Use of geological prior information in reservoir facies modelling

Geological prior information is one of the ways of bringing geological realism into reservoir facies models. Models based on geologically realistic priors are more accurate and provide more robust predictions under uncertainty. Commonly geological prior information is captured from modern depositional environments or outcrops of ancient deposits. The aim of this work was to demonstrate how use of prior information improves the models of fluvial facies and reduces the uncertainty associated with the estimation of channel geometry. Geological priors are built using intelligent techniques like Artificial Neural Networks and Support
Vector Regression. The data driven methods reveal the hidden relationships among the variables that form the priors and also allow handling the associated uncertainty. Furthermore, we used the intelligent prior models to predict realistic parameter combinations which may not have been observed in the available data but are still plausible and may exist in nature. The intelligent prior models were combined with multiple point statistics (MPS) simulation for a test synthetic reservoir case study. Multiple point statistics (MPS) was chosen to model channel
facies because of its capability of modelling realistic geobodies and adaptability to well and seismic data. The study shows improvement in controlling simulated channel geometry and highlighting the impact on volume estimation and oil production.