Reservoir Characterizations by Deep-Learning Model: Detection of True Sand Thickness

TL;DR

This paper presents a deep-learning approach that accurately detects true sand thickness from seismic data, overcoming traditional limitations and improving lithological interpretation in reservoir characterization.

Contribution

The study introduces a novel deep-learning model integrated with synthetic wedge models for precise sand thickness detection from low-resolution seismic data.

Findings

Robustness validated through wedge models and field data

Significant improvement over traditional methods in accuracy

Elimination of over- and under-estimation risks

Abstract

It is an extremely challenging task to precisely identify the reservoir characteristics directly from seismic data due to its inherit nature. Here, we successfully design a deep-learning model integrated with synthetic wedge models to overcome the geophysical limitation while performing the interpretation of thickness of sand bodies from a low resolution of seismic data. Through understanding and learning the geophysical relationship between seismic responses and corresponding indicators for sand thickness, the deep-learning model could automatically detect the locations of top and base of sand bodies identified from seismic traces by precisely revealing the lithology distribution. Quantitative analysis and extensive validations from wedge models and field data prove the robustness of the proposed methodologies. The true sand thickness, identified from the deep-learning model, provides an extremely useful guidance in enhancing the interpretation of lithological and stratigraphic information from seismic data. In addition, the proposed deep-learning approach eliminates the risks of over- and under-estimation of net-to-gross with a significant improvement with respect to the accuracy.