A New Model to Infer Interwell Connectivity in Low Permeability Oil Field

摘要

This paper presents a new model to infer interwell connectivity in low permeability oil field. Establishing connectivity among various injectors and producers is a key to improve the understanding of a reservoir under waterflood. This understanding improves the estimates for ultimate recovery and also helps to better define the future development plan. Because of the complex pore structure, the seepage law of low permeability oilfield is more complex. Therefore, it is difficult to study the interwell connectivity. There is a threshold pressure gradient in fluid flow in low permeability reservoirs. Therefore, the productivity formula of low permeability reservoir is deduced from the equation of motion, which is a linear model when single phase flow or water cut is stable. Similar to the capacitance-resistance model (CRM), the productivity formula and the material balance equation are combined to get the calculation model of accumulative liquid production and cumulative water injection. And the connection coefficient is solved by using the least square principle and genetic algorithm. Different numerical simulation cases are employed to validate the derived method. The traditional method is the relationship between the instantaneous liquid production and the instantaneous water injection, and this model is the relationship between the cumulative liquid production and the cumulative water injection. In addition, the paper presents a case study to compare findings from the results of the new model and the use of interwell tracer. The results obtained from the new model shows good agreement with the results obtained from interwell tracer. The new model has been applied to nearly many wells. Compared with the single-well dynamic analysis, the accuracy of the new model is very high, but the spent time is less than half of the single-well dynamic analysis. This method is a rapid way to have a more reliable understanding of the reservoir heterogeneity and quick prediction of reservoir performance to optimize the waterflood. The method was applied to an oil field which is effective for establishing well interaction pattern. Recommendations were given to improve waterflood efficiency.