Recent progress on reservoir history matching: a review

Abstract

History matching is a type of inverse problem in which observed reservoir behavior is used to estimate reservoir model variables that caused the behavior. Obtaining even a single history-matched reservoir model requires a substantial amount of effort, but the past decade has seen remarkable progress in the ability to generate reservoir simulation models that match large amounts of production data. Progress can be partially attributed to an increase in computational power, but the widespread adoption of geostatistics and Monte Carlo methods has also contributed indirectly. In this review paper, we will summarize key developments in history matching and then review many of the accomplishments of the past decade, including developments in reparameterization of the model variables, methods for computation of the sensitivity coefficients, and methods for quantifying uncertainty. An attempt has been made to compare representative procedures and to identify possible limitations of each.

Efficient global optimization of reservoir geomechanical parameters based on synthetic aperture radar-derived ground displacements

“…Only recently have appropriate inversion methodologies such as inversion algorithms (Iglesias and McLaughlin, 2012;Hesse and Stadler, 2014) and data assimilation techniques (Chang et al, 2010;Baù et al, 2015;Zoccarato et al, 2016) been implemented to resolve, or at least reduce, uncertainty problems in geomechanics. However, the simultaneous management of several uncertain parameters is still a challenge for most inverse approaches (Chang et al, 2010) and often leads to nonphysical solutions and underdetermined or otherwise ill-posed problems (Oliver and Chen, 2011).…”

A review of closed-loop reservoir management

“…To address this problem, Tillier et al (2013) presented an appropriate objective function for history matching of seismic attributes based on image segmentation and a modified Hausdorff metric. The objective function of history matching commonly has a complex shape and multiple local minima (Oliver and Chen 2011). This is mainly because unknown parameters are always much more numerous than available production measurements.…”

“…The other category is based on gradient-free optimization, such as simultaneous perturbation stochastic approximation, genetic algorithm, particle swarm optimization, and pattern search methods (PSMs). Oliver and Chen (2011) have summarized the recent progress on automatic history matching.…”

Simultaneous Inversion of Production Data and Seismic Attributes: Application to a Synthetic SAGD Produced Field Case

“…A number of methods have been proposed to regularize similar inverse problems (Carrera et al, 2005). A possibility consists of narrowing the search space, which can be performed by selecting an appropriate parameterization technique (Oliver, 2010;Le Ravalec-Dupin, 2010). The choice of appropriate parameterization is decisive: it impacts the final matched model and the overall performance of the optimization algorithm (i.e., it helps to avoid local minima and can make it easier to decrease the objective function).…”

“…It entails the iterative minimization of an objective function, that quantifies the data mismatch. Several techniques, referring to history-matching, were developed and shown to be efficient when dealing with production data (Carrera et al, 2005;Oliver and Chen, 2010). Recent works have been focusing on the joint integration of production data and 4D-seismic attributes into reservoir models (Vasco et al, 2004;Stephen et al, 2006;Roggero et al, 2007).…”