Stochastic Economic Ranking Addresses Risk and Uncertainty for Decision-Makers

Abstract

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 213385, “Stochastic Economic Ranking: A Prudent Way To Address Risk and Uncertainty for Decision-Makers,” by Mahesh Narayanan, SPE, Mehaboob Abdulazeez, SPE, and Khadija Bukhamsin, SPE, Saudi Aramco, et al. The paper has not been peer reviewed. _ Project screening and ranking is inevitable even when an organization faces no resource bottlenecks. In project economic predictions, the stochastic approach is prevalent and proves to be the most natural approach to address technical and commercial uncertainties. In contrast with a deterministic approach, a stochastic Monte Carlo simulation helps in understanding the range of possible outcomes to enable effective decision-making. The objective of the complete paper is to present an integrated stochastic work flow that addresses the subsurface, surface, and cost uncertainties to screen, rank, and grade opportunities early in the derisking process. Introduction The proposed work flow simultaneously can address all technical and commercial uncertainties with a Monte Carlo model and predict a range of possible economic outcomes with customized economic analysis tools. In other words, it can achieve complete unbiased sample randomness in expected project outcomes. The work flow also creates a stage-gate decision-tree approach and reviews economics as a series of staged investments. This helps to expose incremental capital in the life cycle of a project responsibly and help with course correction where necessary. Value Addition: Stochastic Approach A common practice in the oil and gas industry is to simply examine low, middle, and high cases. This is a limited view of possible scenarios and provides a false sense of precision regarding expected project outcomes. The term “stochastic” refers to the property of being well-described by a random probability distribution. A stochastic approach features the following advantages: - A stochastic model addresses uncertainties in inputs with complete unbiased randomness in the outcomes. - By running thousands of calculations and using many different estimates of future economic conditions, stochastic models predict a range of possible future investment results, showing the potential upside and downside of each. - Once the work flow is followed and the model is set up, multiple realizations can be created with a single click. Proposed Integrated Stochastic Methodology In the world of project economics, classical stochastic economics is a tool to address uncertainty and risk. In simple terms, a mean or deterministic view of a production profile, along with one view of a schedule, is taken and blended with probabilistic distribution of cost inputs. This data is then taken through a Monte Carlo model to create multiple scenarios to predict a range of possible economic outcomes. This narrow range of economic outcomes only covers the uncertainty in cost but does not cover variability in well performance and time.