Data Science Has Exploded Across the Industry Over the Past 75 Years

Abstract

In 1957, the Journal of Petroleum Technology published an article titled “Application of Large Computers to Reservoir Engineering Problems.” That was the first reference in JPT to what became known as supercomputers. The high-speed computers used in the 1957 article were capable of performing 60 million operations in about 3½ hours. They were proposed to analyze the thorny problem of multiphase flow. “It presently appears that the large computer will be required to investigate multiphase flow and to predict the flow behavior of oil and gas reservoirs considering two- and three-space dimensions.” Now, 67 years later, supercomputer speeds are measured in trillions of operations per second, and investigating multiphase flow is just one of the many uses. This extreme growth in speed has given rise to artificial intelligence (AI) and machine learning (ML), which has found its way into almost every corner of the petroleum industry. The Society of Petroleum Engineers and JPT has kept up with the digital advances in the industry, holding countless conferences, symposia, and other meetings centered on the digital aspects of the industry and recently creating the Data Science and Engineering Analytics (DSEA) technical discipline. In 2019, SPE launched the Data Science and Digital Engineering in Upstream Oil and Gas online publication. Four leaders in the oil and gas digital space shared their views on the current state of data science in the industry and the future of the discipline. Sushma Bhan, currently on the board of directors for Ikon Science, is SPE’s Technical Director for DSEA. Before joining Ikon, she worked for Shell for 32 years, eventually rising to the role of chief data officer for subsurface and wells. “My journey in the oil and gas industry began in 1988, when I joined Shell’s Production Computing Assisted Operations team as a programmer analyst,” Bhan said. “During my time there, I gained valuable exposure to field production operations and developed a deep understanding of real‑time data.” Jim Crompton, who claims he is mostly retired, is an associate professor of petroleum engineering at the Colorado School of Mines, a faculty fellow at the school’s Payne Institute for Public Policy, and director of Reflections Data Consulting. “My academic career started out in exploration geophysics, so my first introduction to engineering analytics came from processing seismic data at Chevron Geophysical Company in Houston,” Crompton said. “I studied earthquake seismology in graduate school, but, when I graduated, I learned the oil and gas industry paid a lot more than the USGS did, so my career goals changed.” Shahab D. Mohaghegh is a professor at West Virginia University and the president of Intelligent Solutions. “Since I started working for the petroleum industry throughout the world using artificial intelligence in 2000, I was able to work with actual data (field measurements) that has been saved by all the companies,” Mohaghegh said. “Working with actual data using AI provided several technologies that have been incredibly fantastic compared to what we have done in the past.” Pushpesh Sharma, the chair of the DSEA Technical Section, holds a PhD degree in chemical engineering from the University of Houston and is senior product manager for Aspen Technology. “Till a few years ago, the focus was on proving the efficacy of data science/machine learning methods for energy use cases,” Sharma said. “However, in recent years, the concerns are around large-scale deployment, maintenance, and the black‑box nature of ML models. Because of that, I started seeing increased focus on the deployment, explainability, and trust of ML models.”