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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.

Today we have 50 years of research in assisted history matching that bring us many fascinating frameworks to obtain multiple history matched models. Recently the evolutionary optimization algorithms for history matching have enjoyed an increasing popularity in our community. However these methods are often criticized for their high computational demands. We are looking to improve the convergence speed of these algorithms while maintaining their ability to obtain multiple history-matched models to have realistic uncertainty quantification.In this paper, we discuss a new approach to history matching and uncertainty quantification using multiobjective stochastic population-based optimization. The current practice in industry is to sum the individual match results from wells and minimize a global misfit value in order to get a history matched model. This work proposes a methodology where we can handle different objectives of history matching separately. We extend original differential evolution (DE) algorithm to optimize multiple objectives. The new algorithm is called differential evolution for multiobjective optimization using Pareto ranking (DEMOPR). We couple this algorithm with Bayesian uncertainty quantification framework to estimate the uncertainty in future recovery.The DEMOPR algorithm is tested for history matching and uncertainty quantification of the PUNQ-S3 reservoir. We have discussed the benefits of new multiobjective approach in fulfilling the targets of PUNQ-S3 problem: obtaining good history matching results and accurate prediction of the ultimate oil recovery. Results are very promising and show better final misfit values and simultaneously, two times increase in the speed of history matching in comparison with the original DE algorithm. We also study the number of models required to reach a stable Bayesian credible interval in quantifying the production uncertainty. We show that multiobjective approach stabilizes faster than original DE which means we need fewer simulations to have a reliable uncertainty estimate in the proposed workflow.

This paper introduces a new stochastic approach for automatic history matching based on a continuous Ant Colony Optimization algorithm. Ant Colony Optimization (ACO) is a multi-agent optimization algorithm inspired by the behavior of real ants. ACO is able to solve difficult optimization problems in both discrete and continuous variables. In the ACO algorithm, each artificial ant in the colony searches for good models in different regions of the parameter space and shares information about the quality of the models with other agents. This gradually guides the colony towards models that match the desired behavior – in our case the production history of the reservoir. The use of ACO in history-matching has been illustrated on PUNQ-S3 reservoir simulation model in North Sea with 45 unknown parameters. This model is fitted to multivariate production data coming from multiple wells. The ACO algorithm is also integrated within a Bayesian framework to quantify uncertainty of the predicted ultimate recovery. Results confirm that Ant Colony Optimization can be used to generate multiple history-matched reservoir models and accurately predict the final recovery from the reservoir after 16.5 years. We have compared the history matching results of ACO algorithm with the Neighborhood Algorithm (NA) and NA coupled with a geostatistical framework applied to the same problem. This comparison indicates that ACO obtains the history matched models from fewer simulations. We conclude that Ant Colony Optimization is a fast, reliable and promising tool for reservoir history matching and uncertainty quantification.