How Chevron is using gen AI to strike oil

Core Insights - Chevron is leveraging generative AI tools to extract insights from massive datasets, particularly in oil and gas operations, which generate significant amounts of data [1][3][4] Data Utilization - The Permian Basin, where Chevron is a major landholder, contains an estimated 20 billion barrels of oil, accounting for about 40% of U.S. oil production and 15% of natural gas production [3] - The Railroad Commission of Texas mandates that all operators publish their activities, providing a competitive advantage for companies like Chevron to learn from public datasets [4] AI Applications - Generative AI is used to analyze geological data and fill in gaps, enhancing operational efficiency and safety by predicting potential interferences between companies [5][7] - Large language models (LLMs) are employed to create engineering standards and safety alerts, ensuring precise adherence to specifications [6] Team Integration - Chevron has integrated teams from different disciplines to enhance collaboration, fostering a culture where machine learning engineers and mechanical engineers work closely together [8] - The company has invested in upskilling engineers in data science and system engineering to improve operational maturity [8] Environmental Considerations - Chevron is actively involved in carbon sequestration, utilizing digital twin simulations and synthetic data to predict the long-term performance of carbon storage reservoirs [9] - The company is focused on managing the environmental impact of energy consumption in data centers and AI operations [9]