Improved Cooperation Between Service Company and Operator for Faster Development of Better Tools
Bibliographic record
Abstract
Abstract The present paper describes the prize to be gained when thorough feedback and co-operation between operator and service company are made a priority during the application of new tools in the oilfield. A real life example is provided to highlight how solution driven discussion and prompt detailed data sharing can enable service companies to develop new tools faster and cheaper to satisfy operator needs of tool reliability and accuracy. In July 2007, BP field-tested the impact tool during a memory PLT campaign in an onshore oilfield, UK. The impact tool is a memory gauge that is capable of logging downhole P/T information, tool-string angle and bi-directional impact resulting from jarring activities. While this trial was the first-time run of this tool within BP, across the industry it was the third application just in the North Sea. Yet during the job it became obvious that there was a need for further improvement of the tool in areas of tool function and set-up. Intensive discussion with the tool-provider on future enhancements of the tool commenced while operations were still going on. Based on this close co-operation, the vendor was able to develop solutions and implement them in the tool within weeks. The paper will provide insights into the project planning, share the relevant details of the operational phase and conclude with the obtained learning which lead to the enhancement of the impact tool. Since implementation of the modifications, the tool has shown excellent results and outstanding performance in recent field-operations.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.026 | 0.010 |
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".