Growthsim - A multiple point framework for pattern simulation

Accurate characterization of underground oil reservoirs is an essential prerequisite to the design of EOR scenarios. Specifically, in reservoir characterization, integrating static and dynamic data into reservoir models to construct realistic models has received considerable attention. Most of the conventional geostatistical approaches of integrating data into reservoir models are based on the variograms, which describe the spatial continuity using a linear template. This paper presents a complete frame work for integrating data into reservoir models based on the non-linear spatial continuity information. The proposed method is more effective in reproducing non-linear patterns. The complex patterns exhibited by geology can be represented in the form of multiple point statistics such as a multiple point histogram. The algorithm starts with extracting mp statistics from training images using an optimal spatial template. After collecting different patterns and building the mp histogram, the pattern reproduction process commences. It begins from data locations (simulatable nodes) and then grows to fill the whole reservoir domain. Simulation is also capable of incorporating proportional maps (for non-stationarity) and dynamic-data within a growth-based frame work. Several synthetic models and model constructed using real data exhibit good agreement between reproduced patterns and given information.