Digital data acquisition has revolutionized the oil and gas industry, offering unprecedented opportunities for efficiency, cost reduction, and informed decision-making. Our industry is at the forefront of a digital transformation, where the effective acquisition and use of data have become paramount to success amidst global pressure for energy transition. Recent industry trends have seen a significant shift toward the use of legacy data, the integration of various sources of data, and the application of machine‑learning techniques, creating a more dynamic and data‑driven landscape.
Machine learning, a subset of artificial intelligence, has become a game-changer where massive data sets from various sources—including drilling, production, and reservoir data—are being analyzed to predict equipment failures, optimize drilling procedures, and improve reservoir management. Machine-learning models also can enhance safety by predicting and preventing accidents, thus reducing downtime and operational risks.
Traditionally, oil and gas companies operated in departmental silos, with different teams managing their data sources independently. Recent industry trends emphasize the integration of various data sources to create a more holistic understanding of reservoirs and operations. The integration of well logs and seismic data, for example, provides a comprehensive view of subsurface conditions. By combining these data sources, operators can better understand geology and reservoir characteristics to improve exploration and development success.
Furthermore, by continuously monitoring and analyzing reservoir performance using data from drilling, production surveillance, and interventions, operators can make timely adjustments to production strategies, maximizing hydrocarbon recovery and field lifespan.
As technology continues to advance, the industry’s potential for further optimization and growth is boundless. Embracing these digital trends is not just a matter of staying competitive; it’s about ensuring a more sustainable and efficient future for the industry as a whole.
This Month’s Technical Papers
Fiber-Optics Approach Monitors Drainage Profile of Multistage Stimulation
Machine-Learning Techniques Classify, Quantify Cuttings Lithology
Holistic Approach Uses Electromagnetic Tools, LWD Data To Improve Reservoir Understanding
Recommended Additional Reading
OTC 32210 Pushing the Limits in Deepwater Data Acquisition for Accelerated Field Development: Industry Record Batch Well Testingby Ozgur Karacali, SLB, et al.
OTC 32643 Evaluation of Loop Current/Loop Current Eddy Fronts To Guide Offshore Oil and Gas Operations by Jill Storie, Woods Hole Group, et al.
SPE 211807 Multifrequency Data-Acquisition Model and Hybrid Neural Network for Precise Electromagnetic Wellbore Casing Inspection by Guang An Ooi, King Abdullah University of Science and Technology, et al.
Jyotsna Asarpota, SPE, is a senior consultant for Halliburton and leads projects to integrate subsurface reservoir models with surface networks, analyzing capacity and identifying bottlenecks. She has also worked on digital transformation strategies and real-time well optimization using advanced automation. Asarpota holds an MS degree in oil and gas engineering from Robert Gordon University and a bachelor’s degree in chemical engineering. She has published several papers on well integrity and fluid and integrated capacity modeling. Asarpota is an active SPE volunteer and has contributed to various conferences and exhibitions as a member of steering and program committees. She is a member of the JPT Editorial Review Board.