From Archives to Abandonment: Applying Generative AI to Optimize Plug and Abandon Processes in Old Oil Wells

Plug and abandonment (P&A) of wells is a critical concern in the oil and gas industry due to safety and environmental reasons. Effective abandonment requires a comprehensive understanding of subsurface contents, including completions, formations, and reservoirs, collectively known as well data. Gathering all relevant well data for each candidate well is essential before initiating any P&A campaign. However, there has been no effective data-gathering process to ensure data accuracy and P&A design. This study aims to design and implement an optimized P&A workflow by leveraging generative artificial intelligence (GenAI) technologies to systematically extract well data from historical manuscripts, well reports, and archival documents. This paper describes how a GenAI utility is used to scan well files to extract comprehensive well data, leaving no room for missing information. The work involves training AI models to recognize and extract relevant information from various historical sources. The validated data are integrated into the Wellbarrier Well Integrity Management System (WIMS), which is used to design and execute the P&A workflows (Omar et al., 2023). By employing GenAI, the study aims to enhance the accuracy, efficiency, and reliability of the P&A process. The novel approach of using AI for data extraction ensures that all relevant historical data are effectively captured and utilized. This leads to optimized P&A workflows that are tailored to the specific conditions and requirements of each well, thereby improving the overall safety and effectiveness of the decommissioning process. The study has delivered significant improvements in the P&A process, including enhanced data accuracy, improved efficiency, and optimized workflows. By ensuring regulatory compliance and mitigating risks, the study will contribute to the long-term integrity and environmental protection of abandoned wells. The findings will also advance the field of well integrity management, providing valuable insights and methodologies for future projects.