Artificial intelligence (AI) and machine-learning algorithms can enable energy companies to digitally reconstruct well histories using both public and company-specific historical well data. This paper discusses how oil and gas companies are using a new generation of AI-driven applications powered by computational-knowledge graphs and AI algorithms to create a digital knowledge layer for oil and gas wells that provides a timeline of significant well events such as drilling problems, blowout preventer tests, bottomhole-pressure (BHP) measurements, and well interventions. The authors explain how they train the application’s machine-learning algorithms to read hundreds of thousands of historical reports to harvest knowledge about the well and store the extracted knowledge in a digital knowledge layer.
Introduction
Even small improvements in upstream operations efficiency can result in dramatic savings. One way to improve efficiency is through better and faster decision-making, enabled by tools that provide seamless access to both the current state and the full history of wells.
