Identifying the Optimum Zone for Reducing Drill String Vibrations
Bibliographic record
Abstract
Abstract Drilling operators witness significant lost time due to early failure of bottomhole equipment resulting from vibration and shock. This eventually leads to cumulative losses of millions of dollars for the industry. Increase in applied weight on bit (WOB) at low angular velocity (RPM) can trigger instabilities leading to stick slip. Potentially, compression and stretch that occurs along the BHA during drilling operations could lead to whirling and buckling. The complexity of the whirling motion causes lateral shifts, shocks and friction against the borehole walls. The driller has limited options. If stick-slip is identified, the driller decreases weight-on-bit (WOB) but whirling may occur from increasing revolutions per minute (RPM). Since the overall goal is to optimize drilling then reducing both WOB and ROB would not be an option since that would results in decrease in rate of penetration (ROP). This puts the driller in a tough situation where both severe vibrations and low ROP could occur simultaneously during drilling operations. There is an optimum zone where drilling parameters – RPM and WOB -- improve BHA/bit stability. A machine learning methodology is described which is able to (a) identify the zone of stability through the use of supervised and unsupervised learning and (b) anticipate an upcoming optimum for safe drilling by merging historic data with real time analysis through the use of online learning. A comparison is presented which compares supervised and unsupervised machine learning in identifying and updating the optimum zone. From this zone, a parameter set of permissible combinations of WOB and RPM can be estimated. The methodology described is then applied to data derived from several hours of drilling in a highly tortuous zone with persistent vibration problems.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".