In the world of oil and gas operations, the generation of enormous amounts of data is a common occurrence. For example, a seismic survey in New Mexico alone can produce a file that reaches a petabyte in size. To process and extract meaningful insights from such vast datasets, Chevron has been utilizing GPUs since 2008. This move put Chevron ahead of the curve, as many other industries did not yet require that level of processing power for complex workloads at the time.
Chevron has now taken its data processing capabilities to the next level by embracing the latest generative AI tools. These tools allow the company to derive even more insights from its massive datasets and unlock greater value. According to Bill Braun, Chevron’s CIO, AI is the perfect match for large-scale enterprises with huge datasets. It provides the necessary computational power to make sense of the data and make informed decisions.
One area where Chevron is leveraging AI is in analyzing the data from the Permian Basin Oil and Gas Project. As one of the largest landholders in the Basin, Chevron recognizes the value of the publicly-available datasets that are required to be published by all operators. These datasets offer an opportunity to learn from competitors and accelerate the learning process for everyone involved. By using generative AI to analyze this vast amount of data, Chevron can quickly gain insights that can give it a competitive edge.
Furthermore, Chevron is using gen AI to enable proactive collaboration and ensure safety in its operations. The company operates in a large, distributed area where data might not be available across the entire expanse. However, gen AI can be layered over various data points to fill in gaps and provide a more comprehensive model. For instance, gen AI can alert Chevron to potential interference with other companies working in the same area, allowing for proactive measures to prevent disruption.
Chevron also relies on large language models (LLMs) to craft engineering standards, specifications, safety bulletins, and other alerts. These models are constantly being fine-tuned to ensure accuracy and adherence to requirements. Additionally, Chevron is exploring ways to inform AI models about geology and equipment to generate predictions, such as identifying the location of the next basin.
In terms of safety, Chevron is beginning to use robotic models to perform dangerous tasks while keeping humans safely away from harm. This approach can be more cost-effective and reduce liabilities for the company. Chevron has also made efforts to bridge the gap between teams on the ground and teams in the office. By embedding different teams together and encouraging cross-disciplinary collaboration, the company has created high-performing teams that work seamlessly together.
Environmental impact is a major concern in the energy sector, including oil and gas operations. Chevron is actively addressing this issue through carbon sequestration and digital twin simulations. Carbon sequestration involves capturing, removing, and permanently storing CO2. Chevron has some of the largest carbon sequestration facilities globally and uses digital twin simulations to ensure the effectiveness of reservoirs holding captured carbon. Moreover, Chevron generates synthetic data to make predictions related to carbon sequestration.
While AI and data centers consume a significant amount of energy, Chevron is committed to managing remote locations as cleanly as possible. The company acknowledges the importance of minimizing environmental impact and strives to find sustainable solutions.
In conclusion, Chevron’s adoption of AI and generative AI tools has enabled the extraction of valuable insights from its massive datasets. The company’s proactive approach to collaboration, safety, and environmental impact sets it apart in the energy sector. By leveraging AI technologies, Chevron is not only optimizing its operations but also staying ahead of the competition and addressing environmental concerns.
