Data-Driven Multi-Asset Optimisation Under Uncertainty: A Case Study Using the New Indonesia's Fiscal Policy

Abstract

Optimizing multiple assets under uncertain techno-economic conditions and tight government policies is challenging. Operator needs to establish flexible Plan of Development (POD)s and put priority in developing multiple fields. The complexity of production and the profit margin should be simultaneously evaluated. In this work, we present a new workflow to perform such a rigorous optimization under uncertainty using the case study of PHE ONWJ, Indonesia. We begin the workflow by identifying the uncertain parameters and their prior distributions. We classify the parameters into three main groups: operations-related (geological complexity, reserves, current recovery, surface facilities, and technologies), company-policies-related (future exploration plan, margin of profit, and oil/gas price), and government-related (taxes, incentives, and fiscal policies). A unique indexing technique is developed to allow numerical quantification and adapt with dynamic input. We then start the optimization process by constructing time-dependent surrogate model through training with Monte Carlo sampling. We then perform optimization under uncertainty with multiple scenarios. The objective function is the overall Net Present Value (NPV) obtained by developing multiple fields. This work emphasizes the importance of the use of time-dependent surrogate approach to account risk in the optimization process. The approach revises the prior distribution with narrow-variance distribution to make reliable decision. The Global Sensitivity Analysis (GSA) with Sobol decomposition on the posterior distribution and surrogate provides parameters’ ranking and list of heavy hitters. The first output from this workflow is the narrow-variance posterior distribution. This result helps to locate the sweet spots. By analyzing them, operator can address specific sectors, which are critical to the NPV. PHE ONWJ, as the biggest operator in Indonesia, has geologically scattered assets, therefore, this first output is essential. The second output is the list of heavy hitters from GSA. This list is a tool to cluster promising fields for future development and prioritize their development based on the impact towards NPV. Since all risks are carried by the operator under the current Gross Split Contract, this result is advantageous for decision-making process. We introduce a new approach to perform time-dependent, multi-asset optimization under uncertainty. This new workflow is impactful for operators to create robust decision after considering the associated risks.

Comparison of Gross Split Production Sharing Contract and Taxation Aspects for Economic Incentives in Indonesia

“…NPV is usually used as a capital allocation to analyze the benefits of a project to execute. In addition, NPV is a direct measure of profitability and provides an overview of how the contractor's cash flow will be affected by each project (Sajjad et al, 2021). Generally, a positive NPV value will be profitable, while projects with a negative NPV value will result in losses (Peterson and Fabozzi, 2002).…”