Curated by Nora Hamidaddin, Associate Editor SandRose Magazine
In this section, we curate a number of recommendations for technical papers from subject matter experts on topics relating to their respective disciplines.
EOR-Enhanced Oil Recovery
Experimental Study of Crude Oil Emulsion Stability by Surfactant and Nanoparticles
Recommended by Zainab Al-Abdulmohsen, DCF – Technical Specialist, Baker Hughes
Almohsin, A. M.; Alabdulmohsen, Z.; Bai. B.; Neogi, P. 2018. Paper SPE-190440-MS presented at the SPE EOR Conference in Oman, 2018.
Nanoparticle-stabilized emulsions have attracted many researchers’ attention in recent years due to many of their specific characteristics and advantages over conventional emulsions stabilized by surfactants or by nanoparticles. For example, the solid nanoparticles can be irreversibly attached to the oil-water interface and form a rigid nanoparticle monolayer on the droplet surfaces, which induces highly stable emulsions. Those emulsions can withstand harsh conditions. Compared to colloidal particles, nanoparticles are one hundred times smaller, and emulsions stabilized by nanoparticles can travel a long distance in reservoirs without much retention.
Cement Evaluation, Logging
Cement Sheath Evaluation
Recommended by Hussein Al-Shabebi, Well Integrity & Production Logging Team Lead, Baker Hughes
Document API-TR-10TR1 published in American Petroleum Institute, 2008.
This API Technical Report discusses the cement sheath evaluation. It focuses on logging and evaluation, presenting both the technology and the application from the end user’s point of view. Great effort was made to ensure that new technical developments are incorporated, and different views and perspectives are represented. It is useful to field and Geoscience personnel as it includes sections on tools capability, selection, running procedure and log QC. It discusses how to best incorporate log interpretation and cementing data into an overall determination of zonal isolation, and it attempts to remedy logs misunderstanding.
Reservoir Simulation and Description, Machine and Deep Learning, Artificial Intelligence
“Reinforcement Learning” in Reservoir Simulation and Field Development Optimization
Recommended by Thamer Sulaimani, Petroleum Engineer, Saudi Aramco; and Marko Maucec, Petroleum Engineer, Saudi Aramco
Nasir Y, He J, Hu C, Tanaka S, Wang K and Wen X (2021). Deep Reinforcement Learning for Constrained Field Development Optimization in Subsurface Two-phase Flow. Frontiers in Applied Mathematics and Statistics Journal. Vol. 7. https://doi.org/10.3389/fams.2021.689934
This paper is an excellent example of the promising applications of AI in the oil and gas industry. Although limited to two-phase flow, the paper sets the ground for the reservoir simulation practitioners who are interested in applying reinforcement learning (action-reward loop) to field development optimization. The authors present a deep reinforcement learning-based AI agent that generates optimized development scenarios given only basic description of the reservoir with minimal computational cost. The developed method incorporates rock and fluid properties, is applicable in 2D and 3D domains, outperforms traditional population-based optimization algorithms, and can be generalized to account for geological uncertainty.
Reservoir Description, Reservoir Simulation, Dimensional Analysis
Pitfalls of 3D Saturation Modelling in the Middle East and Importance of Dimensional Analysis
Recommended by Lautaro Rayo, Petroleum Engineer, Saudi Aramco;
O’Meara, D. “Pitfalls of 3D Saturation Modelling in the Middle East.” Paper presented at the SPE Reservoir Characterization and Simulation Conference and Exhibition, Abu Dhabi, UAE, September 2019. doi: https://doi.org/10.2118/196634-MS
The author Daniel O’Meara gives a refreshing look at “Saturation-Height” modeling – very chilling that you will want to stop calling it that way. The paper is a renewed attempt at convincing Oil and Gas professionals about the beauty and usefulness of Dimensional Analysis, a methodology which constitutes the bedrock of empirical physics and engineering, but that is so often forgotten in papers, books, and peer review meetings. After reading this article, you will better understand how physical models describe data, with wide applications in Engineering and Machine Learning.
