Forecasting Injector/Producer Relationships From Production and Injection Rates Using an Extended Kalman Filter

Abstract

Summary This paper presents an adaptive method, using an extended Kalman filter (EKF), to forecast the injector/producer relationships (IPRs) between multiple injectors and a single producer on the basis of measured production and injection rates. For this method, we used a very simple parametric model, one with two parameters per injector, so that, if a producer depends upon N injectors, our model contains exactly 2N parameters. The EKF is used to adaptively estimate the 2N parameters, from which the IPR between each injector and a producer is then estimated. This approach was tested on synthetic, reservoir simulation, and real data. The tests for synthetic data used Monte-Carlo simulations; the tests for reservoir simulation data used blind data that were generated by a reservoir simulator; and, the tests for real data used data from a Chevron oil field. Test results on synthetic and reservoir simulation data demonstrate the feasibility of the EKF method, and test results on the real data match other field indications about the IPRs between injectors and a producer. All results confirm that this EKF method may provide a good way to infer and track the IPRs and may provide better insight about the IPRs.