An Automatic History Matching Module with Distributed and Parallel Computing

Abstract

The data assimilation process of adjusting variables in a reservoir simulation model to honor observations of field data is known as history matching and has been extensively studied for a few decades. However, limited success has been achieved due to the high complexity of the problem and the large computational effort required in the real fields. Successful applications of the ensemble Kalman filter (EnKF) to reservoir history matching have been reported in various publications. The EnKF is a sequential method: once new data are available, only these data are used to update all the unknown reservoir properties while previous geological information is unused directly. In this method, multiple reservoir models rather than one single model are implemented, and each model is called a member. Conventionally, the impact of each member on the updating is equally treated. Another approach is the weighted EnKF. During the updating, the method weighs the contribution of each member through the comparison between the simulation response and the measurements. Better matching performance has been found in the weighted EnKF than in the conventional EnKF. To improve computational efficiency, two-level high-performance computing for reservoir history matching process is implemented in this research, distributing ensemble members simultaneously while simulating each member in a parallel style.An automatic history-matching module based on the weighted EnKF and highperformance computing is developed and validated through a synthetic case operating from primary, waterflooding to flooding of water alternating with gas. The study shows that the weighted EnKF improves the matching results, and the high-performance computing process significantly reduces the history matching execution time.

Estimation of Unsaturated Soil Hydraulic Parameters Using the Ensemble Kalman Filter

“…Parameters can be estimated using the EnKF by augmenting the state vector with the parameters to be estimated. Such an approach is relatively simple and easy to implement and has been verified to be effective for modeling in several areas: climate and ocean (e.g., Kivman, 2003; Annan et al, 2005), multiphase flow (e.g., Lorentzen et al, 2003), hydrology and geology (e.g., Chen and Zhang, 2006; Wang et al, 2009), and petroleum engineering (e.g., Gu and Oliver, 2005; Liu and Oliver, 2005; Lorentzen et al, 2005; Evensen et al, 2007; Liang et al, 2009). In vadose zone hydrology, a thorough study of the EnKF with an augmentation technique is needed.…”

A mask-state algorithm to accelerate volcanic ash data assimilation

“…where L f represents the ensemble perturbation matrix. In this study, the forecast step is performed in parallel because of the natural/common parallelism of the independent ensemble propagation, which is a trivial approach when employing ensemblebased data assimilation (Liang et al, 2009;Tavakoli et al, 2013;Khairullah et al, 2013).…”

Parallel Implementation of the Deterministic Ensemble Kalman Filter for Reservoir History Matching

“…The grid sizes in the x-and y-direction are both 300 ft. The z-direction grid size is 20,15,26,15,16,14,8,8,18,12,19,18,20,50, and 100 ft from top to bottom. It has heterogeneous absolute permeability.…”

“…As for the reservoir history matching, the parallel technique has not been well developed. An automatic history matching module with distributed and parallel computing was done by Liang et al using weigthed EnKF [14], where two-level high-performance computing is implemented, distributing ensemble members simultaneously by submitting all the simulation jobs at the same time and simulating each ensemble member in parallel. A parallel framework was given by Tavakoli et al [15] using the EnkF and ensemble smoother (ES) methods based on the simulator IPARS, in which a forecast step was parallelized while an analysis step was computed by one central processor.…”