IAMCS Workshop in Large-Scale Inverse Problems and Uncertainty Quantification
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- Texas A&M University
- College Station, TX
- Stephen W. Hawking Auditorium
- George P. and Cynthia Woods Mitchell Institute for Fundamental Physics and Astronomy (MIST)
- Marko Maucec, Halliburton/Landmark Graphics Corporation
- Towards Geology-Guided Quantitative Uncertainty Management in Dynamic Model Inversion
Abstract
Optimal Improved Oil Recovery (IOR) depends to a large extent on the ability to estimate volumes and locations of bypassed oil from available historical data. Assisted simulation History Matching techniques are being used to estimate remaining reserves volumes and locations. The talk will address an approach to history match that more accurately captures model uncertainty. The novelty lies in direct interfacing between the DecisionSpace Desktop Earth Modeling software and a forward simulator, with the rapid generation of model updates in wave-number domain. A workflow is described, based on multi-step Bayesian Markov chain Monte Carlo (MCMC) inversion, outlining a method where proxy model is guided by streamline-based sensitivities, dispensing with the need to run forward simulation for every model realization. The method generates an ensemble of sufficiently diverse static model realizations at the high-resolution geological scale by obeying known geostatistics (variograms) and well constraints. Efficient model parameterization and updating, based on Discrete Cosine Transform is described for rapid characterization of the main features of geologic uncertainty space: structural framework, stratigraphic layering, facies distribution and petrophysical properties. The application of the history-matching workflow on a case study combining geological model with ~1M cells, four different depositional environments and 30 wells with 10-year water-flood history is presented. Finally, the ongoing activity will be presented to develop technology for rapid dynamic ranking of history matched models to optimize the business decisions. The main features include dynamic characterization of geological uncertainty by calculating pattern-dissimilarity, deployment of very fast streamline simulations in evaluating dissimilarity, application of multi-dimensional scaling pattern recognition techniques, and assignment of a few representative realizations for full-physics simulation.