Optimisation of gas field portfolios in the presence of uncertainty

Abstract

The study proposes an approach that incorporates both surface and subsurface parameters of gas projects in a portfolio model called the integrated stochastic gas economic optimisation model (ISGEM_opt). The model uses Monte Carlo simulation and genetic algorithms (GAs) to optimise the portfolio’s production strategy and satisfy contractual requirements under gas-in-place uncertainty. The production strategy is defined as the production rate and the starting time of each field in the portfolio. The optimised strategy demonstrates how to add a scheduling feature to an oil and gas portfolio to meet project milestones. A conceptual model is developed to illustrate how production can be optimised in the presence of uncertainty; it is not specific to any particular system (a synthetic model is used). The model can also be used to demonstrate how risk and expected net present value (NPV) can be combined to select an optimum production strategy. This approach is desirable because optimum solutions vary depending on the defined level of risk. The impact of uncertainty for several gas projects on the expected NPV is evaluated. This allows for defining the maximum amount that investors are willing to pay for information to reduce uncertainty. The research also studies the impact of a much broader set of cases on the expected NPV. These cases are varied by the contractual or regulatory demands, number of projects, potential penalties and discount rates. This is achieved by creating a response surface that works as a surrogate for the ISGEM_opt. These analyses allow for insights into what constitutes good and bad strategies for maximising value while minimising risk.