PSO (particle swarm optimization)-based dense oil reservoir yield replacement automatic optimization method

Abstract

The invention relates to a PSO-based dense oil reservoir yield replacement automatic optimization method, which optimizes the production opportunity of a single well according to dynamic development data of oil reservoir development so as to achieve the purpose of maximizing a final net present value and establish a yield replacement principle. The method comprises the following steps: firstly, calculating single-well movable reserves in an oil reservoir based on a numerical simulation means, and determining a basic use sequence according to the single-well movable reserves from high to low; then, by taking the exploitation interval time as an optimization parameter and the net present value as an optimization target, inputting development limitation conditions, namely output (oil production, liquid production and gas production) limitation, well number limitation and fund limitation, and establishing a tight oil reservoir output replacement automatic optimization model; and finally, on the basis of a particle swarm optimization (PSO) algorithm, planning is carried out step by step according to the limit level until all the limit conditions are met, and when the net present value reaches the maximum value, an oil field reserve utilization and yield replacement result is output. According to the method provided by the invention, the oil-gas field development principle and the automatic optimization theory are combined, and sustainable development of the oil field is ensured while the oil-gas field development benefits are maximized.

Description

Technical Field

[0001] This invention belongs to the field of oilfield development and stable production technology, specifically involving an automatic optimization method for production succession in tight oil reservoirs based on PSO. Background Technology

[0002] Unrestrained oilfield exploitation will trigger multiple crises. In the short term, overexploitation will accelerate the depletion of oil resources, leading to a precipitous drop in oilfield production capacity, and some regions may even lose their energy backbone within decades. Environmentally, disorderly exploitation damages geological structures, potentially causing land subsidence and groundwater pollution. Economically, it will lead to rising extraction costs and reduced efficiency, ultimately making oilfield development unsustainable. To achieve sustainable development, a scientific extraction quota system must be established.

[0003] The core function of production succession is to fill the gap caused by natural decline in production capacity, extend the lifespan of oilfields, optimize resource allocation, reduce development risks, and improve economic efficiency. A scientifically sound production succession strategy is crucial for the sustainable development of oilfields. From the perspective of stable oilfield production and reservoir protection, a scientific development strategy must achieve the dual goals of "reservoir pressure balance" and "geological structure stability." Current research on production succession, both domestically and internationally, generally involves analyzing production data during periods of decline to derive the decline pattern, then using this pattern for prediction, and finally, providing a production succession strategy for the study block. Another approach utilizes the results of detailed reservoir numerical simulations to study the distribution of remaining oil in the study area, and then designs a production succession plan. While some domestic and international research has explored production succession strategies, as of this application, no scholar has yet utilized automatic optimization algorithms to achieve automatic adjustment and optimization of succession strategies.

[0004] To ensure the sustainable development of oilfields and address the problem of slow efficiency and reliance on manual decision-making in production succession strategies, this paper proposes an automatic optimization method for production succession in tight oil reservoirs based on PSO (Pressure-Oriented Sequence). This method combines oil and gas field development principles with automatic optimization theory, scientifically plans production succession principles, ensures the stability of geological conditions within the reservoir, designs reasonable limiting development parameters, and optimizes the oilfield production succession scheme. Summary of the Invention

[0005] In order to overcome the shortcomings of the prior art, the purpose of this invention is to provide an automatic optimization method for production succession in tight oil reservoirs based on PSO, which can optimize the oilfield production succession scheme according to the geological and development characteristics of the study area.

[0006] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0007] An automatic optimization method for production succession in tight oil reservoirs based on PSO, characterized by comprising the following steps;

[0008] Step 1: Determining the development sequence of single wells:

[0009] For the combined development of multiple wells, the principle of production succession dictates that wells with higher production should be developed first, followed by wells with lower production. In the embodiments of this patent, the reservoir is first simulated and calculated, and then the recoverable reserves of each well are calculated and sorted in descending order, and subsequent optimized development is completed in this order.

[0010] Step 2, Limitations on Development Parameters:

[0011] To ensure the profitability of oil fields and prevent reservoir damage due to over-exploitation, it is necessary to limit the annual output, drilling workload, and capital investment of oil fields. This mainly includes the following:

[0012] 1) During the evaluation period, the annual oil production of the oilfield must reach the target oil production. During the contract period, the oil production of the reserve units that are below the economic limit should be stopped in time. The annual liquid production and gas production should not exceed the maximum annual liquid and gas production.

[0013] 2) The annual drilling capacity is limited, and the number of new wells drilled cannot exceed a certain limit.

[0014] 3) The annual investment in the oil field shall not exceed the permitted investment amount for that year; the oil field shall at least recover its costs during the evaluation period, and the total investment during the contract period shall not exceed the permitted total investment amount.

[0015] Step 3: Establishing the optimization model:

[0016] The particle swarm optimization algorithm finds the optimal solution through cooperation and information sharing among individuals in the swarm. All particles in the swarm adjust their speed and position according to their own optimal solution and the global optimal solution of the entire swarm. The formulas for updating speed and position are shown in equations (3) and (4).

[0017] v i =ω×v i +c1×rand()×(pbest i -x i )+c2×rand()×(gbest i -x i (3)

[0018] x i =x i +v i (4)

[0019] In equations (3) and (4), ω is the inertia factor, pbest i It is the individual optimal, gbesti It is the global optimum, i = 1, 2, ..., N, where N is the number of particles, v i It is the particle's velocity, x i is the position of the particle, rand() is a random number between (0,1), c1 and c2 are learning factors. In formula (3), the first part is called the memory term, which represents the influence of the previous velocity on the current update; the second part is called the self-cognition term, which represents a vector from the current point to the individual particle's historical optimal solution, that is, the influence of the particle's own experience on the current update; the third part is called the group cognition term, which represents a vector from the current point to the global historical optimal solution, that is, the influence of the population experience on the current update.

[0020] This invention uses Net Present Value (NPV) as the objective function. NPV is a financial indicator for evaluating the return on investment of a project. It represents the value of the project's future cash flows discounted to the present point in time, minus the initial investment amount, to calculate the project's net income. A positive NPV value indicates that the project is profitable, while a negative NPV value indicates a loss. Generally, if a project's NPV value is greater than zero, the project is considered feasible and can generate a return on investment.

[0021] The formula for calculating net present value is as follows:

[0022]

[0023] Where C0 is the initial investment amount, and is the Cth... t The annual net cash flow, r is the benchmark rate of return or discount rate, and n is the project production cycle.

[0024] Net present value (NPV) is a widely used economic evaluation method in the petroleum industry, primarily used to assess the economic benefits of petroleum exploration and development projects. Petroleum exploration and development involves substantial capital investment and a long investment return cycle; therefore, a comprehensive assessment of the project's investment costs, output benefits, and risks is necessary.

[0025] From Formula 5, the formula for calculating the net present value (NPV) of this study is as follows:

[0026] C t =f(Δx) i ,Δy i C ci ,S ci ,L ci ,θ ci )×oilprice (6)

[0027]

[0028] Among them, C tLet f(Δx) be the net cash flow in year t. i ,Δy i ,Azi,F cli ,S cli HL li ,θ i ) is a function of the annual oil production in year t, which is related to multiple production parameters, such as the toe coordinates of a horizontal well, C ci S is the crack width. ci L represents the crack spacing. ci For the crack half-length, θ ci The wellbore angle is denoted by FC; oilprice is the oil price; C0 is the investment cost, which typically includes the fixed investment cost per well, drilling cost, and fracturing cost; where FC is the fixed investment cost per well; C well The drilling cost per well; nwell represents the number of development wells; C facture Let nf be the fracturing cost and nf be the number of fractures per well. When calculating the net present value (NPV), this function consists of multiple parts, making direct solution difficult. The problem is decomposed into solutions to two subproblems: total economic input and total economic output. The subproblem of total economic input is further decomposed into horizontal well costs and fracture costs, which are then solved separately. Finally, the solutions are combined to form the NPV.

[0029] The beneficial effects of this invention are:

[0030] The method provided by this invention combines oil and gas field development principles and automatic optimization theory, and can automatically plan and design the utilization of oil and gas field production, which has strong advantages in stabilizing oil and gas field production and maximizing the benefits of oil and gas field development. Attached Figure Description

[0031] Figure 1 This is a three-dimensional pressure distribution diagram of a typical well group model used in this embodiment of the invention;

[0032] Figure 2 This is a flowchart illustrating the design scheme of the automatic production succession optimization method in an embodiment of the present invention.

[0033] Figure 3 This is a diagram showing the iterative results of production time optimization in an embodiment of the present invention;

[0034] Figure 4 This is a diagram showing the oil production of different schemes in the embodiments of the present invention;

[0035] Figure 5 This is a diagram showing the pressure distribution after 20 years under different schemes in this embodiment of the invention. Figure 6 Simulate the pressure distribution 20 years from now for different scenarios. Detailed Implementation

[0036] The invention will be further described below with reference to the accompanying drawings.

[0037] A basic numerical simulation model of a 50×50×9 tight oil reservoir fracturing horizontal well network is established using numerical simulation technology, as shown in the attached figure. Figure 1 As shown in the figure. The seepage mode of this model is dual-pore dual-seepage, where the fracture porosity is 1% and the permeability is 100 mD; the matrix porosity is 10% and the permeability is 0.1 mD. The initial reservoir average formation pressure is 12 MPa, the top depth is -1250 m, the layer thickness is approximately 90 m, and the crude oil viscosity is 15 mPa·s. The configuration relationship of "well network-fracture" is the configuration relationship with the maximum net present value. Based on this, the production timing of a single well is optimized. Under the constraints of reasonable development parameters, the automatic optimization of production succession is achieved with the goal of maximizing the final net present value. The specific implementation process is shown in the attached figure. Figure 2 As shown.

[0038] Example:

[0039] As described in the method, the established model is first simulated to obtain the exploitable reserves of each well. Based on the exploitable reserve evaluation results, the basic exploitation sequence of the reservoir is determined, and the calculation results are shown in Appendix Table 1. Finally, according to the production succession principle, the production wells are developed sequentially from high to low, and the development sequence is determined as follows: HW3, HW8, HW6, HW7, HW5, HW1, HW2, HW4.

[0040] Table 1. Reservoir Single Well Production and Development Sequence

[0041] well name <![CDATA[Cumulative oil production / (m 3 )]]> Development sequence HW3 55807 1 HW8 55222 2 HW6 43208 3 HW7 40073 4 HW5 38679 5 HW1 37652 6 HW2 37128 7 HW4 33299 8

[0042] As described in the method, in combination with the production characteristics of the model, reasonable limiting development parameters are designed under the premise of ensuring the stability of the geological conditions inside the reservoir. The specific limiting development parameters and their limiting ranges are shown in Appendix Table 2.

[0043] Table 2 Production Succession Parameter Limitations

[0044]

[0045] As described in the method, under the premise of confirming the well opening sequence, this optimization scheme only involves optimizing the time. Under the constraints of production, drilling, and net present value specified above, the particle number of the particle swarm is set to -20, the number of iterations to -10, and the total number of calculations is 200. To determine a reasonable computational cost, this invention conducted a survey of relevant data on horizontal well fracturing in tight reservoirs in multiple locations. Finally, calculation formulas for each indicator were formulated (see Table 3) to support accurate cost calculation and prediction. The fracture half-length and conductivity have a non-linear relationship with their cost, and a corresponding relationship between the two is established. Figure 3 As shown; the iteration results are attached. Figure 4As shown in the attached figure, based on the optimization results of the production time, the output of different schemes can be obtained. Figure 5 As shown.

[0046] Table 3. Cost of each NPV parameter.

[0047] parameter Oil price, $ / barrel interest rate,% Repair costs, MM$ Vertical well drilling cost, $ / ft Horizontal drilling cost, $ / ft value 85 10 5 100 1500

[0048] Based on the current stage of reservoir development, we conducted a 20-year depletion development simulation, obtaining pressure distribution under two different scenarios: without a production succession strategy and with a production succession strategy. (See attached figure.) Figure 6 As shown in the diagram, pressure distribution analysis conducted after 20 years of production clearly shows that the production model employing the production succession optimization strategy produces a more uniform pressure distribution compared to the model without this strategy. Therefore, the presence of a uniform pressure distribution may indicate greater potential for enhanced oil recovery when the reservoir undergoes secondary development in the future.

Claims

1. An automatic optimization method for production succession in tight oil reservoirs based on PSO, characterized in that, Includes the following steps; Step 1: Determining the development sequence of single wells: For the combined development of multiple wells, in accordance with the principle of production succession, wells with higher production should be developed first, followed by wells with lower production. First, it is necessary to simulate and calculate the reservoir, and then calculate and sort them according to the recoverable reserves of each well from highest to lowest, and complete the subsequent optimization development in this order. Step 2, Limitations on Development Parameters: To ensure the profitability of oil fields and prevent reservoir damage due to over-exploitation, it is necessary to limit the annual output, drilling workload, and capital investment of oil fields. This mainly includes the following: 1) During the evaluation period, the annual oil production of the oilfield must reach the target oil production. During the contract period, the oil production of the reserve units that are below the economic limit should be stopped in time. The annual liquid production and gas production should not exceed the maximum annual liquid and gas production. 2) The annual drilling capacity is limited, and the number of newly drilled wells cannot exceed a certain limit; 3) The annual investment in the oil field shall not exceed the permitted investment amount for that year; the oil field shall at least recover its costs during the evaluation period, and the total investment during the contract period shall not exceed the permitted total investment amount; Step 3: Establishing the optimization model: The particle swarm optimization algorithm finds the optimal solution through cooperation and information sharing among individuals in the swarm. All particles in the swarm adjust their speed and position according to the individual optimal solution they find and the global optimal solution of the entire swarm. The formulas for updating speed and position are shown in equations (3) and (4). v i =ω×v i +c1×rand()×(pbest i -x i )+c2×rand()×(gbest i -x i ) (3) x i =x i +v i (4) In equations (3) and (4), ω is the inertia factor, pbest i It is the individual optimal, gbest i It is the global optimum, i = 1, 2, ..., N, where N is the number of particles, v i It is the particle's velocity, x i is the position of the particle, rand() is a random number between (0,1), c1 and c2 are learning factors. In formula (3), the first part is called the memory term, which represents the influence of the previous velocity on the current update; the second part is called the self-cognition term, which represents a vector from the current point to the individual particle's historical optimal solution, that is, the influence of the particle's own experience on the current update; the third part is called the group cognition term, which represents a vector from the current point to the global historical optimal solution, that is, the influence of the population experience on the current update. Using Net Present Value (NPV) as the objective function, the formula for calculating NPV is as follows: Where C0 is the initial investment amount, and is the Cth... t The annual net cash flow, r is the benchmark rate of return or discount rate, and n is the project production cycle; Net present value (NPV) is used to evaluate the economic benefits of oil exploration and development projects, comprehensively assessing the project's investment costs, output benefits, and risks. From Formula 5, the formula for calculating the NPV in this study is as follows: C t =f(Δx i ,Δy i ,C ci ,S ci ,L ci ,θ ci )×oilprice (6) Among them, C t Let f(Δx) be the net cash flow in year t. i ,Δy i ,Azi,F cli ,S cli HL li ,θ i ) is a function of the annual oil production in year t, which is related to multiple production parameters, such as the toe coordinates of a horizontal well, C ci S is the crack width. ci L represents the crack spacing. ci For the crack half-length, θ ci The wellbore angle is denoted by FC; oilprice is the oil price; C0 is the investment cost, which typically includes the fixed investment cost per well, drilling cost, and fracturing cost; where FC is the fixed investment cost per well; C well The drilling cost per well; nwell represents the number of development wells; C facture Let nf be the fracturing cost and nf be the number of fractures per well. When solving for the net present value (NPV), this function consists of multiple parts and is difficult to solve directly. Therefore, the problem is decomposed into two subproblems: total economic input and total economic output. The subproblem of total economic input is further decomposed into horizontal well cost and fracture cost, which are solved separately. Finally, the solutions are combined to form the NPV.

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