BENCHMARKING TO SET FIELD-LEVEL COST SAVINGS TARGETS AND SUCCESSFUL METHODS TO REDUCE FIELD OPERATING COSTS
Bibliographic record
Abstract
Oil and gas operators have been forced by rising investor expectations and a maturing resource base to both improve operating standards and reduce the cost of operations. During the past five years, the author's company has executed 20 studies in the US and Canada, examining the costs and methods of oil and gas operations for nearly 2,000 fields, for over 100 exploration and production companies. These studies cover more than a dozen basins from the Gulf of Mexico (Shelf and deepwater) to Alaska, including most producing basins in the US lower 48 states and Western Canada. They focus on developing an understanding of leading practices of successful operators, and identifying areas for remedial action to enhance cash flow. The paper examines various methods operators use to identify specific high cost areas for remedial action, and detail examples of successful operations. The focus will be on cost saving opportunities and practices utilised in offshore operations (Shelf and deepwater).
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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 | 0.015 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".