Incorporating Uncertainties in Well-Count Optimization With Experimental Design for the Deepwater Agbami Field

Abstract

This paper describes a methodology for incorporating uncertainties in the optimization of well count for the deepwater Agbami field development. The lack of substantial reservoir-description data is common in many deepwater discoveries. Therefore, the development plan must be optimized and proven to be robust 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.The lack of substantial reservoir-description data is common for many deepwater discoveries. In the Agbami project, the uncertainty in oil in place was significant (greater than a factor of 2). This uncertainty was captured in a range of earth (geologic) models. Additional uncertainty variables, including permeability, fault seals, and injection conformance, were studied concurrently. Multiple well-count development plans (high, mid, and low) were developed and used as a variable in ED. The ED technique allowed multiple well counts to be tested quickly 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 discrete testing of each geologic model determined the optimum well count for each model. The process allowed for testing the robustness of any well count vs. 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.

New Method Combines Simulation and Novel Spreadsheet Tools to Enable Direct Optimization of Expansion Decisions in a Giant Heavy-Oil Field

“…Clarkson and McGovern (2005) presented some practical tools for evaluating coalbed-methane reservoirs, but according to the authors, they do "not explicitly account for well-to-well interference," which was a prime concern in our attempt to optimize well density. Narahara et al (2005) used experimental design techniques in optimizing the well count in view of high reservoir uncertainty; their work used manually created well-count scenarios and did not address our problem of how to quickly place hundreds to thousands of high-quality wells in a very large model. In the end, none of these techniques was applicable to our problem.…”