Abstract
• Differential evolution optimization of number of wells, plateau rate and NPV. • Multi-objective optimization considering ultimate recovery factor and NPV. • The NPV distribution average can be estimated with the average of the variables. • Method provides decision support to field planners in early field development. 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.
Highlights
The field development process is complex, demanding large sums of capital (CAPEX) and operational (OPEX) expenditures to produce hy drocarbons
FPSO 150, 000 bpd 7 million std m3/d 200, 000 bpd for maximizing the benefits while guaranteeing oil and gas field pro duction, Almedallah and Walsh [15] proposed a hybrid k-means clus tering and mixed-integer linear programming approach for optimizing the drilling path constraints; Gonzalez et al [16] proposed a decision support method to advice field planners during early-phase develop ment, and formulated an optimized net present value (NPV) as a mixed-integer linear problem using SOS2 models; and Hoffmann et al [17] proposed a coupling strategy for maximum oil production at each time step of a small North Sea offshore field using a linear problem with SOS2 models
Renamed the well rate decline factor (b) with the letter “m” and made it dependent on initial oil-in-place (N), well productivity (J) and number of wells (Nw). This was achieved by assuming the reservoir as under-saturated and produced by natural depletion, and that reser voir pressure never drops below the bubble point pressure during the production lifetime
Summary
The field development process is complex, demanding large sums of capital (CAPEX) and operational (OPEX) expenditures to produce hy drocarbons. In early stages of development, field planners must decide, with limited information, upon the approximate number of wells required, maximum capacities of topside facilities and field production schedule. These parameters have a large impact on the economic feasibility of the project [1,2]. A high number of wells and high pro cessing capacities increase extraction rates, selling hydrocarbons earlier and minimizing the effect of cash flow discounts, which improves the net present value of the project This strategy implies higher drilling expenditures and expensive topside facilities. CAPEXSUB cost of subsea system (risers, flowlines, umbilicals, Xtrees, manifolds, installation, mooring of FPSO) [USD]
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