Geo-Resource Agent for Automated Reservoir and Mechanical Earth Model Characterization By Integrating Large-Language Models and Domain Specialized Toolbox
Bibliographic record
Abstract
Subsurface resource development relies on complex workflows that integrate exploration, modeling, and reservoir management. These workflows are fragmented across software platforms and teams, and are increasingly challenged by the scale and heterogeneity of geological data. Large Language Models (LLMs) offer new opportunities for automation, but direct application is limited by token constraints and inefficiency in handling large numerical datasets. This paper presents the Geo-Resource Agent, a LangGraph-based framework that couples LLM reasoning with deterministic computational tools. The system follows a four-part design: an Agent Core for planning, a Tool Layer for domain-specific functions, a Retrieval Layer for schema- and unit-aware data access, and an Execution & Storage Layer for auditability. Numerical tasks such as log interpolation and decline curve analysis bypass the LLM, while the model focuses on coordination and schema alignment. Case studies demonstrate transparent reasoning, reduced manual scripting, and scalable automation for subsurface engineering workflows.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".