With the rise of machine learning and other artificial intelligence methods, the ability to digest data for potential application in the petroleum industry has increased tremendously. Data is seen as the key to reducing uncertainty in decisions for this capital‑intensive industry where the costs of bad or delayed decisions are huge. Thus, the right data delivered at the right time can revolutionize the industry and save precious time and money.
Gaining insights from digital data in the highly technical exploration and production industry requires experience, knowledge, and awareness about the acquisition of the data. The technologies presented here aim to facilitate the decision-making process while requiring less time and lower costs without sacrificing the accuracy of the data and still decreasing the probability of human errors. They show how data collected from different vantage points can be integrated with conventional data‑acquisition methods to help visualize and reduce the uncertainty of the subsurface.
Developments in other industries in automation and electronics have enabled modernization and miniaturization of oilfield instruments. Our industry seeks ideas and methods that are reliable, convenient, and practical to inform and guide operations, filling the gaps where and when conventional data is not available.
This Month’s Technical Papers
Autonomous Intelligent Logging Platform Enables Cost and Time Savings
Divided-Time Data-Transmission System Uses a Microchip Storage Ball
Real-Time Fiber-Optics Solution Opens Door to the Wellbore Environment
Recommended Additional Reading
OTC 31327 Density Measurement of Three-Phase Flows Inside Vertical Piping Using Planar Laser‑Induced Fluorescence by Amy Brooke McCleney, Southwest Research Institute, et al.
OTC 31836 Subsea Multiphase Flowmeter Measurement Performance Assurance With an Applied Data Validation and Reconciliation Surveillance Methodology by Emmelyn Graham, BP, et al.
SPE 208712 IADC Code Upgrade: Data Collection and Work Flow Required To Conduct Bit Forensics and Create Effective Changes in Practices or Design by William Watson, Shell, 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.