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The oil and gas industry generates vast amounts of data that, if properly leveraged, can generate insights that lead to recovering hydrocarbons with reduced costs, better safety records, lower costs associated with equipment downtime, and reduced environmental footprint. Data analytics and machine-learning techniques offer tremendous potential in leveraging the data.

Few industries contain as many phases, steps, and levels of interface between the start and end product as the oil and gas industry. It therefore is hardly surprising that communication, in all its varied forms, is at the very heart of our business.

This paper describes a study aiming to provide input for the well-completion-strategy design and operational parameters for a carbonate reservoir experiencing electrical-submersible-pump failures.

Electric-powered fracturing fleets looked impressively resilient in 2020 amid what was otherwise a significant collapse for the hydraulic fracturing sector. Today, the situation is more complicated.

Deal marks the first ADNOC partnership with Pakistani energy companies.

Prospect could hold up to 80 million BOE.

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The authors develop a data-driven approach, enabled by machine learning, to find an optimal operating envelope for gas-lift wells.

The paper describes an integrated work flow to apply autonomous inflow-control devices successfully in an offshore heavy-oil reservoir with significant uncertainty in remaining oil thickness and water/oil contacts.

The continued collaboration of the oil and gas industry with other technological sectors is crucial for its success. SPE has itself been a catalyst for collaboration in the industry through the events it organizes and with other societies to bring together practitioners from different disciplines.