Optimization Methods for History Matching of Complex Reservoirs

Abstract

TX 75083-3836, U.S.A., fax 01-972-952-9435. AbstractConventional direct optimization methods and Evolutionary Algorithms are applied to the problem of history matching in reservoir engineering. For the optimization of complex reservoir models the potential of parallel computing is investigated. Methods to improve the convergence of Evolutionary Algorithms by introducing expert knowledge is discussed. An interface program has been developed which links an industry standard reservoir simulator to an optimization software package designed as a multipurpose environment for parallel optimization. Permeabilities, fault transmissibilities as well as relative permeabilites and barrier locations have been included in the optimization. Results are presented for synthetic and real reservoirs with up to 30 wells and 40 design parameters. The potential and the area of applicability of the optimization method to the problem of reservoir modeling in various modeling phases are discussed. The improvement of performance based on parallelism in a network environment is evaluated. In conclusion, results suggest that Evolution Strategies can be successfully applied for generating possible solutions in the early modeling phase. The introduction of expert knowledge to the optimization methods is essential for reducing the multidimensional search space and improving convergence.

A Step Forward to Closing the Loop between Static and Dynamic Reservoir Modeling

“…In order to account for geological uncertainties and integrate dynamic data to geological model several algorithms of different nature have been proposed, such as: spectral decomposition (Reynolds et al, 1996); pilot point method (RamaRao et al, 1995) gradual deformation method (Hu, 2000), probability perturbations methods (Caers, 2003) and spectral co-simulation perturbation (Le Ravalec-Dupin and Da Veiga, 2001). History matching is often performed on the basis of gradient-based methods or evolutionary strategies (Schulze-Riegert, et al, 2001;Schulze-Riegert and Haase, 2003;Zitzler, 1999).…”

Assisted History Matching for Petroleum Reservoirs in the Social Computing Era

“…The advantage of the multi-objective functions is that they can simultaneously minimize different kinds of data using the Pareto criterion. Several methodologies and techniques have been studied for such optimization problems (Gomez et al, 2001;Schaaf et al, 2008;Riegert et al, 2001). They can be roughly divided into local and global algorithms.…”

“…Conversely, global algorithms can provide multiple solutions in a single run and can escape from local minima efficiently. Heuristic methods such as simulated annealing, Genetic Algorithms (GA) and Evolutionary Strategies (ES) are known to be highly effective searching techniques (Riegert et al, 2001). Nonetheless, they require a large number of evaluations of the misfit function; in most cases these evaluations are represented by simulation runs.…”

History matching of petroleum reservoirs employing adaptive genetic algorithm

“…This optimization technique usually involves minimizing the objective function that describes the mismatch between the available field historic data and reservoir simulator response. GA as a stochastic optimization tool outperforms other gradient based methods (steepest descent, GaussNewton method, conjugate gradient etc.,) toward reaching a global optimal solution escaping the local optima (Gill 1981;Ouenes 1992;Tamhane et al 2000;Gomez et al 2001;Romero and Carter 2001;Schulze-Riegert et al 2001;Choudhary et al 2007). …”