Computational Intelligence for Deepwater Reservoir Depositional Environments Interpretation

TL;DR

This paper introduces a computational intelligence framework that automates the interpretation of depositional environments in deepwater reservoirs, aiming to improve consistency and reduce labor in stratigraphic analysis.

Contribution

It presents a novel methodology using computational intelligence techniques to automate stratigraphic interpretation of deepwater reservoirs, enhancing efficiency and consistency.

Findings

Finite state transducer models effectively interpret depositional environments.

The methodology reduces stratigrapher workload and variability.

Demonstrated on well log data with promising results.

Abstract

Predicting oil recovery efficiency of a deepwater reservoir is a challenging task. One approach to characterize a deepwater reservoir and to predict its producibility is by analyzing its depositional information. This research proposes a deposition-based stratigraphic interpretation framework for deepwater reservoir characterization. In this framework, one critical task is the identification and labeling of the stratigraphic components in the reservoir, according to their depositional environments. This interpretation process is labor intensive and can produce different results depending on the stratigrapher who performs the analysis. To relieve stratigrapher's workload and to produce more consistent results, we have developed a novel methodology to automate this process using various computational intelligence techniques. Using a well log data set, we demonstrate that the developed methodology and the designed workflow can produce finite state transducer models that interpret deepwater reservoir depositional environments adequately.