Jonggeun Choe

Ensemble Kalman filter (EnKF) has been researched for reservoir characterization in petroleum engineering. However, the repeated assimilation causes lots of simulation cost. Ensemble smoother (ES) assimilates all available data once. It has advantages over EnKF: efficiency and simplicity. The two ensemble methods are based on the same assumptions: Gaussian distribution and trust in the mean of all ensembles. Many researchers have pointed out that EnKF gives overshooting and filter divergence pro

Ensemble Kalman filter (EnKF) has the limitation of applications for multi-point geostatistics because it assumes Gaussian random field. It also uses all ensembles to get covariance matrix, even though they have different permeability field each other, resulting in filter divergence. The proposed method suggests the concept of clustered covariance by grouping initial ensembles using a distance-based method. Hausdorff distance is used for calculating similarity between permeability fields and the

Summary Riserless drilling is an unconventional technique using a relatively small diameter pipe as a mud return line from the sea floor instead of a large diameter marine riser. The schemes were developed in the late 1960s to reduce wear on blowout presenters and to make drill pipe re-entry easier by balancing internal and external subsea well pressures. However, these concepts were not implemented at that time because water depths were shallow, and technology was not available. In the Gulf of

History matching is essential for estimating reservoir performances and decision makings. Ensemble Kalman filter (EnKF) has been researched for inverse modeling due to lots of advantages such as uncertainty quantification, real-time updating, and easy coupling with any forward simulator. However, it requires lots of forward simulations due to recursive update. Although ensemble smoother (ES) is much faster than EnKF, it is more vulnerable to overshooting and filter divergence problems. In this r

Ensemble Kalman filter (EnKF) has been widely studied due to its excellent recursive data processing, dependable uncertainty quantification, and real-time update. However, many previous works have shown poor characterization results on channel reservoirs with non-Gaussian permeability distribution, which do not satisfy the Gaussian assumption of EnKF algorithm. To meet the assumption, normal score transformation can be applied to ensemble parameters. Even though this preserves initial permeabili

Ensemble-based analyses are useful to compare equiprobable scenarios of the reservoir models. However, they require a large suite of reservoir models to cover high uncertainty in heterogeneous and complex reservoir models. For stable convergence in ensemble Kalman filter (EnKF), increasing ensemble size can be one of the solutions, but it causes high computational cost in large-scale reservoir systems. In this paper, we propose a preprocessing of good initial model selection to reduce the ensemb

Oil sands have great amount of reserves in the world with increasing commercial productions. Prediction of reservoir performances of oil sands is challenging mainly due to long simulation time for modeling heat and fluids flows in steam assisted gravity drainage (SAGD) operations. Because of accurate modeling difficulties and limited geophysical data, it requires many simulation cases of geostatistically generated fields to cover uncertainty in reservoir modeling. Therefore, it is imperative to

Summary For economic and technical reasons, the industry has used directional, extended-reach, horizontal, and multilateral wells. Although technologies are well developed for these wells, and there are numerous successes in the last decade, these wells still have high level of risk in drilling and completion. Well control is one of the relatively unanswered, but important, operations because improper well control, followed by a blowout is one of the most expensive and feared operational hazards

Ensemble Kalman filter (EnKF) is one of the widely used optimization methods in petroleum engineering. It uses multiple reservoir models, known as ensemble, for quantifying uncertainty ranges, and model parameters are updated using observation data repetitively. However, it requires a large number of ensemble members to get stable results, causing huge simulation time. In this study, we propose a sampling method using principal component analysis (PCA) and K-means clustering. It excludes poor en