Leveraging Large Language Models for Cost Management and Supply Chain Optimization

Abstract In this paper, we introduce a cutting-edge solution to the complex challenge of managing operational costs in unconventional asset development, particularly concerning continuous well drilling, completion, and field maintenance operations within the oil and gas sector. These complicated field operations involve hundreds of service providers and incur vast volumes of invoices every year. Tremendous values are lost due to missed opportunities to improve contracting strategy, optimize material and equipment supplies, and identify cost-prohibitive design elements during drilling, completion, production, and plant maintenance through proper spend categorization. Leveraging the power of a machine learning solution, Large Language Model (LLM), and an interactive user interface, we automate the challenging task of categorizing millions of invoices from diverse service providers. Techniques including sentence transformation embedding, transfer learning, fine-tuning, and re-training process are employed to enhance model performance and adaptability to diverse invoice types. Through rigorous model training and iterative refinement facilitated by the user interface, our approach attains an impressive accuracy exceeding 90% across all regions. Results prove that the Large Language Model has a wide application in business optimization during unconventional asset development. This automated algorithm provides real-time insights through spending and significantly reduces the turnaround time. This study also enables direct identification of cost-saving opportunities such as potential reductions in fuel expenses related to drilling and completion activities. The advanced analytics capabilities following the modeling effort allow engineers and analysts in multiple functions to identify cost-saving opportunities through customizing visualization tools in various areas. This paper presents original methodology to adopt the Artificial Intelligence, i.e., Large Language Model, in the oil and gas industry and demonstrate its values with case histories.