Research On Optimization Design Method Of Low Permeability Oil CO₂ Flooding Well Based On Particle Swarm Optimization Algorithm

In recent years,the proportion of low-permeability reservoirs has increased year by year.Due to the large differences between low-permeability reservoirs and conventional reservoirs,the traditional area well pattern is difficult to adapt to new development needs.Therefore,the well layout of low-permeability reservoirs has become a research hotspot.As the key link of CCUS?carbon capture,transport,utilization,and storage?technology,CO₂ flooding network has the dual role of improving low-permeability reservoir recovery and reducing carbon emissions,which can effectively promote the development of low-permeability reservoirs.Aiming at the problems of low permeability reservoir parameters and difficult modeling of conventional methods,a nonlinear regression model for low-permeability reservoirs was obtained using a data modeling method.The analysis results show that the established model has good accuracy and reliability.The low-permeability reservoir model has the characteristics of diversity and high dimensionality.The numerical optimization method has a complex structure and takes a long time.Therefore,this paper will study the optimization algorithm of CO₂ flooding network in low permeability reservoirs based on particle swarm optimization.In order to solve the problems of long time-consuming and inaccurate results in the process of solving multiple high-dimension functions of low-permeability reservoirs by standard particle swarm optimization,the algorithm is improved.A hybrid adaptive particle swarm optimization algorithm is proposed.The results show that the improved algorithm proposed in this paper has better search capability in optimizing multiple functions.Area well network is currently the commonly used mining method in China.Aiming at the problem of well spacing,gas injection rate,and well shut-in time for CO₂ flooding in homogeneous low-permeability reservoirs,a well-area model of homogeneous oil reservoir with a maximum net present value was established.On this basis,the sensitivity analysis of horizontal well length and production time was carried out,and its influence on reservoir development was obtained.Aiming at the differences in final oil recovery and CO₂ storage at different locations caused by the difference of permeability distribution in heterogeneous low permeability reservoirs,a dual-objective joint optimization model was established with the goal of optimizing the maximum oil production and minimum gas-oil ratio.,By optimizing its solution to get the best position.Further consider the issue of encryption in the late stage of well network development,and propose a well network intelligent encryption method.Finally,the sensitivity analysis of well spacing and injection rate was carried out,and its influence on well pattern development was analyzed.For the problem of large uncertainties in reservoir parameters,a robust optimization method is applied to well pattern optimization.Considering the uncertainty of oil price,drilling cost and bottom hole pressure separately,a well robust optimization method was proposed.The simulation results show that the robust optimization of well pattern well ensures the stability of reservoir development.