Hydrocarbon Field (Re-)Development as Markov Decision Process

Abstract

Summary Hydrocarbon field (re-)development requires that a multitude of decisions are made under uncertainty. These decisions include the type and size of surface facilities, location, configuration, and number of wells but also which data to acquire. Both types of decisions, which development to choose and which data to acquire, are strongly coupled. The aim of appraisal is to maximize value while minimizing data acquisition costs. These decisions have to be done under uncertainty owing to the inherent uncertainty of the subsurface but also of other costs and economic parameters. Conventional value of information (VOI) evaluations can be used to determine how much can be spent to acquire data. However, VOI is very challenging to calculate for complex sequences of decisions with various costs and including the risk attitude of the decision-maker. We are using a partially observable Markov decision process (MDP) to determine the policy for the sequence and type of measurements and decisions to do. A partially observable MDP (POMDP) is characterized by the states (here: description of the system at a certain point in time), actions (here: measurements and development scenario), transition function (probabilities of transitioning from one state to the next), observations (measurements, appraisal activity), and rewards [costs for measurements, expected monetary value (EMV) of development options]. Solving the MDP gives the optimum policy, sequence of the decisions, the probability of maturation (POM) of a project, the EMV, the expected loss, the expected appraisal costs, and the probability of economic success (PES). These key performance indicators can then be used to select in a portfolio of projects the ones generating the highest expected reward for the company. Combining the production forecasts from numerical model ensembles with probabilistic capital and operating expenditures and economic parameters allows for quantitative decision-making under uncertainty.