Bibliographic record
Abstract
How To Select PDC Bit for Optimal Drilling Performance R. Nygaard; R. Nygaard U. of Calgary Search for other works by this author on: This Site Google Scholar G. Hareland G. Hareland U. of Calgary Search for other works by this author on: This Site Google Scholar Paper presented at the Rocky Mountain Oil & Gas Technology Symposium, Denver, Colorado, U.S.A., April 2007. Paper Number: SPE-107530-MS https://doi.org/10.2118/107530-MS Published: April 16 2007 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Get Permissions Search Site Citation Nygaard, R., and G. Hareland. "How To Select PDC Bit for Optimal Drilling Performance." Paper presented at the Rocky Mountain Oil & Gas Technology Symposium, Denver, Colorado, U.S.A., April 2007. doi: https://doi.org/10.2118/107530-MS Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentAll ProceedingsSociety of Petroleum Engineers (SPE)SPE Rocky Mountain Petroleum Technology Conference / Low Permeability Reservoirs Symposium Search Advanced Search Abstract Selecting bit when you have multiple bit vendors giving their proposal can be a challenge for the operator. To justify the bit selection can be hard for the drilling engineer in a multiple vendor situation. The purpose of this paper is to show a systematic approach on how to select PDC bit based on quantitative measure by using a simple scorecard. When the drilling organization has agreed on the overall drilling objective for the well a scorecard is used as decision criteria for selecting bits. To quantify the input for each bit a drilling simulator was used. The simulator can, based on a rock strength prognosis for a well, predict the rate of penetration and bit wear for each bit based on the bit design. For other criteria which are more difficult to obtain e.g. ability to create dog leg a qualitative ranking was used. In the two field examples shown from the North Sea the method has worked well to give a reliable and transparent bit selection method. Using a scorecard also reduced the ambiguity among the bit company representatives on how the selection process was done. Keywords: bit selection, cutter, bit design, Artificial Intelligence, criteria, Simulation, scorecard, bit wear, objective, ROP Subjects: Drill Bits, Drilling Operations, Bit design Copyright 2007, Society of Petroleum Engineers You can access this article if you purchase or spend a download.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.046 | 0.026 |
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".