Abstract
A new methodology for efficiently modeling, updating, and optimizing large waterflooding operations is presented that bases the ensemble closed-loop production optimization (EnOpt) upon the capacitance resistive model (CRM) as the underlying dynamical system. This allows us to use observation data from the production wells to characterize and forecast the reservoir response and further use them to control the injection wells to maximize the reservoir production and sweep efficiency (percentage of oil in-place replaced by the injection fluid). Using an ensemble based method allows us to incorporate nonlinear effects in the CRM description and allows a real-time estimation that may incorporate a variation of reservoir parameters in the CRM model with time. Basing the EnOpt method on CRM (an approximate model) as opposed to (first principles based) the reservoir simulations allows a much quicker computational response and ultimately can be helpful in cases where geological data is scarce and/or the operation involves a large number of wells. Synthetic examples are used to demonstrate how EnOpt/CRM can successfully match for the oil and water production rates with a good forecasting ability. Final optimized injection rates lead to a significantly higher oil production and a much improved reservoir sweep efficiency.
Published Version
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