In the ever-evolving landscape of the oil and gas industry, the integration of artificial intelligence (AI) emerges as a transformative force. Dr. Purnaprajna Mangsuli, Head of Data Science at Schlumberger, delves into the intricacies of this paradigm shift, emphasizing the potential impact on decision-making processes during the Machine Learning Developer Summit (MLDS) 2024.
Dr. Mangsuli begins by shedding light on the traditional methods of identifying oil reserves. He introduces the audience to the complexities involved, such as employing sonar waves and seismic imaging to pinpoint hydrocarbon possibilities. The focus shifts to the challenges faced by geologists in analyzing seismic images to determine fault lines and potential oil reserves.
The discussion seamlessly transitions to the core theme – the integration of AI to optimize workflows. Dr. Mangsuli emphasizes that AI should act as an aid to geologists rather than replacing them outright. The primary objective is to significantly reduce the lengthy 3-year turnaround time from survey initiation to good production.
Addressing the complexities of understanding document layouts, particularly in well-completion reports, Dr. Mangsuli highlights the importance of extracting valuable information from diverse sources. This includes graphs, tables, and textual data found in scanned documents.
Dr. Mangsuli introduces AI solutions designed to extract digital information from graphical elements such as curves and plots. He tackles challenges associated with document variations, noisy images, and handwritten characters, emphasizing the need for AI’s assistance in handling such intricacies.
The narrative progresses to unlocking valuable insights from historical well-completion data. Dr. Mangsuli introduces semantic search capabilities, allowing users to efficiently query and retrieve specific information. The focus extends to creating a robust database of golden records by extracting common information from multiple reports.
Dr. Mangsuli candidly addresses challenges in deploying AI algorithms at scale. He acknowledges the industry’s sensitivity to costs and sheds light on ongoing efforts to find cost-effective solutions. Offering a glimpse into the future, Dr. Mangsuli discusses ongoing projects that leverage generative AI. He previews the integration of AI systems with simulation and analysis engines, aiming to provide comprehensive insights into the oil and gas landscape.
In concluding remarks, Dr. Purnaprajna Mangsuli underlines the significance of ongoing AI projects and the potential of semantic search to redefine decision-making in the oil and gas sector. As the industry embraces AI, the journey towards enhanced productivity and streamlined processes gains momentum, promising a future where data-driven strategies drive success in the oil and gas domain.
