Prediction and reliability analysis of reservoir lithology spatial distribution

Abstract

Reliable lithology spatial distribution directly reflects the geological situation of the reservoir, which is the basis of stratigraphic correlation, sedimentary modeling, and other geological research. Under the condition of limited reservoir data, it is a challenging task to accurately depict the lithology spatial distribution and provide a quantitative reliability analysis of the results. In this study, we propose a flexible spatial distribution prediction and model reliability analysis method. Firstly, the method develops a spatially dependent deep Kriging technology to fit the heterogeneous characteristics of the reservoir lithology, and adopts the extracted spatial key information and related reservoir attributes to invert lithology spatial distribution intelligently. Then, it focuses on the real-time assimilation of non-Gaussian data in the reliability modeling and quantitatively analyzes the reliability of the prediction system under the non-Gaussian hypothesis. Finally, the method is applied to the actual heterogeneous reservoir, good results are achieved in the prediction accuracy, model fitting degree, model reliability, and time performance compared with other methods. The method is conducive to finding future mineral deposits locations and reducing exploration costs.