SPE 136861 Comparative Study of Novel Population-Based Optimization Algorithms for History Matching and Uncertainty Quantification: PUNQ-S3 Revisited Yasin Hajizadeh, Mike Christie, Vasily Demyanov, SPE, Institute of Petroleum engineering, Heriot Watt University, Edinburgh, UK Copyright 2010, Society of Petroleum Engineers This paper was prepared for presentation at the Abu Dhabi International Petroleum Exhibition & Conference held in Abu Dhabi, UAE, 1–4 November 2010. This paper was selected for presentation by an SPE program committee following review of information contained in an abstract submitted by the author(s). Contents of the paper have not been reviewed by the Society of Petroleum Engineers and are subject to correction by the author(s). The material does not necessarily reflect any position of the Society of Petroleum Engineers, its officers, or members. Electronic reproduction, distribution, or storage of any part of this paper without the written consent of the Society of Petroleum Engineers is prohibited. Permission to reproduce in print is restricted to an abstract of not more than 300 words; illustrations may not be copied. The abstract must contain conspicuous acknowledgment of SPE copyright. Abstract In modern field management practices, there are two important steps that shed light on a multimillion dollar investment. The first step is history matching where simulation model is conditioned to production and/or seismic data. In this inverse problem, we calibrate our model to reproduce the historical observations from the field. In second step we quantify uncertainty of the predictions made by calibrated models. These two steps are tied together; multiple history matched models are essential for a realistic uncertainty estimate of the future field behavior. Stochastic population-based optimization methods have been used in the last two decades as popular tools in history matching frameworks. Stochastic sampling algorithms explore/exploit the parameter space to find diverse good-fitting models. Recently two innovative algorithms were proposed to tackle history matching problem; ant colony optimization [Hajizadeh et al. 2009] and differential evolution [Hajizadeh et al. 2010]. However these algorithms were applied for history matching of a simple reservoir model with few unknown parameters. The question is the capability of these new methods for solving complex history matching problems and estimation of the uncertainty associated with these models. This paper compares the application of ant colony, differential evolution and neighbourhood algorithms for history matching and uncertainty quantification of the PUNQ-S3 reservoir. PUNQ-S3 model is a synthetic benchmark case with challenging parameterization, history matching and uncertainty quantification steps. We compare performance of the above algorithms in sampling the parameter space and obtaining multiple history-matched models. The paper also includes comparison of convergence properties of these algorithms for this high dimensional problem. We show that novel stochastic population-based optimization algorithms can be successfully applied for history matching problems with large number of unknown parameters. The algorithms are integrated with a Bayesian framework to quantify uncertainty of the predictions. Results confirm that the proposed methodology provides reliable predictions of the future reservoir recovery. Introduction History matching of reservoir models and uncertainty quantification of the predictions are two important steps in any field management process. In history matching phase, simulation models are calibrated based on the observed production history of the reservoir. Parameters of the reservoir model are perturbed and model’s output is compared with available fluid production rates or pressure measurements. This procedure is repeated until a good agreement is obtained between output of the simulation and observations. History matching is an inverse problem with non-unique solutions. Multiple combinations of the reservoir properties can provide a good match to observed field behavior. In order to realistically quantify the uncertainty of predictions, multiple history-matched reservoir models are required. Diverse models are likely to show different production behavior in the future due to different reservoir parameters.