Introduction to Geological Uncertainty Management in Reservoir Characterization and Optimization
Robust Optimization and History Matching
Article
Choosing a representative subset of realizations can reduce significantly the number of simulations and the computational cost associated with optimization under geological uncertainty. Methods that use dynami...
Chapter
Almost all activities in real life entail different kinds of uncertainty. From daily decisions to complicated problems, such as petroleum reservoir characterization, suffer from uncertainties. Uncertainty can ...
Book
Robust Optimization and History Matching
Chapter
Our knowledge from underground reservoirs is not complete and is limited to some sparse core and log data, seismic data, geological interpretations, etc. This limited knowledge leads to a significant extend of...
Chapter
As discussed in Sect. 3.4, one of the challenges in history matching is the high dimensionality (large number of model parameters) of the reservoir model realizations th...
Chapter
As mentioned earlier, one of the challenges in history matching and field development optimization under geological uncertainty is the high computational cost of the process. The majority of the computational ...
Chapter
Whenever there are observed dynamic data obtained from the reservoir understudy, we can reduce the geological uncertainty by conditioning the prior geological realizations to the observed data (Oliver and Chen...
Chapter
Decision making about field development plans has to consider the inherent uncertainties of sub-surface hydrocarbon reservoirs; therefore, the decisions would be stable under different geological scenarios. As...