Toward Low Costs for High Cost Resources
Bibliographic record
Abstract
Abstract Oil and gas companies are being driven to find and develop resources that are intrinsically higher cost than those that were accessible a decade ago. They must face the challenge of lowering full cycle costs so that they can provide their shareholders with the returns that they have become accustomed to in an environment of volatile and unpredictable commodity prices. In this paper, we draw on the experience of practitioners in US and international deep water exploration and development and unconventional resource plays as well as consultants active in developing strategies and performance improvement programs for high cost resources, to uncover common themes in moving toward low costs for high cost resources. In the view of the authors, three key themes emerge in the effort to lower costs: scale, excellence in execution and controlled experimentation. Scale is a prerequisite for lowering unit costs and leveraging lessons learned; excellence in execution extracts the full value embodied in scarce skilled employees; controlled experimentation is by definition necessary to continuously rewrite the rules of the game in producing high cost resources and progressively drive costs down. This system, once created, can be deployed in new fields and basins to build profitable growth from high cost resources (See Figure).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".