Emerging Technologies in Formation Evaluation: Machine Learning and Geological Storage of CO2

Wednesday, 18 October

0830 - 1130

Room 214 D

Technical Session

This session focuses on emerging technologies in formation evaluation, including machine learning and geological storage of CO2.

Session Chairperson(s)

0830-0855 214963
Machine Learning Assisted Petrophysical And Geochemical Reservoir Description Integrating Multi-scale Well Data

C. Xu, Aramco Americas; Y. Jin, Aramco Research Center; T. Lin, Aramco Services Company; W. Li, Aramco Americas; Y. Alzayer, Z. Ibrahim, Saudi Aramco
0855-0920 214926
Advanced Characterization of the Jansz-io Field Using AI-Assisted NMR Workflows

M. Waugh, Chevron Australia Pty Ltd; M. Qleibo, X. Yang, V. Jain, B.C. Cheong, M. Hakimi Bin Daud, SLB
0920-0945 214950
Limitations And Fallacies of Carbon Capture and Storage (CCS) and Impact on Oil and Gas Production

S. Farouq Ali, M. Soliman, University of Houston
1015-1040 214991
Application Of Artificial Intelligence (AI) Based Algorithm To Predict Contamination During Formation Fluid Sampling

A. Hunnur, S. Smith, E. Hall, Baker Hughes
1040-1105 214964
CO₂ Sequestration in the Arbuckle Group: Flow Simulation in Osage County, Oklahoma

A. Hamzat, B. Milad, R.G. Moghanloo, University of Oklahoma
1105-1130 215113
Underground Gas Storage Capacity Estimation Considering Water Evaporation and Salt Precipitation in Depleted Gas Reservoirs: Method and Case Study in W23 UGS

Y. He, Y. Qiao, Y. Tang, N. Wang, L. Tang, J. Qin, Southwest Petroleum University
Alternate 214782
An Efficient Approach For Automatic Parameter Inversion Based On Deep Reinforcement Learning

D. Li, Z. Chen, China University of Petroleum at Beijing; K. Sepehrnoori, The University of Texas At Austin

2023 Technical Program