Differential evolution for early-phase offshore oilfield design considering uncertainties in initial oil-in-place and well productivity

Abstract

During the early phases of offshore oil field development, field planners must decide upon general design features such as the required number of wells and maximum oil processing capacity (field plateau rate), usually by performing sensitivity studies. These design choices are then locked in subsequent development stages and often prevent from achieving optimal field designs in later planning stages when more information is available and uncertainties are reduced.

In the present study, we propose using numerical optimization of net present value (NPV) to advice field planners when deciding on the appropriate number of wells, maximum oil processing capacity (plateau rate) in a Brazilian offshore oil field. Differential Evolution (DE) is employed for solving the optimization models. The uncertainties considered are well productivity and initial oil-in-place, handled by (1) using the mean of the distributions and (2) Monte Carlo simulation. A multi-objective optimization was also formulated and solved including ultimate recovery factor in addition to net present value.

The proposed method successfully computes probability distributions of optimal number of wells, plateau rate and NPV. If one wishes to compute the mean of such distributions only, for most cases it is adequate to run an optimization using the mean of the input values instead of performing Monte Carlo sampling. The multi-objective optimization allows to find field designs with high ultimate recovery factor and high NPV. In this case, the value of NPV is similar to the optimum NPV value when optimizing NPV only. The methods described could provide decision support to field planners in early stages of field development.