Well Count Optimization Incorporating a Wide Range of Uncertainties for the Deepwater Agbami Field

Abstract

This paper describes the methodology for incorporating uncertainties in the optimization of well count for the deepwater Agbami project. The lack of substantial reservoir description data is common in many deepwater discoveries. Therefore, the development plan must be optimized for a wide range of uncertainties. In the Agbami project, the design of experiments or experimental design (ED) technique was incorporated to optimize the well count across a wide range of subsurface uncertainties.Multiple well count development plans (high, mid, and low) were developed and used as a variable in ED. Also, multiple geologic models representing the broad range in uncertainty in oil in place (greater than a factor of two) and in net-to-gross were built and used as a variable. Additional uncertainty variables, including permeability, fault seals, and injection conformance, were studied concurrently. The ED technique allowed multiple well counts to be quickly tested against multiple geologic models. With the net present value (NPV) calculated for each case, not only was the well count for the overall highest NPV determined, but ED allowed discrete testing of each geologic model to determine the optimum well count for each model.A methodology was developed for optimizing well count development plans over a broad range of uncertainties including a range of geologic models, which vary in oil in place and net-to-gross. The process allowed testing the robustness of any well count versus any uncertainty (or set of uncertainties).A method was demonstrated quantifying the difference between perfect and imperfect knowledge of the reservoir description (geologic model) as it pertains to well locations.A total well count of 38 was concluded to be the optimum well count for the Agbami project based on NPV. This well count proved to be robust across the full range of uncertainties tested.

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