Improved Geological Modeling and Dynamic Data Integration Using the Probability Perturbation Approach

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2006-05

Authors

Barrera, Alvaro Enrique

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Abstract

The lack of geological information to appropriately reproduce the flow paths in typical reservoir modeling scenarios is one of the most significant sources of uncertainty and, consequently, it has been repeatedly addressed in multiple geological modeling approaches. However, current modeling techniques that combine geological and dynamic information are computationally extensive and frequently lead to geologically inconsistent models. Practitioners and particularly geologists have found the traditional geostatistics, based on the variogram and kriging, suitable to describe geological heterogeneity within a single facies, but too limiting for describing more organized geological features such as channels, fractures, and facies distributions among others. These geological features usually have the largest impact on the flow response, calling for a different approach using multiple-point statistics instead of variogram models to capture the required conditional information to generate more accurate models that exhibit more structural organization. Honoring the geological model is an important objective during the generation of static geological models; however, it is commonly forgotten during the integration of dynamic information. Reconciliation between the model predictions and the field records represents a rather significant challenge, considering the highly non-linear relationship between the model parameters and the production response. Reproduction of the geological heterogeneity during the calibration of the model response with the production history is the motivation behind the selection of a probabilistic approach for dynamic data integration. This work focuses on a probabilistic approach to integrate dynamic data that ensures consistency between reservoir models developed from one stage to the next. The algorithm relies on efficient parameterization of the dynamic data integration problem and permits rapid assessment of the updated reservoir model at each stage. This report is presented as part of the requirements to obtain a Master of Science degree in petroleum engineering under the Fast Track Option. It summarizes the proposal and preliminary results related with the Ph.D. dissertation that is currently on going.

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