Uncertainty Evaluation Based on Bayesian Transformations: Taking Facies Proportion as An Example

Abstract

Many input parameters in reservoir modeling cannot be uniquely determined due to the incompleteness of data and the heterogeneity of the reservoir. Sedimentary facies modeling is a crucial part of reservoir modeling. The facies proportion is an important parameter affecting the modeling results, because that proportion directly determines the net gross ratio, reserves and sandbody connectivity. An uncertainty evaluation method based on Bayesian transformation is proposed to reduce the uncertainty of the facies proportion. According to the existing data and geological knowledge, the most probable value of the facies ratio and the prior distribution of uncertainty are estimated. The prior distribution of the facies proportion is divided into several intervals, and the proportions contained in each interval are used in facies modeling. Then, spatial resampling is carried out for each realization to obtain the likelihood estimation of the facies proportion. Finally, the posterior distribution of the facies ratio is achieved based on Bayesian transformation. The case study shows that the uncertainty interval of sandstone proportion in the study area has been reduced from [0.31, 0.59] to [0.35, 0.55], with a range reduction of 29%, indicating that the updated posterior distribution reduces the uncertainty of reservoir lithofacies proportion, thereby reducing the uncertainty of modeling results.