Research On Waterflood Optimization Method Using Streamlines

Waterflood performance of mature fields can be improved significantly by modifying injection and production rates at individual wells.Compared to improving production through infill wells,rate changes are economical and readily implemented.Permeability heterogeneity leads to water directivity and waterflooding disproportion.It affect the field development seriously.From existing production experience,it is closely related to the equilibrium degree of waterflooding.Waterflooding oil field can use the method of injectiong/production strategy to make waterflood equilibrium,then get the purpose of improving development.Based on above,the definition of the equilibrium displacement is proposed.We use saturation variance to evaluate the degree of displacement equalization and take it as object function.In most traditional optimization methods,the number of evaluations of the objective function at each optimization step is of the same order as the number of control variables.As a result,applying traditional optimization methods to the exploitation of mature waterfloods generally involves elevated computational costs.The reservoir engineering method leads to major calculation error.To address this problem,we propose a new method,combine numerical simulation and reservoir engineering methods to solve the objective function,hereafter called linearization method.Firstly,the injection and production control units are divided and the average property value is computed for each of the control unit.Then we can calculate the initial oil saturation in each control unit.We propose a new optimization method based on flux patterns in which the number of simulations per optimization step is independent of the number of control variables.At each optimization step,our method approximates the complicated objective function of well rates by means of a local linear sensitivity analysis based on the flux patterns generated by streamline simulation or a finite-volume flow diagnostic technique.The generation of the flux patterns requires only a single simulation.This sensitivity analysis allows the oil/water production rates to be estimated as linear functions of well rates,and hence it locally linearizes the objective function.Using the linearized objective function within this optimization step does not require additional simulation until the determination of next optimization step,which reduces the computational cost dramatically compared to traditional optimization approaches.Using a concept model to validate the method,the result show that the method is feasible.The control optimization method is applied to a large scale real oilfield.The result are compared with those of the current plan provided by the oil industry.A higher performance is obtained by our optimization approach than by the current plan.