Evaluation of Reservoir Porosity and Permeability from Well Log Data Based on an Ensemble Approach: A Comprehensive Study Incorporating Experimental, Simulation, and Fieldwork Data

• Published in: Natural resources research (New York, N.Y.) Vol. 34; no. 1; pp. 383 - 408
• Main Authors: Nyakilla, Edwin E., Guanhua, Sun, Hongliang, Hao, Charles, Grant, Nafouanti, Mouigni B., Ricky, Emanuel X., Silingi, Selemani N., Abelly, Elieneza N., Shanghvi, Eric R., Naqibulla, Safi, Ngata, Mbega R., Kasala, Erasto, Mgimba, Melckzedeck, Abdulmalik, Alaa, Said, Fatna A., Nadege, Mbula N., Kasali, Johnson J., Dan, Li
• Format: Journal Article
• Language: English
• Published: New York Springer Nature B.V 01.02.2025
• Subjects: Artificial neural networks; Back propagation networks; Core analysis; Fieldwork; Gaussian process; geophysical logging; Machine learning; Mathematical models; Mean square errors; Multivariate analysis; Neural networks; normal distribution; Performance evaluation; Permeability; Porosity; prediction; Reservoirs; Support vector machines; Well logs

Permeability and porosity are key parameters in reservoir characterization for understanding hydrocarbon flow behavior. While traditional laboratory core analysis is time-consuming, machine learning has emerged as a valuable tool for more efficient and accurate estimation. This paper proposes an ens...