Preventing Drill String Twist Off: Automatic Flagging of Abnormal Pressure Loss Signatures in Real-Time
Bibliographic record
Abstract
Abstract Abnormal pressure loss during a drilling operation signals a failure in the fluid hydraulics circulating system. This could be due to a washout in the drill pipe (either in the body or connection), bottom hole assembly (BHA) component or connection, downhole tool failure, surface mud pump problem, or losses into the formation. This paper describes a machine learning approach to accurately flag abnormal pressure losses and identify the root cause. An operational procedure to prevent twist offs once abnormal pressure loss is flagged is also outlined. The primary method used to flag abnormal pressure loss is to compare the standpipe pressure to a statistically modeled pressure calibrated using past data from the same well. Using the standpipe pressure, statistically modeled pressure, weight on bit, RPM, and flow in, an abnormal pressure loss belief is then calculated. The belief tracks standpipe pressure trends when WOB, RPM, and flow-in signals are relatively constant. Once an abnormal pressure loss belief has been identified, further analysis is performed using contextual data, such as survey information and BHA components, to identify the root cause. This study included learning from over 100 wells on which the approach was deployed, and abnormal pressure loss was flagged. Washouts due to drill pipe and BHA connection failures and BHA failures, such as seal failures, have been shown to periodically result in abnormal pressure drops throughout a BHA run. Factors such as wellbore geometry may cause the seal/joint to open or close during the BHA run. This is reflected as drops in pressure under stable drilling parameters, such as weight on bit, RPM, and flow in, followed by a pressure increase back to normal. These periodic abnormal pressure decreases continue until the drill pipe or BHA is changed or fully twisted off. However, for drill pipe body washouts, the pressure tends to bleed off/decrease over a longer period in a more consistent manner. The ability to identify a washout in the drill pipe or BHA can be immensely helpful in preventing a twist off thereby eliminating significant non-productive time. This paper outlines a novel automatic abnormal pressure loss alert system, a process for differentiating between drill pipe body, drill pipe/BHA connection and tool failures, and an operational procedure to handle such alerts when they are flagged.
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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) | 0.000 | 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".