The relentless march of computational intelligence continues to redefine the paradigms of petroleum engineering, offering sophisticated solutions to long-standing challenges in subsurface characterization and operational optimization. A compelling triptych of recent research illuminates this trajectory, showcasing the burgeoning capacity of machine learning (ML) to unlock substantial efficiencies and enhance decision-making across the exploration and production lifecycle.
Paper SPE 222299 presents a framework for synthesizing crucial openhole well logs by ingeniously leveraging readily available mud-log and logging-while-drilling data within the complex geological context of the Gulf of Thailand. The authors demonstrate that robust algorithms, particularly random forest and gradient-boosting regressors, can achieve remarkable predictive accuracy. This offers a pragmatic solution to populate data-deficient legacy wells, crucial for informed reservoir management, and promises tangible fiscal benefits by curtailing the necessity for costly conventional logging.
Paper SPE 35892 introduces a physics-informed ML framework to elevate permeability prediction in notoriously heterogeneous carbonate reservoirs. By integrating physics-based constraints—specifically, modeling the discrepancy between core and nuclear-magnetic-resonance-derived permeability—this research enhances the predictive power of tree-ensemble algorithms. Physics-informed models transcend purely data-driven methodologies, offering more-robust and generalizable frameworks by embedding domain-specific physical understanding directly into the learning process, thereby bridging the gap between empirical observation and fundamental reservoir physics.
Further exemplifying ML’s strategic value, paper SPE 224365 details an intelligent system for optimizing depth selection in formation pressure testing (FPT). An artificial neural network trained on an extensive suite of well logs demonstrates a remarkable 94% specificity in identifying depth intervals likely to yield invalid pressure data. This capability is paramount in minimizing resource expenditure on unproductive tests, particularly in complex reservoir settings. The model provides a data-driven, consistent alternative to traditional, often subjective, FPT planning, underscoring the value of ML in derisking and streamlining critical field operations.
Collectively, these contributions signal a mature phase in the application of ML within the oil and gas sector. No longer confined to academic exploration, these techniques are providing robust, field-deployable solutions that enhance subsurface interpretation, improve operational foresight, and potentially drive economic benefits. The continued success of such endeavors will undoubtedly rely on the synergistic fusion of domain expertise, high-quality data, and the ever-evolving sophistication of ML algorithms.
Summarized Papers in This August 2025 Issue
SPE 222299 Machine Learning Unlocks Potential of Mud Logs, LWD in the Gulf of Thailand by Sethawut Palviriyachote, Texas A&M University, et al.
OTC 35892 Machine-Learning Approach Optimizes Formation-Pressure Testing in Complex Reservoirs by Ahmed K. Khassaf, Basrah University of Oil and Gas, et al.
SPE 224365 Physics-Informed Machine Learning Enhances Permeability Prediction in Carbonate Reservoirsby Mohammad K. Aljishi, University of Oklahoma, et al.
Recommended Additional Reading
SPE 224566 Applying Machine Learning in Highly Laminated Formation To Differentiate Pay and Nonpay Zones and Resolve Rt for Fit-for-Purpose Azimuthal Resistivity Tool Selection by Armando Vianna, Baker Hughes
SPE 223396 Early Signs of Gas Recognition Based on Machine-Learning Analysis of Passive Acoustics by Y. Maslennikova, TGT Diagnostics, et al.
Peyman Moradi, SPE, is a research scientist at Baker Hughes. He holds a PhD degree in petroleum engineering from the University of Calgary and has completed postdoctoral research at both the University of Calgary and The University of Texas at Austin. With more than 10 years of experience in the upstream sector, Moradi has worked as a reservoir engineer and scientific programmer for leading organizations including ExxonMobil, Tenaris, Xecta, ESG Solutions, and Petropars. His research interests include fiber-optic distributed acoustic and distributed temperature sensing monitoring and diagnostics, well testing analysis, formation evaluation, microseismic analysis, and well-design solutions. Moradi has published more than 50 papers and served as a technical reviewer for several journals and SPE conferences. He is also an active volunteer with SPE, serving as a judge for SPE Student Paper Contests and Section Awards and serving on committees for the Unconventional Resources Technical Conference and the SPE Western Regional Meeting.
