Research On Optimization Model And Algorithm Of Oil-water Well Measures Scheme Based On Computational Intelligence

With the development of many domestic oil fields gradually entering the stage of high water cut,taking measures to increase production has been the main method used by oil field units to delay the decline of production,stabilize oil and water control and improve profits.Adopting efficient measures can avoid ineffective measures and reduce unnecessary mining costs,thereby improving the economic benefits of enterprises.Therefore,predicting the effect of each measure in advance and optimizing the measure plan is the focus of the current oil production factories to rationally arrange the production stabilization measures and effectively control the production cost.In view of the above problems,this thesis starts with the study of the measure effect prediction,and proposed a measure effect prediction model that integrates hybrid dilated convolutional neural networks,bidirectional gated recurrent unit,and scaled dot-product attention mechanism,predicting the monthly oil production and water cut under various measures.Through step-by-step improvement experiments on the model and comparative experiments with other models,it has been demonstrated that the model proposed in this thesis has higher accuracy and effectiveness in predicting the effects of measures.Secondly,a measure scheme optimization model for oil-water wells based on an improved honey badge algorithm was proposed.Considering the potential of oil-water well measures,production decline rules and other constraints,a single-year measure planning model and a multi-year stochastic planning model were established with the goal of maximizing production,minimizing water content,and maximizing net present value.The effectiveness of the improved algorithm was verified by conducting experiments on benchmark test functions and comparing it with standard artificial bee colony algorithm,grey wolf algorithm,whale optimization algorithm,and particle swarm algorithm using elite reverse learning strategies and random search strategies to improve the population initialization and location update methods of the standard honey badge algorithm.The best measure scheme was obtained by using the improved honey badge algorithm to solve the proposed measure planning model.Finally,based on the above research results,this thesis designs a set of oil and water well measure scheme optimization system,which plays a certain reference value for the technical personnel of the oilfield in the single-year or multi-year measure planning.