Leveraging Large Language Models for Cost Management and Supply Chain Optimization
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".