Hybridization of optimized supervised machine learning algorithms for effective lithology

E Aniyom, A Chikwe, J Odo - SPE Nigeria Annual International …, 2022 - onepetro.org
Lithology identification is an important aspect in reservoir characterization with one of its
main purpose of well planning and drilling activities. A faster and more effective lithology …

Application of genetic algorithm on data driven models for optimized ROP prediction

D Duru, A Kerunwa, J Odo - SPE Nigeria Annual International …, 2022 - onepetro.org
The demand for cost-effective drilling operations in oil and gas exploration is ever growing.
One of the important aspects to tackling the aforementioned difficulty is determining the …

Hybridized probabilistic machine learning ranking system for lithological identification in geothermal resources

P Ekeopara, J Odo, B Obah, V Nwankwo - SPE Nigeria Annual …, 2022 - onepetro.org
Geothermal resources are characterized by hard rocks with very high temperatures making it
difficult to implement conventional tools for petrophysical analysis such as lithological …

[PDF][PDF] Study of Natural Gas Compressibility Factor (Z) to Improve Experimental Data Prediction Using Peng-Robinson State Equation.

HM Sidrouhou, O Bacha, M Korichi… - Petroleum & …, 2023 - researchgate.net
Among the essential thermodynamic parameters of behavior, analysis (PVT) in natural gas
engineering is the gas compressibility factor (Z). In this work, we improve the results of the …

[引用][C] Machine Learning Approach to Generate Synthetic Sonic Logs in Geothermal Wells

C Vivas, S Salehi - Geothermal Rising Conference, San Diego, California, 2021