Year: 2010

In this research we develop efficient parameterization algorithms for history matching based on the principal right singular vectors of the dimensionless sensitivity matrix corresponding the maximum a posteriori estimate of reservoir model parameter. The necessary singular vectors can be computed with the Lanczos algorithm without explicit computation of the sensitivities. We provide a theoretical argument…Continue Reading History Matching With Parameterization Based on the SVD of a Dimensionless Sensitivity Matrix

Because of its ease of implementation, computational efficiency and the fact that it generates multiple history-matched models, which conceptually allows one to characterize the uncertainty in reservoir description and future performance predictions, the ensemble Kalman filter (EnKF) provides a highly attractive technique for history matching production data. Despite these attractive features, EnKF had not been…Continue Reading Covariance Localization for Ensemble Kalman Filter

The ensemble Kalman filter (EnKF) has become a popular method for history matching production and seismic data in petroleum reservoir models. However, it is well known that EnKF may fail to give acceptable data matches especially for highly nonlinear problems. This reseach project focus on the development of iterative forms of EnKF and ensemble smoother…Continue Reading Iterative forms of EnKF and Ensemble Smoother (ES)

The ensemble Kalman filter (EnKF) has recently become a popular history-matching tool largely because of its computational efficiency and ease of implementation. While EnKF has improved previous history matches obtained manually in several field cases, and often appears to give reasonable results for realistic synthetic history-matching problems, one cannot theoretically establish that EnKF samples correctly…Continue Reading Combining Ensemble Kalman Filter and Markov Chain Monte Carlo

In this reseach project, we investigate the use of ensemble Kalman filter (EnKF) for production data assimilation with discrete cosine transform (DCT) and kernel principle component analysis (KPCA) parameterization for channelized reservoirs, where the prior geostatistical model is based on multi-point statistics. In the discrete consine transform (DCT), any 2-D image (or reservoir property fields)…Continue Reading EnKF Data Assimilation with Kernel PCA and Discrete Cosine Transform Parameterizations

The production optimization step of the “closed-loop” reservoir management is an optimal well control problem determining optimal operating conditions to maximize hydrocarbon extraction or net present value (NPV) for the remaining expected life of a reservoir. It is an important step of the “closed-loop” reservoir management, in which rstly the geological models are calibrated with…Continue Reading Production Optimization Under Linear and Nonlinear Constraints

For large scale production optimization problem, gradient based algorithms seem to be the main feasible approach. However, such algorithms have not been widely performed in practice because of the following shortcomings:(i) it is very complicated to commutate the gradient of the objective function by the adjoint method which requires explicit knowledge of the simulator numeric…Continue Reading Derivative-free Production Optimization Algorithms

Determination of optimal location for the injection (and production) wells is a critical step in reservoir development plan in order to produce higher hydrocarbon at lower cost. Finding optimal well locations is an optimization problem on discrete variables (well gridblock indices) and is normally done with a non-gradient based method such as genetic algorithm. A…Continue Reading Optimal Well Placement

This reseach project investigates the application of the ensemble Kalman filter (EnKF) to the integration of well test data into heterogeneous reservoir models generated from geological and geophysical data. EnKF does not require computing the gradient of an objective function, and hence can be applied easily with any reservoir simulator, and more importantly, is far…Continue Reading Well Test Data Assimilation Using EnKF

This study presents a modified simultaneous perturbation stochastic approximation (SPSA) algorithm for automatic history matching. The new SPSA algorithm simultaneously perturbs the reservoir model by using unconditional realizations to generate a stochastic search direction. This search direction is always downhill and the expectation of the stochastic search direction is the Newton direction with CM as…Continue Reading Uncertainty Quantification of Reservoir Performance Predictions Using a Stochastic Optimization Algorithm