The Study Of Braided River Reservoir Geological Modeling Constrained By Seismic Data

Geological modeling technology is a research hot spot at home and abroad in recent years, and occupies a very important position in the field of reservoir description, is also an important part. Traditional variogram-based geostatistics conditional stochastic simulation is a routine tool in reservoir modeling to model reservoir heterogeneities and assess the spatial uncertainties. However, complex patterns are typically poorly reproduced by traditional geostatistics. Multiple-point geostatistics approach based on training images therefore was introduced to solve this problem. Introducing geological prior knowledge or conceptual models into reservoir modeling through training images is a breakthrough contribution of multiple-point geostatistics to reservoir modeling.The normal reservoir modeling method only use well log data, Such modeling results is difficult to reflect the faithful reservoir heterogeneity, structural and uncertainty. so the modeling process seismic data for seismic constrained reservoir geological modeling. This will play to their superiority, the final reservoir model can meet the actual geological conditions.This paper uses the multi-point geo-statistical algorithm simulating the sedimentary micro facies and analysis the uncertainty of the simulated results and the uncertainty caused by seed. When using the logging data modeling,the simulated results of two training image can be compared. When using the seismic data constraint modeling, the uncertainty in simulation result of two sets of training images were analyzed and the effects of different Permanence of Updating Ratios and seeds can be analyzed. Finally, through the new drilled horizontal 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 in the modeling based on seismic data, different random seeds and the control level of seismic data cannot result make the simulated results have a big difference. This phenomenon proves that seismic data constraint of reliability and validity in the modeling. statistics modeling.