Uncertainty Assessment of Well-Placement Optimization

Abstract

Summary Determining the best location for new wells is a complex problem that depends on reservoir and fluid properties, well and surface-equipment specifications, and economic criteria. Numerical simulation is often the most appropriate tool to evaluate the feasibility of well configurations. However, because the data used to establish numerical models have uncertainty, so do the model forecasts. The uncertainties in the model reflect themselves in the uncertainties of the outcomes of well-configuration decisions. We never possess the true and deterministic information about the reservoir, but we may have geostatistical realizations of the truth constructed from the information available. An approach that can translate the uncertainty in the data to uncertainty in the well-placement decision in terms of monetary value was developed in this study. The uncertainties associated with well placement were addressed within the utility-theory framework using numerical simulation as the evaluation tool. The methodology was evaluated by use of the Production forecasting with UNcertainty Quantification (PUNQ)-S3 model, which is a standard test case that was based on a real field. Experiments were carried out on 23 history-matched realizations, and a truth case was also available. The results were verified by comparison to exhaustive simulations. Utility theory not only offered the framework to quantify the influence of uncertainties in the reservoir description in terms of monetary value, but it also provided the tools to quantify the otherwise arbitrary notion of the risk attitude of the decision maker. A hybrid genetic algorithm (HGA) was used for optimization. In addition, a computationally cheaper alternative was also investigated. The well-placement problem was formulated as the optimization of a random function. The genetic algorithm (GA) was used as the optimization tool. Each time a well configuration was to be evaluated, a different realization of the reservoir properties was selected randomly from the set of realizations, all of which honored the geologic and dynamic data available from the reservoir. Numerical simulation was then carried out with this randomly selected realization to calculate the objective function value. This approach has the potential to incorporate the risk attitudes of the decision maker and was observed to be approximate but computationally feasible.