An iterative ensemble Kalman filter for reservoir engineering applications

Abstract

The study has been focused on examining the usage and the applicability of ensemble Kalman filtering techniques to the history matching procedures. The ensemble Kalman filter (EnKF) is often applied nowadays to solving such a problem. Meanwhile, traditional EnKF requires assumption of the distribution's normality. Besides, it is based on the linear update of the analysis equations. These facts may cause problems when filter is used in reservoir applications and result in sampling error. The situation becomes more problematic if the a priori information on the reservoir structure is poor and initial guess about the, e.g., permeability field is far from the actual one. The above circumstance explains a reason to perform some further research concerned with analyzing specific modification of the EnKF-based approach, namely, the iterative EnKF (IEnKF) scheme, which allows restarting the

Review of Field Development Optimization of Waterflooding, EOR, and Well Placement Focusing on History Matching and Optimization Algorithms

“…Since the Brugge benchmark project, several researchers have applied new and improved methods to the same data in order to compare the performance with other methods [74]. One such study was performed by [75], who developed forms of the iterative ensemble smoother using the Levenberg-Marquardt method to increase computational efficiency and improve the quality of the history match.…”

The use of the ensemble Kalman filter for production management in the presence of impermeable obstructions

“…This is a type of inverse problem that aims at estimating reservoir model parameters such as porosities and permeabilities, among others, so as to minimize the mismatch between actual measurements and simulated values (Gu and Oliver 2006;Oliver and Chen 2011;Saad and Ghanem 2009). There are several approaches for automatic history matching which differ in whether the error function used to optimize the model parameters is linear or nonlinear (Krymskaya et al 2009). Most of the traditional history matching techniques do not allow for continuous model updating, instead they simultaneously use all recorded data to generate an appropriate reservoir flow model.…”

“…Most of the traditional history matching techniques do not allow for continuous model updating, instead they simultaneously use all recorded data to generate an appropriate reservoir flow model. However, with the recent technological advances and the deployment of permanent sensors to monitor the various reservoir and production parameters, the use of continuously available data becomes critical for keeping the reservoir model up-todate (Bianco et al 2007;Gu and Oliver 2006;Krymskaya et al 2009). Therefore, to overcome some of the limitations associated with traditional history matching techniques, sequential data assimilation methods are required.…”

“…Therefore, to overcome some of the limitations associated with traditional history matching techniques, sequential data assimilation methods are required. One of the most commonly used methods is the ensemble Kalman filter (EnKF), which is a Monte Carlo based methodology for history matching and real time updates of reservoir models (Aanonsen et al 2009;Chen et al 2009;Gu and Oliver 2006;Krymskaya et al 2009;Lorentzen et al 2006;Oliver and Chen 2011). First introduced by Evensen (1994) to calibrate oceanographic models (Bianco et al 2007), the EnKF has since been successfully employed in both groundwater and reservoir simulation and several review articles that assess its performance and successes are available (Aanonsen et al 2009;Evensen 2003;Oliver and Chen 2011).…”

A Time Domain Update Method for Reservoir History Matching of Electromagnetic Data

“…In the article by Aanonsen et. al., [14] an extensive overview about the application of EnKF in reservoir engineering was presented focusing primarily on the incorporation of production data [15], [16], [17]. With production data having been readily incorporated, there has been the desire to incorporate electromagnetic data to exploit the strong resistivity contrast for obtaining better forecasts and reducing ensemble spread.…”