Integrated evaluation of structural uncertainty using history matching from seismic imaging uncertainty model

Structural uncertainty is the first-order uncertainty in reservoir modeling both in terms of (1) the difficulty of structural interpretation due to limited data acquisition/quality and (2) the magnitude of its impact on uncertainty in hydrocarbon in-place and fluid flow performance. Though it is a common practice to consider structural uncertainty in the evaluation of hydrocarbon in-place in the appraisal phase of field developments, structural uncertainty is rarely considered when evaluating future production performance in the production phase. The reason is mainly the difficulty in integrating dynamic data into structural uncertainty modeling. This research aims at developing a method/workflow for structural uncertainty modeling which integrates geophysical and geological data during the process of history matching. This dissertation first proposes a semiautomatic seismic interpretation method using geostatistical pattern simulation. From the resulting multiple structural interpretations, a prior stochastic structural model is built and used as a geological/geophysical constraint for the subsequent history matching. Next, a new history matching method is proposed: numerical models are inverted from dynamic fluid flow response while honoring the previous structural model. The method consists of searching for numerical models that match production history from a large set of prior structural model realizations. To make such a search effective, a parameter space defined with a "similarity distance" is introduced. The inverse solutions are found in this space using a stochastic search method. Synthetic but realistic reservoir examples are presented to demonstrate the proposed workflow/methods.