Abstract
Shale asset investment decisions are difficult to model because they are portfolios of options under complex and time-evolving uncertainties. Sequential development decisions must balance short-term cash flow with long-term value creation. Typical assets consist of thousands of locations, making exact analysis impossible. Consequently, decision support tools are very simple, usually consisting of decision trees, fixed price decks, and no midstream constraints. We develop a heuristic that maps the current information of an asset, such as inventory, prices, and estimated production, to a well schedule. We combine the heuristic with Monte Carlo simulation and decision trees to form a shale field development model that includes all relevant features while being scalable to realistic problem sizes. To demonstrate our method's performance, we create a sample shale asset with 370 wells, price and production uncertainty, midstream constraints, and 30 decision periods. On the sample problem, our method exhibits first-order stochastic dominance over the decision tree approach and improves expected value by 407%. Using our modeling approach will improve overall decision quality in shale asset management.
NSTL