A method and system for predicting the optimal well location of a fire flooding to injection well based on injection-production linkage

Abstract

The application discloses a kind of based on injection-production linkage's fire flooding conversion injection well optimal well site prediction method and system, method includes following process: using fire flooding numerical model to determine the best time of exhaust well conversion injection gas well and the main control engineering parameter value in the fire flooding process under the optimal production effect;Under the condition of the main control engineering parameter value under the optimal production effect and the best time of exhaust well conversion injection gas well, the corresponding recovery factor of conversion injection well at different positions is calculated by the fire flooding numerical model;The optimal well site evaluation model is used to predict the optimal well position of conversion injection well.The application solves the problem of predicting the well site of conversion injection well when improving the producing degree of oil reservoir, which can effectively prolong the production time, reduce the engineering risk, and fully improve the fire flooding efficiency, providing a reference for the actual mine production to achieve the optimization of production effect.

Description

Technical Field

[0001] This invention belongs to the field of enhanced oil recovery technology for heavy oil reservoirs, and relates to gravity fire-drive exploitation methods for heavy oil reservoirs. Specifically, it relates to a method and system for predicting the optimal well location for fire-drive to injection wells based on injection-production linkage. Background Technology

[0002] In heavy oil reservoir development, reservoir combustion technology is one of the most effective, and THAI (Thawed Intake) technology is a promising one. Compared with traditional combustion, this technology has higher oil displacement efficiency and energy utilization, and is applicable to a wider range of reservoirs. Therefore, THAI is currently one of the important methods for heavy oil development. However, THAI is also one of the most complex technologies among all heavy oil thermal recovery technologies. Its combustion mechanism is complex, field testing is difficult, and it is hard to effectively control the advance speed and direction of the fire line. In addition, oxygen can easily break through horizontal production wells, causing the production well temperature to become too high and burn the wellbore, even creating huge engineering hazards. At the same time, when the oil layer is too thick or greatly affected by reservoir heterogeneity, the advance of the fire line in the direction of good anisotropic properties and low pressure, or in small layers, leads to problems such as low vertical utilization, gas over-coverage, uneven combustion, and difficulty in maintaining stable combustion. Summary of the Invention

[0003] To address the problems existing in the prior art, the present invention aims to provide a method and system for predicting the optimal well location of fire-driven injection wells based on injection-production linkage. The present invention can solve problems such as over-coverage and gas channeling in heavy oil THAI fire-driven operations, maximize the fire-driven recovery rate, extend the mining time, and guide mine production.

[0004] The technical solution adopted in this invention is as follows:

[0005] A method for predicting the optimal well location in fire-drive to injection wells based on injection-production linkage includes the following steps:

[0006] The optimal time for converting exhaust wells to gas injection wells and the values ​​of key engineering parameters during the fire-driven process under optimal production effects are determined using a fire-driven numerical model. Under the conditions of the key engineering parameters and the optimal time for converting exhaust wells to gas injection wells under optimal production effects, the recovery rate of the conversion wells at different locations is calculated using the fire-driven numerical model. The establishment process of the fire-driven numerical model includes: establishing a geological model based on the geological parameters of the oilfield block; and establishing a fire-driven numerical model with injection-production linkage of gas injection wells, conversion wells, and horizontal production wells based on the geological model.

[0007] The optimal well location of the injection well is predicted using the optimal well location evaluation model. The optimal well location evaluation model adopts a multivariate nonlinear fitting equation. The process of establishing the multivariate nonlinear fitting equation includes: establishing a multivariate nonlinear fitting equation between the gas injection well, the injection well, the horizontal well and the recovery rate according to the recovery rate corresponding to the different locations of the injection well.

[0008] Preferably, the geological parameters of the oilfield block include porosity, permeability, oil saturation, crude oil viscosity, reservoir pressure, reservoir depth, and oil layer thickness.

[0009] Preferably, the geological parameters of the oilfield blocks are taken as their respective average values, and a geological model is established based on the values ​​of the geological parameters of the oilfield blocks.

[0010] Preferably, when taking the average value of the geological parameters of each oilfield block, outlier data of each geological parameter are removed.

[0011] Preferably, the main control engineering parameters during the fire-driven process include the gas injection rate, the perforation location of the gas injection well, the distance between the gas injection well and the horizontal well, and the length of the horizontal section of the horizontal well.

[0012] Preferably, when using the fire-driven numerical model to determine the optimal time for converting the exhaust well to the gas injection well, the conversion time of the well with the highest recovery rate is taken as the optimal time for converting the exhaust well to the gas injection well.

[0013] Preferably, the timing for converting an exhaust well into an injection well includes: converting an exhaust well into an injection well when the air-to-oil ratio in the produced components rises rapidly, or converting an exhaust well into an injection well when the combustion front reaches the exhaust well;

[0014] The injection time of the injection well is:

[0015] The time when the air-to-oil ratio in the produced components reaches 1:1 during the rising phase of the air-to-oil ratio;

[0016] Or the time it takes for the combustion front to reach the exhaust well.

[0017] Preferably, the process of determining the values ​​of the main control engineering parameters in the fire-drive process under optimal mining effect using the fire-drive numerical model includes:

[0018] The main control engineering parameters are used as input variables of the fire-driving numerical model. Based on the recovery rate output by the fire-driving numerical model, the main control engineering parameters corresponding to the highest recovery rate are used as the values ​​of the main control engineering parameters under the optimal mining effect.

[0019] Preferably, the multivariate nonlinear fitting equation is as follows:

[0020]

[0021] Where a0~a9 are constants obtained from regression; y represents the recovery rate; a planar triangle is established with the injection well (A), the conversion well (B), and the vertical section (C) of the production well on the same horizontal plane of the fire-driven numerical model as the three vertices, then x1 represents the secant function value of ∠BAC, and x2 represents the secant function value of ∠BCA.

[0022] This invention also provides an optimal well location prediction system for fire-drive to injection wells based on injection-production linkage, comprising:

[0023] The calculation module is used to determine the optimal time for converting exhaust wells to gas injection wells and the values ​​of the main control engineering parameters during the fire-driven process under the optimal production effect using a fire-driven numerical model. Under the conditions of the optimal production effect and the optimal time for converting exhaust wells to gas injection wells, the module calculates the recovery rate of the conversion wells at different locations using the fire-driven numerical model. The establishment process of the fire-driven numerical model includes: establishing a geological model based on the geological parameters of the oilfield block; and establishing a fire-driven numerical model with injection-production linkage of gas injection wells, conversion wells, and horizontal production wells based on the geological model.

[0024] Prediction module: used to predict the optimal well location of injection wells using the optimal well location evaluation model for injection wells. The optimal well location evaluation model for injection wells adopts a multivariate nonlinear fitting equation. The process of establishing the multivariate nonlinear fitting equation includes: establishing a multivariate nonlinear fitting equation between the gas injection well, the injection well, the horizontal well and the recovery rate according to the recovery rate corresponding to the different locations of the injection well.

[0025] The present invention has the following beneficial effects:

[0026] This invention, based on an injection-production linkage-based optimal well location prediction method for injection-conversion wells in fire-flooding, combines conventional THAI fire-flooding technology with top-down fire-flooding technology, proposing a novel injection-production linkage fire-flooding technology. This technology avoids the problems faced by THAI fire-flooding technology during production and effectively combines the advantages of top-down fire-flooding technology, improving the vertical utilization of the formation. By changing the location of the injection-conversion well in the fire-flooding numerical model using the injection-conversion well-prediction method based on injection-production linkage, the recovery rate at different locations is obtained. The best-fit equation for the curve is analyzed using fitting software, and the optimal location of the injection-conversion well is determined based on the practicality of the fitted equation. This invention not only solves the problems faced by conventional THAI fire-flooding technology during production, extending production time and improving the vertical utilization of the formation, but also determines the optimal location of the injection-conversion well, significantly improving fire-flooding efficiency, maximizing the recovery rate, and providing a reliable reference for actual mine production. Attached Figure Description

[0027] The accompanying drawings, which are provided to further illustrate the invention and form part of this application, are not intended to limit the scope of the invention.

[0028] Figure 1 This is a flowchart of the optimal well location prediction method for fire-driven to injection wells based on injection-production linkage according to an embodiment of the present invention.

[0029] Figure 2 This is a three-dimensional schematic diagram of the injection-production linkage fire-drive technology in an embodiment of the present invention;

[0030] Figure 3 This is a two-dimensional supplementary schematic diagram of the injection-production linkage fire-drive technology in the embodiments of the present invention;

[0031] Figure 4 This is a comparison chart of the fire-driving effects of conventional THAI fire-driving technology and the injection-production integrated fire-driving technology in this embodiment of the invention;

[0032] Figure 5 This is a three-dimensional fitting diagram of the multivariate nonlinear fitting equation in an embodiment of the present invention;

[0033] Figure 6 This is a structural block diagram of the optimal well location prediction system for fire-driven to injection wells based on injection-production linkage, as described in this invention. Detailed Implementation

[0034] To make the objectives, technical solutions, and advantages of this invention clearer, the invention will be further described in detail below with reference to embodiments and accompanying drawings. Here, the illustrative embodiments and descriptions of this invention are used to explain the invention, but are not intended to limit the invention.

[0035] To effectively address the problems of gas channeling, over-coverage, and engineering risks inherent in conventional THAI fire flooding, this invention proposes a method for predicting the optimal well location of fire flooding-to-injection wells based on injection-production linkage. This method not only combines the advantages of THAI and Top-down fire flooding technologies, effectively reducing the development risks and extending production time of conventional THAI fire flooding, but also maximizes the combustion efficiency of fire flooding and further improves the recovery rate by predicting the injection time and optimal location of the injection well. This invention solves the problem of predicting the location of injection wells when increasing reservoir utilization, effectively extending production time and reducing engineering risks while significantly improving fire flooding efficiency, providing a reference for optimizing mining results in actual mining operations.

[0036] Reference Figure 1 The optimal well location prediction method for fire-driven to injection wells based on injection-production linkage of the present invention includes the following steps:

[0037] Step 1: Collect and organize the geological parameters of the oilfield block, process the geological parameter data, and determine the values ​​of the geological parameters for fire-driven oilfields; the geological parameters of the oilfield block include porosity, permeability, oil saturation, crude oil viscosity, reservoir pressure, reservoir depth, and oil layer thickness.

[0038] Step 2: Establish a geological model based on the geological parameters determined in Step 1;

[0039] Step 3: Determine the main control engineering parameters in the fire-driven process from numerous actual production parameters of the mine. The main control engineering parameters in the fire-driven process include the gas injection rate, the perforation position of the gas injection well, the distance between the gas injection well and the horizontal well, and the length of the horizontal section of the horizontal well. Based on Step 2, further establish a fire-driven numerical model of injection and production linkage, and use the obtained main control engineering parameters as variable inputs to the fire-driven numerical model for numerical simulation. Based on the recovery rate parameter output by the simulation results, determine the values ​​of the main control engineering parameters of the fire-driven numerical model under the optimal mining effect.

[0040] Step 4: Input the injection time of the gas well to the injection well as a variable into the fire-drive numerical model for simulation. Determine the optimal injection time of the gas well to the injection well based on the recovery rate obtained from the fire-drive numerical model simulation.

[0041] Step 5: Under the conditions of the main control engineering parameters of the fire-driven numerical model determined in Step 3 for optimal mining effect and the optimal conversion time of the gas well to the gas injection well determined in Step 4, change the location of the conversion well and obtain the recovery rate corresponding to the conversion well at different locations through the fire-driven numerical model.

[0042] Step 6: Use fitting software to fit the recovery rate data of the injection wells obtained in Step 5 at different locations, and establish a multivariate nonlinear fitting equation between the gas injection wells, injection wells, horizontal wells and the recovery rate.

[0043] The multivariate nonlinear fitting equation is as follows:

[0044]

[0045] Where a0 to a9 are constants obtained from the regression, and y represents the recovery rate; see Figure 2 and Figure 3 If a planar triangle is established with the injection well A, the conversion well B, and the vertical well section C of the production well on the same horizontal plane in the fire-driven numerical model as the three vertices, then x1 represents the secant function value of ∠BAC and x2 represents the secant function value of ∠BCA.

[0046] Step 7: Use the multivariate nonlinear fitting equation obtained in Step 6 as the evaluation model for the optimal well location of the injection well. Use this mathematical model to analyze and predict the optimal well location layout of the injection well and the corresponding fire-drive effect.

[0047] like Figure 6 As shown, the present invention also provides an optimal well location prediction system for fire-drive to injection wells based on injection-production linkage, comprising:

[0048] The calculation module is used to determine the optimal time for converting exhaust wells to gas injection wells and the values ​​of the main control engineering parameters during the fire-driven process under the optimal production effect using a fire-driven numerical model. Under the conditions of the optimal production effect and the optimal time for converting exhaust wells to gas injection wells, the module calculates the recovery rate of the conversion wells at different locations using the fire-driven numerical model. The establishment process of the fire-driven numerical model includes: establishing a geological model based on the geological parameters of the oilfield block; and establishing a fire-driven numerical model with injection-production linkage of gas injection wells, conversion wells, and horizontal production wells based on the geological model.

[0049] Prediction module: used to predict the optimal well location of injection wells using the optimal well location evaluation model for injection wells. The optimal well location evaluation model for injection wells adopts a multivariate nonlinear fitting equation. The process of establishing the multivariate nonlinear fitting equation includes: establishing a multivariate nonlinear fitting equation between the gas injection well, the injection well, the horizontal well and the recovery rate according to the recovery rate corresponding to the different locations of the injection well.

[0050] Example

[0051] The following uses the reservoir characteristic parameters of a heavy oil block in an oilfield as an example to illustrate the above-mentioned method for predicting the optimal location of fire-driven exhaust wells in thick oil layers based on injection-production linkage. It is worth noting that this embodiment is only for better illustrating the present invention and does not constitute a specific limitation on the present invention.

[0052] like Figure 1 As shown, the optimal well location prediction method for fire-driven to injection wells based on injection-production linkage in this embodiment includes the following steps:

[0053] Step 1: Collect and organize the reservoir geological parameter data of existing blocks in the oilfield. Taking the reservoir characteristics of a heavy oil block in a certain oilfield as an example, the reservoir geological parameters of each heavy oil block in the oilfield are shown in Table 1.

[0054] Table 1

[0055]

[0056] The geological parameters are processed as follows: First, the mean value of the parameters with the same characteristic parameters in the blocks is calculated (e.g., the porosity of blocks D and E is 22% to 26%, and the calculated average porosity is 24%). After removing outliers, the arithmetic mean value of the same characteristic parameters in the above blocks is obtained. The obtained mean value is used as the value of the geological parameters corresponding to the fire drive numerical model. The values ​​of each parameter are shown in Table 2.

[0057] Table 2

[0058]

[0059] Step 2: Establish a heavy oil geological model based on the geological parameter values ​​determined in Step 1.

[0060] Step 3: Based on actual field conditions and literature review, the key engineering parameters for fire-driven recovery were determined, including: gas injection rate, perforation location of the gas injection well, distance between the gas injection well and the horizontal well, and length of the horizontal section of the horizontal well. Building upon the geological model established in Step 2, a fire-driven recovery numerical model was further developed, incorporating the injection-production linkage of the gas injection well, the conversion well, and the horizontal production well. By changing the values ​​of the key control factors in the fire-driven recovery numerical model, the recovery rates under different conditions were obtained. The changes in gas injection rate, perforation location of the gas injection well, distance between the gas injection well and the horizontal well, and the length of the horizontal section of the horizontal well, along with the corresponding recovery rates, are shown in Table 3.

[0061] Table 3

[0062]

[0063]

[0064] To achieve optimal extraction results, based on the changes in recovery rate in the numerical model of fire-driven extraction under different conditions, the final values ​​of the main control engineering parameters in the fire-driven extraction numerical model with injection and production linkage were determined as follows: gas injection rate of 15000 m³ / h. 3 / d(After the exhaust well is converted to injection, the single well gas injection rate is 5000m 3 / d), the perforation location is the upper perforation, the distance between the gas injection well and the horizontal well is 50m, and the length of the horizontal section of the horizontal well is 200m.

[0065] Step 4: Determine the optimal conversion time for the injection well. Generally, there are two conversion times for injection wells: one is when the air-to-oil ratio in the produced components rises rapidly, reaching a 1:1 ratio, at which point the exhaust well becomes an injection well; the other is when the combustion front reaches the exhaust well. During the fire-drive numerical model simulation, the exhaust well was continuously venting. After determining the time of rapid rise in the air-to-oil ratio and the time when the combustion front reaches the exhaust well, the exhaust well was converted into an injection well at the corresponding time points for fire-drive simulation. The changes in conversion time and recovery rate are shown in Table 4. The optimal conversion time was determined to be when the air-to-oil ratio in the exhaust well rapidly increases.

[0066] Table 4

[0067]

[0068] Step 5: Based on the determination of the parameters of the fire-driven numerical model of injection-production linkage and the optimal injection time of the injection well, the recovery rate of the fire-driven numerical model under different well locations is obtained by changing the well location of the injection well. The relevant data of well location and corresponding recovery rate are shown in Table 5.

[0069] Table 5

[0070]

[0071]

[0072] Step 6: Analyze the recovery rate data of different injection well locations and corresponding positions obtained in Step 5, and use professional fitting software to fit the data to obtain the multivariate nonlinear equations of recovery rate and injection well location.

[0073]

[0074] Where y represents the recovery rate; a planar triangle is established with the injection well A, the conversion well B, and the vertical well section C of the production well on the same horizontal plane of the fire-driven numerical model as the three vertices, then x1 represents the secant function value of ∠BAC, and x2 represents the secant function value of ∠BCA.

[0075] Step 7: Analyze the fitted curve equation, from... Figure 4 and Figure 5 It can be concluded that the recovery rate when the development effect is best (x1=3.18, x2=1.81) is 51.20%, which is 17.2% higher than the recovery rate of injection-production linkage fire-driven recovery after the main control engineering parameters are determined; and 37.4% higher than the recovery rate of conventional THAI fire-driven recovery.

Claims

1. A method for predicting the optimal well location in fire-drive to injection wells based on injection-production linkage, characterized in that, The process includes the following: The optimal time for converting the exhaust well to the gas injection well and the values ​​of the main control engineering parameters during the fire-driven process under the optimal production effect are determined using a fire-driven numerical model. Under the conditions of the values ​​of the main control engineering parameters and the optimal time for converting the exhaust well to the gas injection well under the optimal production effect, the recovery rate corresponding to the conversion well at different locations is calculated using the fire-driven numerical model. The process of establishing the fire-drive numerical model includes: establishing a geological model based on the geological parameters of the oilfield block; and establishing a fire-drive numerical model with injection-production linkage, including gas injection wells, conversion wells, and horizontal production wells, based on the geological model. The optimal well location of the injection well is predicted using the optimal well location evaluation model. The optimal well location evaluation model adopts a multivariate nonlinear fitting equation. The process of establishing the multivariate nonlinear fitting equation includes: establishing a multivariate nonlinear fitting equation between the gas injection well, the injection well, the horizontal well and the recovery rate according to the recovery rate corresponding to the different locations of the injection well.

2. The method for predicting the optimal well location of a fire-driven to injection well based on injection-production linkage as described in claim 1, characterized in that, The geological parameters of the oilfield block include porosity, permeability, oil saturation, crude oil viscosity, reservoir pressure, reservoir depth, and oil layer thickness.

3. The method for predicting the optimal well location of a fire-driven to injection well based on injection-production linkage as described in claim 2, characterized in that, The geological parameters of each oilfield block are taken as their respective average values, and a geological model is established based on the values ​​of the geological parameters of the oilfield block.

4. The method for predicting the optimal well location of a fire-driven to injection well based on injection-production linkage as described in claim 3, characterized in that, When taking the average value of the geological parameters of each oilfield block, outlier data of each geological parameter are removed.

5. The method for predicting the optimal well location of a fire-driven to injection well based on injection-production linkage as described in claim 1, characterized in that, The key engineering parameters during fire-driven operations include the gas injection rate, the perforation location of the gas injection well, the distance between the gas injection well and the horizontal well, and the length of the horizontal section of the horizontal well.

6. The method for predicting the optimal well location of a fire-driven to injection well based on injection-production linkage as described in claim 1, characterized in that, When using the fire-driven numerical model to determine the optimal time for converting the exhaust well to the gas injection well, the conversion time of the well with the highest recovery rate is taken as the optimal time for converting the exhaust well to the gas injection well.

7. The method for predicting the optimal well location of a fire-driven to injection well based on injection-production linkage as described in claim 6, characterized in that, The timing for converting an exhaust well to an injection well includes: converting an exhaust well to an injection well when the air-to-oil ratio in the produced components rises rapidly, or converting an exhaust well to an injection well when the combustion front reaches the exhaust well; The injection time of the injection well is: The time when the air-to-oil ratio in the produced components reaches 1:1 during the rising phase of the air-to-oil ratio; Or the time it takes for the combustion front to reach the exhaust well.

8. The method for predicting the optimal well location of a fire-driven to injection well based on injection-production linkage as described in claim 1, characterized in that, The process of determining the values ​​of the main control engineering parameters in the fire-drive process under optimal mining effect using the fire-drive numerical model includes: The main control engineering parameters are used as input variables of the fire-driving numerical model. Based on the recovery rate output by the fire-driving numerical model, the main control engineering parameters corresponding to the highest recovery rate are used as the values ​​of the main control engineering parameters under the optimal mining effect.

9. The method for predicting the optimal well location of a fire-driven to injection well based on injection-production linkage as described in claim 1, characterized in that, The multivariate nonlinear fitting equation is as follows: Where a0~a9 are constants obtained from regression; y represents the recovery rate; a planar triangle is established with the injection well (A), the conversion well (B), and the vertical section (C) of the production well on the same horizontal plane of the fire-driven numerical model as the three vertices, then x1 represents the secant function value of ∠BAC, and x2 represents the secant function value of ∠BCA.

10. A system for predicting the optimal well location in fire-driven to injection wells based on injection-production linkage, characterized in that, include: Calculation module: used to determine the optimal time for converting the exhaust well to the gas injection well and the values ​​of the main control engineering parameters during the fire-drive process under the optimal production effect using the fire-drive numerical model; under the conditions of the values ​​of the main control engineering parameters and the optimal time for converting the exhaust well to the gas injection well, the recovery rate corresponding to the conversion well at different locations is calculated by the fire-drive numerical model. The process of establishing the fire-drive numerical model includes: establishing a geological model based on the geological parameters of the oilfield block; and establishing a fire-drive numerical model with injection-production linkage, including gas injection wells, conversion wells, and horizontal production wells, based on the geological model. Prediction module: used to predict the optimal well location of injection wells using the optimal well location evaluation model for injection wells. The optimal well location evaluation model for injection wells adopts a multivariate nonlinear fitting equation. The process of establishing the multivariate nonlinear fitting equation includes: establishing a multivariate nonlinear fitting equation between the gas injection well, the injection well, the horizontal well and the recovery rate according to the recovery rate corresponding to the different locations of the injection well.

Images