Selecting optimal cases for uncertainty quantification and history matching

Over the last few years increasing interest has been focused on workflows for uncertainty assessment in reservoir management. Structured approaches exist for assessing the impacts of uncertainty on investment decision-making in the oil and gas industry. These approaches mostly rely on simplified component models for each decision domain, e.g. G&G models, production scenarios, drilling models, processing facilities, economics and related costs. etc. Because of its complexity, the integration of dynamical modeling is only gradually entering this domain of decision-making processes. It is generally accepted that any model reliably predicting future quantities as much as reserve estimates should be able to reproduce known history data. This requires a model validation process called History Matching - which is traditionally cumbersome and time consuming. A repetition for alternative geo-models as part of a comprehensive uncertainty assessment was almost not feasible due to sequential workflows. Today, the application of optimization techniques and distributed computing facilities allow faster turn around times. In this work, a fiamework for assisted History Matching and Uncertainty Quantification (MEPO) is used to structure and speed up the calibration process with a high degree of reproducihility. A selection procedure is introduced to define a number of alternative geological models as an input to the history matching process. Optimization methods are applied for calibration purposes and the impact of multiple full field simulation models on reserve estimates of gas reservoir is investigated.