A multi-objective optimization algorithm was used to develop five-year capital plans for a portfolio of unconventional assets. The approach explored development plan tradeoffs, while considering statistical uncertainty of reserves additions. The objective was to apply the theory of multi-objective methods to an existing planning model, and to evaluate if such methods can provide greater knowledge, faster analysis, and better solutions. The tradeoff analysis workflow applied recent advances in constrained multi-objective optimization. A hybrid optimization algorithm, combining elements of mathematical programming and evolutionary computation, was used to calculate tradeoff frontiers under varying assumptions and constraints. Portfolio metrics included net present value, capital, reserve replacement ratio, capital effectiveness, production, reserves, and well counts. Statistical metrics were used to calculate uncertainty bounds (P10 and P90) on portfolio reserves additions, plus confidence factors for achieving given reserves targets. Constraints on drilling activity by area over time were also applied. The optimization algorithm successfully calculated tradeoff frontiers and efficient portfolios. The approach showed how tradeoff frontiers change with varying assumptions and constraints, and provided knowledge of strategic opportunities and limitations. The model enabled rapid replanning as objectives, constraints, and source data changed. The optimizer delivered better solutions than the previous planning model, and helped to identify short lists of optimized capital plans faster than otherwise possible. Reserves uncertainty metrics quantified the tradeoff between reserves targets and reserves confidence. The analysis revealed weaknesses of published uncertainty methods, and highlighted possible improvements for future research.
展开▼
