Complex Geology Estimation Using the Iterative Adaptive Gaussian Mixture (IAGM)
B. Sebacher, A. Stordal and R.G. Hanea
Event name: ECMOR XIV - 14th European Conference on the Mathematics of Oil Recovery
Session: Ensemble Methods
Publication date: 08 September 2014
Info: Extended abstract, PDF ( 1.2Mb )
Price: € 20
In the past years the multi-point geostatistical (MPS) simulation geo-models have been used successfully, creating realistic geological instances(facies fields). However, the estimation of such geological deposits it is still a challenge, especially when an Assisted History Matching (AHM) model based on Bayesian inversion is used. This is hampered when we deal with complex geometry and topology and when the number of the facies types is greater than two. In the MPS context of facies fields generation, the facies fields estimation is carried out combining two components. One is the facies field parametrization and the other is the AHM method used. In this study we present a parametrization of the facies fields, carried out in a multidimensional normalized space by drawing from a marginal conditional Gaussian distribution. This is a generalization of a parametrization used in a previous study for channelized reservoirs and can be used for any type of geological layouts that comes out from the prior (in the MPS case, the training image). The parametrization ensures that the updates are always facies realizations with the same topological characteristics as the prior. However, traditional history matching methods tend to either destroy this topological structure or collapse into a single realization giving unrealistic description of the uncertainty. To solve this issue the iterative adaptive Gaussian mixture for a small tuning parameter has been used as AHM method with maximum three iterations. The method is tested for a 2D reservoir where four facies types are present, of which one exhibits channelized geometry. The topology is complex because two facies types are not in direct contact, information provided by the training image. After assimilation of the production data the IAGM was able to reduce the prior uncertainty towards an ensemble with realistic geological structure with good estimations and predictions.