DATA - Characterization, Drilling and Completions Analytics

Wednesday, 13 March

1000 - 1130

PALOMINO F-H

Technical_and_Invited_Presenter

In this session, we present a diverse range of topics in the realm of Characterization and Drilling Completion Analytics, offering insights into cutting-edge techniques and methodologies:
Explore the prediction of mineralogical composition in unconventional reservoirs, comparing data-driven and chemistry-based models.
Discover the application of physics-informed neural networks for CH4/CO2 adsorption characterization and learn about implementing continuous feedback loops for autonomous drilling, optimizing operations between surface and downhole.

Chairperson

Neha Sharma - Strathcona Resources

Raz Binesh - Spartan Controls

1000-1030 218116
Prediction of Mineralogical Composition in Heterogeneous Unconventional Reservoirs: Comparisons between Data-driven and Chemistry-Based Models

Z. Yang, A. Ghanizadeh, A. Younis, C. Clarkson, University of Calgary
1030-1100 218117
Continuous Feedback Loop between Surface and Downhole for Autonomously Drilling a Section

Q. Liu, S. Ba, P. Goel, J. Kim, SLB
1100-1130
Towards Optimized Completion: A Data-Driven Proxy for WCSB Wells

H. Dehghanpour, T. Moussa, University of Alberta
Alternate 218029
Physics-Informed Neural Network for CH4/CO2 Adsorption Characterization

H. WANG, S. Chen, M. Wang, University of Calgary; Z. Wu, China University of Geosciences (Wuhan); G. Hui, China University of Petroleum (Beijing)

2024 Technical Program

DATA - Characterization, Drilling and Completions Analytics