PREDICTION OF OIL TANK BEHAVIOR USING A PROXY FLOW MODEL TECHNICAL AREA

摘要

Using production data and a production flow record based on production data, a deep neural network (RNP) is formed to model a simulation of proxy flow from a reservoir. The simulation of proxy reservoir flow is performed using an overall Kalman filter (FKen), based on the RNP formed. FKen assimilates new data by updating a current set to match historicals by minimizing the difference between a predicted production output from the proxy flow simulation and measured production data from 'a deposit. Using the current updated assembly, a second proxy reservoir flow simulation is performed based on the RNP formed. Assimilation and realization are repeated while new data is available for assimilation. The predicted behavior of the reservoir is determined based on the simulation of proxy reservoir flow. An indication of the predicted behavior is provided to facilitate the production of fluids from the reservoir. Figure to be published with the abstract: Fig. 1