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Many decisions in petroleum exploration and development have to be made with significant uncertainties. Stochastic simulations are frequently used to capture the consequences of a decision in relation to the uncertain variables within reservoirs, infrastructures and economics. The results of stochastic simulations are often used in the following risk management process to optimize decisions. A big challenge in this process is to frame the outputs of stochastic simulations in a manner which makes sense to the decision-maker and provides him with clear judgemental insights into the problem and its related uncertainties. Traditional decision-tree analysis provides a good way of formulating and structuring decision problems, but one of its disadvantages is that only the expected value is used at a decision node. Most of the decision issues involve both continuous and discrete uncertainties, which can only be fully evaluated using distribution functions. By combining the decision-tree approach with stochastic simulation the term of expected value can be extended to a distribution function. Distribution functions can provide more details than expected values for understanding the influence of relevant uncertainties in the context of a full risk spectrum. Depending on the decision-maker’s risk preference, decisions can be made based on not only the expected value but also upside or downside. A simple example on Value of Information is used to demonstrate the combination of decision-tree approach and stochastic simulation, where the value of information realized by a decision is quantified by a distribution function through Monte Carlo simulation rather than an expected value. A real decision situation concerning infill drilling in a North Sea field is presented to show the procedure of stochastic modeling of a decision tree in the framework of integrated project valuation. Important decisions along the time horizon include scheduling of drilling campaign, pilot or side-track drilling of adjacent prospects, and installing of a new template. |
