Technology Focus: Bit Technology and Bottomhole Assemblies (December 2012)
Bibliographic record
Abstract
Technology Focus The oil and gas business continues to migrate toward deeper plays, less drillable geology, and more tortuous directional objectives, all within a cost framework that sustained high oil prices are driving upward. Our part of the oil and gas industry—well construction—is having to expand drilling capability and continuously improve efficiency so that operators can keep marginal projects economic and avoid dramatic departures from planned schedule and cost. Against this background, it is vital that our knowledge of the downhole drilling environment improves. The historical habit of “try it and see,” driven by an absence of downhole measurements or the applied use of them, all too frequently failed to deliver predictable performance, especially in technically demanding scenarios. Our industry deserves better than that, so it is appropriate to ask, “Are we optimized yet?” As in previous features, there is progress to celebrate. The articles here will demonstrate that the quest to improve understanding of drillstring and tool behavior, to optimize well placement, and to execute with failure avoidance and ever-improving performance continues. It is much rarer now to see operations plagued by poor bit selection, baffled drilling teams, and repeated tool failures—a tribute to the drilling industry’s hunger to improve. There is, however, more to do—rate of penetration to be raised, bit life to be lengthened, tool life to be improved, geosteering to be refined. So, if your team is not actively working on improvement in these areas or is not equipped with the knowledge or data to do so, ask why; there are people and tools out there ready to help. Let’s remember that, when we integrate the measurements, the gurus, and the operational practitioners, improvement usually results. The key to an optimized operation is knowledge; and performance will tell you whether your operation has enough of it. So, if predictable, improving performance is not what you see from your operation or your service, keep pushing. Our industry needs it. Recommended additional reading at OnePetro: www.onepetro.org. SPE/IADC 151175 A Systematic Approach To Improving Directional Drilling Tool Reliability in HP/HT Horizontals in the Haynesville Shale by Errol Pinto, Shell Upstream Americas, et al. SPE 151377 Anomalous Behaviors of a Propagating Borehole by Luc Perneder, University of Minnesota, et al.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".