Robust multi-objective production optimization with CO2 emissions reduction

In this paper, we describe an efficient methodology for simultaneously maximizing the expected profitability of oil field production and minimizing the expected emission of greenhouse gasses associated with the production through optimizing controls of the reservoir injection and production wells. Instead of simply minimizing water production and injection as is often done as a surrogate for energy consumption, we use an emissions calculator to account for the energy efficiency of the injection and compression system. Because our approach to minimization is efficient, we are able to account for uncertainty in geology during the minimization and to use the operational simulation model for the field. The most common approach to solving for Pareto optimal solutions is through some type of scalarization of the optimization problem. In this study, we apply the weighted-sum method, which despite its limitations when applied to problems with feasible regions for objective outcomes that are not convex provides Pareto optimal solutions at a relatively low cost. Finally, we apply the methodology to the problem of well controls for a three-well field in the Norwegian Sea with platform facilities shared by another field. The reservoir model has 330,000 active cells with an active aquifer. The emissions calculator uses pump characteristics to account for fuel usage attributed to water injection. Gas compression, water treatment, and base energy costs are estimated by calibration of allocated energy usage to historical production data. The expectation of the objective functions is approximated by the sample average of the objective functions over the ensemble of 50 history-matched model realizations. Control variables are the injector and producer rates over one-month intervals. The Stochastic Simplex Approximate Gradient (StoSAG) method was used to estimate the gradient of the scalarized objective function and a quasi-Newton method (BFGS) was used for minimization. Results showed that moderately large reductions in CO2 emissions from a reference case optimized purely for profitability could be obtained at the cost of modest reductions in NPV. Larger reductions in CO 2 emissions were costlier. Additionally, the optimized reservoir production strategies were not intuitively obvious, indicating that a formal multi-objective optimization approach was beneficial. • Demonstrate robust optimization for CO2 emission reduction and NPV maximization. • Characterized the Pareto front for the two objectives on a real field case. • Reduced predicted CO2 emissions 12% for a 2% reduction in net present value.