Enhancing Model Consistency in Ensemble-Based History Matching
- Chris Carpenter (JPT Technology Editor)
- Document ID
- Society of Petroleum Engineers
- Journal of Petroleum Technology
- Publication Date
- April 2018
- Document Type
- Journal Paper
- 93 - 95
- 2017. Society of Petroleum Engineers
- 12 in the last 30 days
- 66 since 2007
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This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 186049, “Enhancing the Geological Models Consistency in Ensemble-Based History Matching: An Integrated Approach,” by A. Perrone, F. Pennadoro, A. Tiani, and E. Della Rossa, Eni, and J. Saetrom, Resoptima, prepared for the 2017 SPE Reservoir Characterization and Simulation Conference and Exhibition, Abu Dhabi, 8–10 May. The paper has not been peer reviewed.
The aim of this work is to present the effectiveness of a fully integrated approach for ensemble-based history matching on a complex real-field application. The predictive ability of the ensemble of models is greatly enhanced through an integrated work flow promoting collaboration between all subsurface disciplines. One key feature of the ensemble-based method that is especially important for complex reservoirs is that it overcomes the typical limitation of the traditional approaches where the number of uncertainty parameters resulting from practical or algorithm constraints often has to be reduced.
The distinguishing feature of ensemble-based methodologies is their capability of integrating multiple sources of data while quantifying and propagating the uncertainty in reservoir-model parameters. In order to achieve a proper uncertainty assessment, one must couple the ensemble-based data-assimilation technique with an integrated work flow covering the entire reservoir-modeling process. The core of this methodology is an iterative data-assimilation process, which incorporates the reservoir-modeling steps in order to condition the ensemble of model realizations to the available data.
The integrated reservoir-modeling process is used to generate an initial guess for the reservoir models, representing prior knowledge of the reservoir uncertainties. Throughout this process, the relevant model uncertainties must be properly identified and quantified. It is worth noting that significant differences among models may arise from different geological hypotheses or scenarios and from the random component typically introduced by geostatistical methods.
In the complete paper, the authors use the ensemble-smoother-with-multiple-data-assimilation (ES-MDA) algorithm to condition the ensemble of reservoir models to the historical observations. According to this methodology, the ensemble predictions are used to compute a statistical approximation of the sensitivity to the input (uncertain) variables, which are modified in a predefined number of iterations to reduce the mismatch between the simulated and measured dynamic data. The methodology requires the definition of an error associated with each observed source (e.g., field, wells) and data type (e.g., rates, pressures). The ensemble approach allows for a grid-based parameterization, which, driven by the ensemble correlations, provides the ability to modify the value of a variable at some specific location without inappropriately forcing a change to other variables or locations. Consequently, all uncertain model parameters are retained because they may still have a relevant effect on the production forecasts.
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