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).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".