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 …

In the last decade, ensemble-based methods have been widely investigated and applied for
data assimilation of flow problems associated with atmospheric physics and petroleum …

The use of the ensemble smoother (ES) instead of the ensemble Kalman filter increases the
nonlinearity of the update step during data assimilation and the need for iterative …

The study presented here is directed to accumulate the body of knowledge which is up to
now built around the techniques of Evolutionary Computation in the Oil Industry, particularly …

Abstract The ensemble Kalman filter (EnKF) has become a popular method for history
matching production and seismic data in petroleum reservoir models. However, it is known …

This paper presents a comparison study in which several partners have applied methods to
quantify uncertainty on production forecasts for reservoir models conditioned to both static …

The key ingredients to successful real-time reservoir management, also known as a “closed-
loop” approach, include efficient optimization and model-updating (history-matching) …

The application of the ensemble Kalman filter (EnKF) for history matching petroleum
reservoir models has been the subject of intense investigation during the past 10 years …

Adjoint equations for 3D, three-phase flow problems are developed and implemented to
calculate the sensitivity of production data to permeability fields and well skin factors …

Deep‐learning has achieved good performance and demonstrated great potential for
solving forward and inverse problems. In this work, two categories of innovative deep …