Construction method and application of injection condition control and productivity prediction model
By constructing an injection condition control and production capacity prediction model, the problems of excessive fracture propagation and crossflow in the development of low-permeability reservoirs were solved, achieving efficient development of low-permeability reservoirs and improving crude oil production, while optimizing injection strategies and reservoir management.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- CHINA PETROLEUM & CHEMICAL CORP
- Filing Date
- 2024-11-18
- Publication Date
- 2026-05-19
AI Technical Summary
Low-permeability reservoirs face challenges during development, including lower recovery rates, higher water cuts, increased difficulty in water injection, greater seepage resistance, and formation energy depletion. Traditional fracturing techniques may lead to the formation of single, deep, and long fractures, increasing the risk of crossflow and reducing crude oil production.
An injection condition control and production capacity prediction model was constructed. By acquiring core sample data, a functional relationship between reservoir characteristics and permeability changes was established, pore structure and fracture morphology were simulated, and injection condition control and production capacity prediction model were constructed in combination with production well data to optimize injection pressure and quantity and achieve simultaneous injection and production.
It enables efficient development of low-permeability reservoirs, improves crude oil production, reduces development risks, enhances the scientific nature and adaptability of reservoir management, optimizes injection strategies, and controls the formation and expansion of fracture networks.
Smart Images
Figure CN122065701A_ABST
Abstract
Description
Technical Field
[0001] This disclosure relates to the field of oil extraction technology, specifically to a method and application for constructing an injection condition control and production capacity prediction model. Background Technology
[0002] In the field of oil extraction, the efficient development of low-permeability reservoirs plays a crucial role in expanding crude oil production. However, these reservoirs face a series of problems during development, including low recovery rates, high water cuts, increased difficulty in water injection, significant seepage resistance and capillary resistance, and rapid depletion of formation energy. These problems severely restrict the efficiency and sustainability of low-permeability reservoir development.
[0003] To address the aforementioned issues, water injection well pressure drive technology is commonly used to replenish formation pressure. However, this technology typically employs a phased implementation strategy: first fracturing, then well shut-in, and finally oil production. While it replenishes formation pressure to some extent, if the injection volume or pressure is not precisely controlled during the high-pressure water injection phase, excessively long single fractures may form due to geostress, increasing the risk of inter-reservoir flow and thus reducing crude oil production. Summary of the Invention
[0004] The purpose of this disclosure is to provide a method for constructing an injection condition control and capacity prediction model and its application, so as to partially or completely solve the above problems.
[0005] To achieve the above objectives, a first aspect of this disclosure provides a method for constructing an injection condition control and production capacity prediction model. The method includes: acquiring reservoir characteristic datasets, permeability datasets, and fracture initiation pressure datasets from multiple core samples; the reservoir characteristic datasets include reservoir characteristic datasets under initial conditions and reservoir characteristic datasets under multiple preset injection conditions; the permeability datasets include permeability datasets under initial conditions and permeability datasets under multiple preset injection conditions; constructing a first model based on the reservoir characteristic datasets, permeability datasets, and fracture initiation pressure datasets; performing numerical simulations on the first model to determine the permeable zone area under multiple preset injection conditions, and acquiring production volume datasets and oil production datasets for each permeable zone area; and constructing an injection condition control and production capacity prediction model based on the first model and the production volume datasets and oil production datasets of the production wells.
[0006] In some embodiments, a first model is constructed based on a reservoir feature dataset, a permeability dataset, and a fracturing pressure dataset, comprising: determining a first functional relationship based on the reservoir feature dataset and the permeability dataset, wherein the first functional relationship is a functional relationship between the permeability change value and the injection conditions and reservoir features, and the permeability change value is the difference between the permeability of the core sample under initial conditions and the permeability under each preset injection condition; determining a second functional relationship based on the reservoir feature dataset under initial conditions, the permeability dataset under initial conditions, and the fracturing pressure dataset, wherein the second functional relationship is a functional relationship between the fracturing pressure and the reservoir features; and constructing the first model based on the first functional relationship and the second functional relationship.
[0007] In some embodiments, the injection conditions include injection pressure and injection volume, and the reservoir characteristics include porosity and the number of fractures, wherein the first functional relationship is: ΔK = aZ 3 +bPhi 2 +cNum, where ΔK is the permeability change, Z is the injection pressure and injection volume, Phi is the porosity, Num is the number of fractures, and a, b, and c are the influence coefficients of the relationship; the second functional relationship is: Pf=dNum0 3 +eK0 2 +fPhi0, where Pf is the initiation pressure, K0 is the permeability under initial conditions, Num0 is the number of fractures under initial conditions, Phi0 is the porosity under initial conditions, and d, e, and f are the influence coefficients of the relationship.
[0008] In some embodiments, based on the first model and the production data sets of the production wells, an injection condition control and production capacity prediction model is constructed, including: determining a third functional relationship and a fourth functional relationship based on the production data sets of the production wells, wherein the third functional relationship is a functional relationship between the production volume and the permeable area, and the fourth functional relationship is a functional relationship between the production volume and the permeable area; and constructing the injection condition control and production capacity prediction model based on the first model, the third functional relationship, and the fourth functional relationship.
[0009] In some embodiments, the third functional relationship is: Q l =g(Sk) f / ΔK) 3 +h(Sk f / ΔK) 2 +i(Sk f / ΔK)+j, where Q l The production rate of the production well; Sk f The area of the infiltration region is represented by g, h, i, and j, which are the influence coefficients of the relational expression. The fourth functional relationship is: Q o =l(Sk) f / ΔK)3 +m(Sk f / ΔK) 2 +n(Sk f / ΔK)+p, where Q o The oil production of a production well; Sk f denoted as the area of the infiltration region; l, m, n, and p are the influence coefficients of the relational formula.
[0010] In some embodiments, after constructing the injection condition control and production capacity prediction model, the construction method further includes: acquiring multiple preset injection condition actual microseismic datasets, actual fluid production datasets of production wells, and actual oil production datasets, wherein the actual microseismic datasets include data on changes in pore structure and fracture morphology under different injection conditions; and optimizing the injection condition control and production capacity prediction model based on the actual microseismic datasets, the actual fluid production datasets of production wells, and the actual oil production datasets.
[0011] A second aspect of this disclosure provides an injection condition control and capacity prediction model, which is constructed using the construction method provided in the first aspect or any embodiment of the first aspect of this disclosure.
[0012] A third aspect of this disclosure provides a method for simultaneous injection and production based on an injection condition control and production capacity prediction model. The method employs the injection condition control and production capacity prediction model provided in the second aspect of this disclosure. The method includes: under current injection conditions, collecting microseismic data of the injection well, fluid production change data of the production well, and oil production change data within a preset number of days; inputting the microseismic data, fluid production change data, and oil production change data into a pre-constructed injection condition control and production capacity prediction model for predictive analysis; adjusting the injection pressure and / or injection volume according to the model prediction results, and outputting the adjusted fluid production and oil production of the production well; and simultaneously conducting production operations after adjusting the injection pressure and / or injection volume, while monitoring the fluid production and oil production of the production well.
[0013] A fourth aspect of this disclosure provides an injection-production synchronization system based on an injection condition control and production capacity prediction model. The system includes: a data acquisition unit for collecting microseismic data of the injection well and fluid production and oil production change data of the production well within a preset number of days under current injection conditions; a model prediction unit configured with the injection condition control and production capacity prediction model provided in the second aspect of this disclosure, for performing predictive analysis based on the microseismic data and fluid production change data; a control unit for adjusting the injection pressure and / or injection volume according to the model prediction results, and outputting the adjusted fluid production and oil production of the production well; and for synchronously performing production operations after adjusting the injection pressure and / or injection volume, and monitoring the fluid production and oil production of the production well; and a display unit for displaying the model prediction results and the fluid production and oil production of the production well, and for receiving adjustment commands input by the user.
[0014] A fifth aspect of this disclosure provides a machine-readable storage medium storing instructions for causing a machine to execute the injection-production synchronization method based on an injection condition control and capacity prediction model provided in the third aspect of this disclosure.
[0015] By constructing an injection condition control and production capacity prediction model, the injection pressure and / or injection volume of low-permeability reservoirs can be intelligently controlled, thereby forming effective large-scale fractures in the reservoir, achieving simultaneous injection and production, and improving crude oil productivity.
[0016] Other features and advantages of the embodiments disclosed herein will be described in detail in the following detailed description section. Attached Figure Description
[0017] The accompanying drawings are provided to further illustrate the embodiments of this disclosure and form part of the specification. They are used together with the following detailed description to explain the embodiments of this disclosure, but do not constitute a limitation thereof. In the drawings:
[0018] Figure 1 This is a flowchart illustrating a method for constructing an injection condition control and capacity prediction model according to an embodiment of this disclosure;
[0019] Figure 2 This is a flowchart illustrating a method for simultaneous injection and production based on an injection condition control and capacity prediction model provided in an embodiment of this disclosure.
[0020] Figure 3 This is an overall flowchart of the construction and application of an injection condition control and capacity prediction model provided in the embodiments of this disclosure;
[0021] Figure 4This is a schematic diagram of the structure of an injection-production synchronization system based on an injection condition control and production capacity prediction model provided in an embodiment of this disclosure. Detailed Implementation
[0022] The specific embodiments of this disclosure will be described in detail below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are for illustration and explanation only and are not intended to limit the scope of this disclosure.
[0023] In the development of low-permeability reservoirs, creating more microscopic and interconnected fractures can increase the extent of the fracture network, thereby improving crude oil production. However, traditional fracturing, well shut-in, and well opening techniques may lead to excessive fracture propagation during the high-pressure water injection stage, resulting in single, deep fractures. This increases the risk of inter-reservoir flow and reduces crude oil production.
[0024] Currently, pressure-driven water injection technology mainly focuses on improving the development effect of well groups through high-pressure water injection, but it does not consider simultaneous injection and production. Simultaneous injection and production, which involves injection and production during the pressure-driven process, can theoretically optimize the development effect of oil fields. However, no relevant research has been found on how to effectively implement simultaneous injection and production during pressure-driven processes, or how to control the formation of fractures caused by the injected fluid.
[0025] Based on the fundamental principles of hydraulic fracturing, under the premise of primary fracture initiation, slickwater or a certain concentration of oil displacement agent can be injected at high flow rates as fracturing fluid to achieve reasonable fracture formation and propagation. This method induces the generation and propagation of micro-fractures through friction loss, forming a fan-shaped sweep surface, thereby fully displacing the remaining oil in the reservoir pores into the fractures. This not only helps to expand the influence range of waterflooding and improve oil displacement efficiency, but also allows for the simultaneous production of fluid in the production well. However, fractures formed by excessively high injection pressure or excessive injection volume will lead to crossflow between injection and production wells. Therefore, effective control of fractures caused by the injected fluid is essential in this process.
[0026] To address the issues of excessively large fractures and crossflows that may result from excessively high injection pressure or injection volume, this disclosure proposes a model for controlling injection conditions and predicting production capacity. This model intelligently controls injection conditions based on production well output and microseismic (or fiber optic) data, and simultaneously predicts the production capacity and stabilization time of wells under adjusted injection conditions. Specifically, by intelligently controlling injection pressure and / or injection volume and accurately monitoring fracture morphology, effective control of the fracture network is achieved, providing a new technical approach for the efficient development of low-permeability reservoirs.
[0027] Figure 1 This is a flowchart illustrating a method for constructing an injection condition control and capacity prediction model according to an embodiment of this disclosure.
[0028] like Figure 1 As shown in the embodiments of this disclosure, a method for constructing an injection condition control and capacity prediction model is provided, the method including steps S101 to S104.
[0029] In step S101, reservoir characteristic datasets, permeability datasets, and fracture initiation pressure datasets of multiple core samples are obtained.
[0030] The reservoir feature dataset includes a reservoir feature dataset under initial conditions and a reservoir feature dataset under multiple preset injection conditions. The permeability dataset includes a permeability dataset under initial conditions and a permeability dataset under multiple preset injection conditions.
[0031] In some embodiments, injection conditions include injection pressure and injection volume, and reservoir characteristics include porosity and number of fractures.
[0032] For example, reservoir characteristic datasets, permeability datasets, and fracturing pressure datasets for multiple core samples can be obtained by conducting high-pressure water injection experiments and water injection fracturing experiments on multiple core samples.
[0033] Taking a high-pressure water injection experiment with a core sample as an example, multiple preset injection conditions are set as injection pressures. That is, different injection pressures are set while maintaining a certain injection volume to conduct high-pressure water injection experiments on the core sample. The experimental steps are as follows: First, the initial parameters of the core sample are tested to obtain reservoir characteristic data and permeability data under the initial conditions. For example, the porosity, number of fractures, and permeability of the core sample under the initial conditions can be obtained through core porosity testing, CT scanning, and core permeability testing, respectively. Second, water is injected into the core sample at injection pressures of 20 MPa, 40 MPa, 60 MPa, and 80 MPa to obtain reservoir characteristic data and permeability data under multiple preset injection pressures. Again, the porosity, number of fractures, and permeability of the core sample under multiple preset injection pressures can be obtained through core porosity testing, CT scanning, and core permeability testing, respectively.
[0034] By systematically acquiring and analyzing reservoir characteristics, permeability, and fracturing pressure data of core samples under different injection conditions, accurate assessment of reservoir response was achieved, providing crucial basic data for model establishment.
[0035] In step S102, a first model is constructed based on the reservoir feature dataset, permeability dataset, and fracturing pressure dataset.
[0036] The first model is used to simulate the changes in pore structure and crack morphology under different injection conditions.
[0037] In some embodiments, constructing a first model based on a reservoir feature dataset, a permeability dataset, and a fracturing pressure dataset may specifically include the following steps:
[0038] 1) Determine the first functional relationship based on the reservoir feature dataset and the permeability dataset.
[0039] The first functional relationship is the functional relationship between the permeability change value and the injection conditions and reservoir characteristics. The permeability change value is the difference between the permeability of the core sample under the initial conditions and the permeability under each preset injection condition.
[0040] Specifically, the first functional relationship is:
[0041] ΔK=aZ 3 +bPhi 2 +cNum, where ΔK is the permeability change, Z is the injection pressure and injection volume, Phi is the porosity, Num is the number of fractures, and a, b, and c are the influence coefficients of the relationship.
[0042] 2) Determine the second functional relationship based on the reservoir characteristic dataset, permeability dataset, and fracturing pressure dataset under the initial conditions.
[0043] The second functional relationship is the functional relationship between fracturing pressure and reservoir characteristics.
[0044] Specifically, the second functional relationship is as follows:
[0045] Pf = dNum0 3 +eK0 2 +fPhi0, where Pf is the initiation pressure, K0 is the permeability under initial conditions, Num0 is the number of fractures under initial conditions, Phi0 is the porosity under initial conditions, and d, e, and f are the influence coefficients of the relationship.
[0046] 3) Construct the first model based on the first functional relationship and the second functional relationship.
[0047] By accurately determining and applying the first and second functional relationships, the constructed first model can realistically reflect the dynamic changes in reservoir pore structure and fracture morphology under different injection conditions. This model not only provides a solid foundation for the establishment of injection condition control and production capacity prediction models, but also improves the scientific nature and prediction accuracy of oil extraction strategies to a certain extent.
[0048] In step S103, numerical simulation is performed on the first model to determine the area of the permeable zone under multiple preset injection conditions, and the production volume dataset and oil production dataset of the production well under each permeable zone area are obtained.
[0049] In step S104, an injection condition control and production capacity prediction model is constructed based on the first model and the production fluid and oil production datasets of the production wells.
[0050] In some embodiments, constructing an injection condition control and production capacity prediction model based on the first model and the production volume dataset and oil production dataset of the production well may include the following specific steps:
[0051] 1) Based on the production data sets of production wells, determine the third and fourth functional relationships.
[0052] The third functional relationship is the functional relationship between the liquid production rate and the area of the permeable region, and the fourth functional relationship is the functional relationship between the oil production rate and the area of the permeable region.
[0053] Specifically, the third functional relationship is as follows:
[0054] Q l =g(Sk) f / ΔK) 3 +h(Sk f / ΔK) 2 +i(Sk f / ΔK)+j, where Q l The production rate of the production well; Sk f denoted as the area of the infiltration region; g, h, i, and j are the influence coefficients of the relational formula.
[0055] The fourth functional relationship is:
[0056] Q o =l(Sk) f / ΔK) 3 +m(Sk f / ΔK) 2 +n(Sk f / ΔK)+p, where Q o The oil production of a production well; Sk f denoted as the area of the infiltration region; l, m, n, and p are the influence coefficients of the relational formula.
[0057] 2) Based on the first model, the third functional relationship and the fourth functional relationship, construct an injection condition control and capacity prediction model.
[0058] By accurately predicting the permeable zone area under different injection conditions through numerical simulation and combining it with actual fluid and oil production data from production wells, a model for controlling injection conditions and predicting production capacity was ultimately constructed. This model not only improves the accuracy of production capacity prediction for production wells but also optimizes injection strategies, enabling efficient development of low-permeability reservoirs while reducing development risks and enhancing the scientific nature and adaptability of reservoir management.
[0059] In some embodiments, after constructing the injection condition control and capacity prediction model, the following steps may also be included:
[0060] 1) Obtain actual microseismic datasets, actual fluid production datasets, and actual oil production datasets under multiple preset injection conditions. The actual microseismic datasets include data on changes in pore structure and fracture morphology under different injection conditions.
[0061] 2) Based on actual microseismic datasets, actual fluid production datasets and actual oil production datasets from production wells, optimize the injection condition control and production capacity prediction models.
[0062] By optimizing the model using actual microseismic data, fluid production data, and oil production data, the accuracy of production capacity prediction was improved, making the prediction results closer to the actual production situation.
[0063] The embodiments disclosed herein utilize an injection condition control and production capacity prediction model to intelligently control the injection pressure and / or injection volume in low-permeability reservoirs, thereby forming effective large-scale fractures in the reservoir, achieving simultaneous injection and production, and improving crude oil productivity.
[0064] In this embodiment of the disclosure, an injection condition control and capacity prediction model is also provided. This injection condition control and capacity prediction model can be constructed using the construction method of the injection condition control and capacity prediction model provided in the above embodiments of the disclosure.
[0065] Figure 2 This is a flowchart illustrating a method for simultaneous injection and production based on an injection condition control and capacity prediction model, according to an embodiment of this disclosure.
[0066] like Figure 2 As shown in the embodiments of this disclosure, an injection-production synchronization method based on an injection condition control and capacity prediction model is provided. The injection condition control and capacity prediction model provided in the embodiments of this disclosure is used. The injection-production synchronization method includes steps S201 to S204.
[0067] In step S201, under the current injection conditions, microseismic data of the injection well, fluid production change data and oil production change data of the production well are collected within a preset number of days.
[0068] Under the current injection pressure, there are actual microseismic data and production volume and oil production of the production well. Since the microseismic data and production change data will change as the development time increases, the microseismic data and production change data within a preset number of days can reflect the formation of reservoir microfractures under the current injection conditions, thereby enabling accurate identification of the microfracture formation area of the reservoir.
[0069] In some embodiments, microseismic monitoring or fiber optic monitoring sensors can be used to monitor changes in pore structure and fracture morphology during the injection process.
[0070] Taking a microseismic monitoring sensor as an example, the preset number of days can be 1-20 days. For example, a microseismic monitoring sensor can be placed in a well adjacent to the injection well to collect microseismic data of the injection well within 1-20 days under the current injection conditions. At the same time, the production volume change data and oil production change data of the production well can be obtained from the production well.
[0071] In step S202, the microseismic data, fluid production change data, and oil production change data are input into the pre-constructed injection condition control and production capacity prediction model for prediction and analysis.
[0072] In this embodiment of the disclosure, the injection condition control and production capacity prediction model can predict the water channeling time of the well group, the adjusted injection pressure and / or injection volume, the adjusted fluid production and oil production of the next stage production well and the stable production time based on microseismic data, fluid production change data and oil production change data, which is convenient for real-time monitoring and adjustment.
[0073] In step S203, the injection pressure and / or injection volume are adjusted according to the model prediction results, and the adjusted production volume and oil production of the production well are output.
[0074] The adjustment mechanism of the injection condition control and production prediction model in this embodiment is as follows: the microseismic data corresponds to the reservoir microfracture formation area under the current injection pressure. This area will correspond to high injection pressure. According to the first functional relationship (i.e., the functional relationship between the permeability change value and the injection conditions and reservoir characteristics), the permeability of this high injection pressure area can be determined. According to the third and fourth functional relationships (i.e., the functional relationship between the production rate and oil production of the production well and the area of the permeable area), the impact of changes in injection parameters on the production rate and oil production can be determined.
[0075] In a specific example, if the current injection pressure is 50 MPa, the initial production rate of the production well is 7 t / d, and the production rate after 20 days is 10 t / d, then the model will combine the current microseismic data to determine the high-pressure injection zone, predict the high-permeability area of the reservoir, and output the water channeling time of the well group (such as water channeling may be sent after 2 months to prompt the staff to adjust the injection parameters in time), the adjusted injection pressure of 40 MPa (that is, the range of permeability change can be controlled by adjusting the injection pressure), and the theoretically achievable production rate of the production well after adjustment is 12 t / d, with a stable production time of up to 3 years.
[0076] In another specific example, if the current injection pressure is 20 MPa, the initial production rate of the production well is 3 t / d, and the production rate after 20 days is 3 d / t, then the model will combine the current microseismic data to determine the high-pressure injection zone and predict the high-permeability area of the reservoir. At this time, the production rate of the production well is low, so the model will combine the corresponding microseismic data under the current injection pressure to give an injection pressure adjustment strategy and output the water channeling time of the well group. The adjusted injection pressure is 40 MPa. After the adjustment, the theoretical production rate that the production well can achieve is 12 t / d, and the stable production time can reach 3 years.
[0077] In step S204, extraction work is carried out simultaneously after adjusting the injection pressure and / or injection volume, and the production volume and oil production of the production well are monitored.
[0078] By implementing a simultaneous injection and production method based on injection condition control and production prediction models, microseismic monitoring data and production data from production wells can be effectively collected and analyzed, enabling precise identification and real-time monitoring of reservoir microfracture formation areas. This method utilizes model prediction and analysis to dynamically adjust injection pressure and injection volume, optimizing the fluid and oil production of production wells and significantly improving the exploitation efficiency and stability of low-permeability reservoirs. Furthermore, the model can predict water channeling time in well groups, providing a scientific basis for timely adjustment of injection parameters, thereby extending the stable production time of oil wells and achieving efficient reservoir management and sustainable development.
[0079] Figure 3 This is an overall flowchart of the construction and application of an injection condition control and capacity prediction model provided in the embodiments of this disclosure.
[0080] See Figure 3 The overall process for constructing and applying the injection condition control and production capacity prediction model is as follows: First, through high-pressure water injection experiments on core samples, the permeability variation law and reservoir characteristic variation law of the core samples are obtained to establish the functional relationship between permeability variation and injection conditions and reservoir characteristics. Second, through core water injection fracturing experiments, the fracturing pressure of the core samples is determined to establish the functional relationship between fracturing pressure and reservoir characteristics. Third, based on the functional relationships between permeability variation and injection conditions and reservoir characteristics, and between fracturing pressure and reservoir characteristics, a production capacity prediction model considering changes in pore structure and fracture morphology is constructed. Fourth, based on this production capacity prediction model considering changes in pore structure and fracture morphology, the production capacity variation law of the production well can be predicted. Based on this production capacity prediction model considering changes in pore structure and fracture morphology and the production capacity variation law of the production well, an injection condition control and production capacity prediction model considering changes in pore structure and fracture morphology is constructed. Fifth, the model constructed in step four is optimized based on actual microseismic data and actual production capacity variation data of the production well. The sixth step is to adjust the injection pressure and / or injection volume based on the microseismic data and the production capacity change data of the production well.
[0081] For example, in one specific implementation, the overall construction and application of the injection condition control and capacity prediction model may include the following steps:
[0082] Step 1: First, test the initial parameters of the core sample. Use a CT scanner to test the porosity φ and fracture distribution in the initial state, and test the permeability K of the core sample in the initial state.
[0083] Step 2: Water was injected into the core samples at injection pressures of 20MPa, 40MPa, 60MPa, and 80MPa respectively. The changes in porosity φ and fracture distribution of the core samples under different water injection pressures were measured by core porosity testing and CT scanning.
[0084] Step 3: Test the change in core permeability K value after fracturing cores with different injection pressures.
[0085] Step 4: Repeat the above steps multiple times, integrate the obtained experimental data, and fit the relationship between permeability change (ΔK), high-pressure injection parameters (Z), and reservoir characteristics (C). The high-pressure injection parameter Z consists of the injection volume Q and injection pressure P, while the reservoir characteristics C consist of porosity φ and the number of fractures. Finally, the relationship can be derived... Δ K = F(Z) + F(C).
[0086] Step 5: Perform water injection fracturing experiments on the core samples, starting from 10 MPa and gradually increasing the injection pressure. Use a CT scanner to detect the fracture parameters and record the initiation pressure.
[0087] Step 6: Replace with different core samples (core samples obtained from different coring operations), repeat step 5, measure the changes in fracture initiation pressure of core samples with different reservoir characteristics, record the data, and establish the relationship between fracture initiation pressure and reservoir characteristics based on the experimental data.
[0088] Step 7: Based on Step 4 and Step 6, construct a production capacity prediction model that considers the changes in fracture morphology and pore structure based on the characteristics of micro-pressure-driven simultaneous injection and production. On this basis, conduct simultaneous numerical simulation of pressure-driven injection to characterize the relationship between injection pressure, pore pressure, reservoir stress and permeability changes. Use numerical simulation software to predict permeability changes and production volume during high-pressure injection.
[0089] Step 8: Under field conditions, place microseismic monitoring or fiber optic monitoring sensors in wells adjacent to the injection well to monitor the distribution of fracture morphology generated during high-pressure injection. Simultaneously, obtain the variation pattern of fluid production from the production wells in the well group.
[0090] Step 9: Based on the crack morphology and liquid production change data obtained under field conditions, optimize the production capacity prediction model that considers crack morphology and pore structure changes constructed in Step 8 based on data learning, and improve the model accuracy.
[0091] Step 10: In actual production, place microseismic (or fiber optic) sensors near the injection well of the well group. Based on the optimized production capacity prediction model that considers the changes in fracture morphology and pore structure, predict the water channeling time of the well group through microseismic data and production volume change data. At the same time, adjust the injection pressure in real time to effectively extend the water channeling time of the well group.
[0092] For example, in another specific implementation, the overall construction and application of the injection condition control and capacity prediction model may include the following steps:
[0093] Step 21: First, test the initial parameters of the core sample. Use a CT scanner to test the porosity φ and fracture distribution in the initial state, and test the permeability K of the core sample in the initial state.
[0094] Step 22: Fill the water with a volume of 0.05m³. 3 0.01m 3 0.03m 3 0.05m 3 0.1m 3 0.5m 3 During the process, water was injected into the core samples, and the changes in porosity φ and fracture distribution of the core samples under different water injection volumes were tested using core porosity testing and CT scanner.
[0095] Step 23: Test the change in permeability K value of core samples after fracturing with different injection volumes.
[0096] Step 24: Repeat the above steps multiple times, integrate the experimental data, and fit the relationship between permeability change (ΔK) and high-pressure injection parameters (Z) and reservoir characteristics (C). The high-pressure injection parameter Z is composed of the injection volume Q and the injection pressure P, and the reservoir characteristics C is composed of porosity φ and the number of fractures. Finally, ΔK = F(Z) + F(C) can be obtained.
[0097] Step 25: Conduct water injection fracturing tests on the core samples, starting from 0.05m. 3 Initially, the water injection volume was continuously increased, and a CT scanner was used to detect its crack parameters and record its crack initiation pressure.
[0098] Step 26: Replace with different core samples (core samples obtained from different core samples), repeat step 15, measure the changes in fracture initiation pressure of core samples with different reservoir characteristics, record the data; and establish the relationship between fracture initiation pressure and reservoir characteristics based on the experimental data.
[0099] Step 27: Based on steps 24 and 26, construct a production capacity prediction model that considers the changes in fracture morphology and pore structure based on the characteristics of micro-pressure-driven simultaneous injection and production; on this basis, conduct simultaneous numerical simulation of pressure-driven injection to characterize the relationship between water injection volume, injection pressure, pore pressure, reservoir stress and permeability changes, and use numerical simulation software to predict permeability changes and production volume during high-pressure injection.
[0100] Step 28: Under field conditions, place microseismic monitoring or fiber optic monitoring sensors in wells adjacent to the injection well to monitor the distribution of fracture morphology generated during high-pressure injection. Simultaneously, obtain the variation pattern of fluid production from the production wells in the well group.
[0101] Step 29: Based on the crack morphology and liquid production change data obtained under field conditions, optimize the production capacity prediction model that considers crack morphology and pore structure changes constructed in Step 28 based on data learning to improve the model accuracy.
[0102] Step 30: In actual production, microseismic (or fiber optic) sensors are placed near the injection wells of the well group. Based on the optimized production capacity prediction model that considers the changes in fracture morphology and pore structure, the water channeling time of the well group is predicted by using microseismic data and production volume change data. At the same time, the injection volume is adjusted in real time to effectively extend the water channeling time of the well group.
[0103] Figure 4 This is a schematic diagram of the structure of an injection-production synchronization system based on an injection condition control and production capacity prediction model provided in an embodiment of this disclosure.
[0104] like Figure 4 As shown in the present invention, an injection-production synchronization system based on an injection condition control and production capacity prediction model is provided. The system 100 includes a data acquisition unit 110, a model prediction unit 120, a control unit 130, and a display unit 140.
[0105] In some embodiments, the data acquisition unit 110 is used to collect microseismic data of the injection well and fluid production change data and oil production change data of the production well within a preset number of days under the current injection conditions.
[0106] In some embodiments, the model prediction unit 120 is configured with an injection condition control and production capacity prediction model provided in this disclosure, for performing predictive analysis based on microseismic data and liquid production change data.
[0107] In some embodiments, the control unit 130 is used to adjust the injection pressure and / or injection volume according to the model prediction results, and output the adjusted fluid production and oil production of the production well; and to perform production operations synchronously after adjusting the injection pressure and / or injection volume, and monitor the fluid production and oil production of the production well.
[0108] In some embodiments, the display unit 140 is used to display model prediction results and the fluid production and oil production of the production well, as well as to receive adjustment instructions input by the user.
[0109] For specific details and benefits of the injection-production synchronization system based on injection condition control and capacity prediction model provided in the embodiments of this disclosure, please refer to the above description of the injection-production synchronization method based on injection condition control and capacity prediction model, which will not be repeated here.
[0110] In this embodiment of the disclosure, a machine-readable storage medium is also provided, on which instructions are stored, which are used to cause a machine to execute the injection-production synchronization method based on the injection condition control and capacity prediction model provided in the above embodiments of the disclosure.
[0111] Those skilled in the art will understand that embodiments of this disclosure can be provided as methods, systems, or computer program products. Therefore, this disclosure can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this disclosure can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0112] This disclosure is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this disclosure. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.
[0113] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.
[0114] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.
[0115] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.
[0116] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.
[0117] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.
[0118] It should be noted that although the terms "first," "second," etc., are used herein to describe different modules, steps, and data in the embodiments of this disclosure, these terms are only for distinguishing between different modules, steps, and data, and do not indicate a specific order or degree of importance. In fact, the terms "first," "second," etc., can be used interchangeably.
[0119] Although the operations are described in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order or serial order shown, or requiring all of the operations shown to obtain the desired result. In certain environments, multitasking and parallel processing may be advantageous.
[0120] The acquisition, transmission, storage, use, and processing of data in this embodiment comply with the relevant provisions of national laws and regulations.
[0121] It should be noted that in the embodiments disclosed herein, certain software, components, models, and other existing solutions in the industry may be mentioned. These should be considered as exemplary and are intended only to illustrate the feasibility of implementing the technical solutions disclosed herein. However, they do not mean that the applicant has used or necessarily used such solutions.
[0122] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.
[0123] The above are merely embodiments of this disclosure and are not intended to limit the scope of this disclosure. Various modifications and variations can be made to this disclosure by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this disclosure should be included within the scope of the claims of this disclosure.
Claims
1. A method for constructing an injection condition control and capacity prediction model, characterized in that, The construction method includes: A reservoir feature dataset, a permeability dataset, and a fracture initiation pressure dataset are obtained from multiple core samples. The reservoir feature dataset includes a reservoir feature dataset under initial conditions and a reservoir feature dataset under multiple preset injection conditions. The permeability dataset includes a permeability dataset under initial conditions and a permeability dataset under multiple preset injection conditions. Based on the reservoir feature dataset, the permeability dataset, and the fracture initiation pressure dataset, a first model is constructed. The first model is used to simulate the changes in pore structure and fracture morphology under different injection conditions. Numerical simulations were performed on the first model to determine the area of the permeable zone under multiple preset injection conditions, and the production volume data and oil production data of the production well under each permeable zone area were obtained. Based on the first model and the production data sets of the production wells, an injection condition control and production capacity prediction model is constructed.
2. The construction method according to claim 1, characterized in that, The construction of the first model based on the reservoir feature dataset, the permeability dataset, and the fracturing pressure dataset includes: Based on the reservoir feature dataset and the permeability dataset, a first functional relationship is determined, wherein the first functional relationship is a functional relationship between the permeability change value and the injection conditions and reservoir features, and the permeability change value is the difference between the permeability of the core sample under the initial conditions and the permeability under each preset injection condition; Based on the reservoir characteristic dataset, the permeability dataset, and the fracturing pressure dataset under the initial conditions, a second functional relationship is determined, wherein the second functional relationship is a functional relationship between fracturing pressure and reservoir characteristics; The first model is constructed based on the first functional relationship and the second functional relationship.
3. The construction method according to claim 2, characterized in that, The injection conditions include injection pressure and injection volume, and the reservoir characteristics include porosity and the number of fractures, wherein... The first functional relationship is: ΔK = aZ 3 +bPhi 2 +cNum, where ΔK is the permeability change, Z is the injection pressure and injection volume, Phi is the porosity, Num is the number of fractures, and a, b, and c are the influence coefficients of the relationship. The second functional relationship is: Pf = dNum0 3 +eK0 2 +fPhi0, where Pf is the initiation pressure, K0 is the permeability under initial conditions, Num0 is the number of fractures under initial conditions, Phi0 is the porosity under initial conditions, and d, e, and f are the influence coefficients of the relationship.
4. The construction method according to claim 1, characterized in that, The step of constructing an injection condition control and production capacity prediction model based on the first model and the production volume dataset and oil production dataset of the production well includes: Based on the production data sets of the production wells, a third functional relationship and a fourth functional relationship are determined, wherein the third functional relationship is the functional relationship between production and the area of the permeable region, and the fourth functional relationship is the functional relationship between production and the area of the permeable region; Based on the first model, the third functional relationship, and the fourth functional relationship, the injection condition control and capacity prediction model is constructed.
5. The construction method according to claim 4, characterized in that, The third functional relationship is: Q l =g(Sk) f / ΔK) 3 +h(Sk f / ΔK) 2 +i(Sk f / ΔK)+j, where Q l The production rate of the production well; Sk f denoted as the area of the infiltration region; g, h, i, and j are the influence coefficients of the relational formula. The fourth functional relationship is: Q o =l(Sk) f / ΔK) 3 +m(Sk f / ΔK) 2 +n(Sk f / ΔK)+p, where Q o The oil production of a production well; Sk f denoted as the area of the infiltration region; l, m, n, and p are the influence coefficients of the relational formula.
6. The construction method according to any one of claims 1-5, characterized in that, After constructing the injection condition control and capacity prediction model, the construction method further includes: Acquire actual microseismic datasets, actual fluid production datasets, and actual oil production datasets under multiple preset injection conditions. The actual microseismic datasets include data on changes in pore structure and fracture morphology under different injection conditions. Based on the actual microseismic dataset, the actual fluid production dataset and the actual oil production dataset of the production well, the injection condition control and production capacity prediction model is optimized.
7. An injection condition control and capacity prediction model, characterized in that, The injection condition control and capacity prediction model is constructed using the construction method described in any one of claims 1-6.
8. A method for simultaneous injection and production based on injection condition control and production capacity prediction models, characterized in that, The method employs the injection condition control and capacity prediction model as described in claim 7, and the method includes: Under the current injection conditions, collect microseismic data of the injection wells, fluid production change data and oil production change data of the production wells within a preset number of days; The microseismic data, the liquid production change data, and the oil production change data are input into a pre-constructed injection condition control and production capacity prediction model for prediction and analysis. Adjust the injection pressure and / or injection volume based on the model prediction results, and output the adjusted fluid production and oil production of the production well; Production is carried out simultaneously after adjusting the injection pressure and / or injection volume, and the production volume of the production well is monitored.
9. A simultaneous injection and production system based on injection condition control and production capacity prediction models, characterized in that, The system includes: The data acquisition unit is used to collect microseismic data of the injection well and fluid production change data and oil production change data of the production well within a preset number of days under the current injection conditions. The model prediction unit is equipped with the injection condition control and production capacity prediction model as described in claim 7, which is used to perform prediction analysis based on the microseismic data and the liquid production change data. The control unit is used to adjust the injection pressure and / or injection volume according to the model prediction results, and output the production volume and oil production of the production well after adjustment; and to simultaneously carry out the production operation after adjusting the injection pressure and / or injection volume, and monitor the production volume and oil production of the production well. The display unit is used to display the model prediction results and the fluid production and oil production of the production well, as well as to receive adjustment instructions input by the user.
10. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions that cause the machine to perform the method as described in claim 8.