Research The Uncertainty In Reservoir Modeling

Uncertainty is a core problem in reservoir modeling study. In the Cares Jef’ smonograph “Modeling uncertainty in the earth science” worked in the Reservoir PredictionCenter at Stanford University, uncertainty in the oil and gas and earth sciences are discussed:uncertainty is caused by an incomplete understanding about what we like to quantify. In thecase of imperfect existing data and different research methods, reservoir modeling resultsexist great uncertainty, and cannot reflect the reservoir geological condition comprehensively.Uncertainty in reservoir modeling can be reduced with additional data or a more completeunderstanding of the physical process or linkage of processes.This paper uses the multi-point geo-statistical algorithm simulating the sedimentarymicro facies and analysis the uncertainty of the simulated results and the uncertainty causedby seed. When using the logging data modeling，the simulated results of two training imagecan be compared. When using the seismic data constraint modeling, the uncertainty insimulation result of two sets of training images were analyzed and the effects of differentPermanence of Updating Ratios and seeds can be analyzed. Finally, through the new drilledhorizontal wells the uncertainty of the simulation results were validatedWe find that if only use the logging data, because of the limitation of the logging data,the zone between the different wells will out of control. The uncertainty is very high. But inthe modeling based on seismic data, different random seeds and the control level of seismicdata cannot result make the simulated results have a big difference. This phenomenon provesthat seismic data constraint in the modeling can reduce the uncertainty tremendously. Newdrilling horizontal wells also shows the simulation results restraint by seismic data isreasonable and the level of uncertainty is smaller.