dynamics simulation to make the system reach thermodynamic equilibrium by simulating the motion and interaction of molecules; The data obtained after the simulation balance is analyzed to take 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 as the representation of the imbibition efficiency, and the calculation formula is: In the formula, η is the recovery rate; N total is the total amount of oil molecules in the initial oil phase range before imbibition; and residual is the residual oil molecule amount in the initial oil phase range after imbibition.

4. The optimal soak time prediction system is applicable to the simulation calculation of the fracturing soak absorption displacement efficiency method in any one of claims 1-3, characterized in that: It comprises a parameter input module, an orthogonal design simulation module, a parameter output module, a database, and a prediction module; The parameter input module is used to input shale oil reservoir parameters, fluid parameters, and production parameters; The orthogonal design simulation module is used to select key factors affecting the imbibition displacement efficiency during the soak period 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 orthogonal design simulation module; The database is used to save and record the data of the parameter input module, the orthogonal design simulation module, and the parameter output module; The database further comprises a machine learning method, which takes the imbibition displacement efficiency of fracturing fluid on crude oil as the core target, saves and records the data between the corresponding influencing factors and the imbibition displacement efficiency; The prediction module is used to build a prediction model for determining the optimal soak time after inputting parameters in the input module, thereby maximizing the imbibition displacement efficiency of crude oil.

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