Application of Supervised Machine Learning in Porosity Prediction from Seismic Data and Uncertainty Quantification

Seismic quantitative interpretation in oil and gas exploration benefits significantly from the integration of machine learning (ML) techniques, as highlighted in this study. By leveraging ML, the research focuses on optimizing the selection of elastic attributes derived from seismic data to predict porosity in reservoirs accurately and efficiently. Attributes such as P-Impedance, S-Impedance, Lamda-rho, Mau-rho, Poisson impedance_fluid, and Poisson impedance_Litho are systematically evaluated against porosity data from well logs to establish robust correlations using various ML algorithms.

The study emphasizes the automation of attribute selection, which traditionally is labor-intensive and prone to uncertainties. Through ML, the process becomes streamlined, enhancing the accuracy of porosity mapping and overall reservoir characterization. Moreover, the research addresses the challenge of uncertainty quantification by demonstrating high correlations between predicted and observed porosity values across training and validation wells. This validation underscores the reliability and generalizability of the ML models developed.

Ultimately, the application of ML techniques in seismic data interpretation offers substantial improvements in efficiency and reliability. By reducing uncertainties and providing rigorous uncertainty quantification, this approach enhances decision-making in the oil and gas industry, contributing to more informed reservoir modeling and exploration strategies.