Proactive Prevention of Ferromagnetic Iron-Induced RSS Tool Failures Using Novel Testing Techniques and Operational Modifications.
Bibliographic record
Abstract
Abstract The ubiquity of complicated and extended-reach horizontal wellbores with tighter windows has spurred the copious use of the rotary steerable system (RSS) in drilling operations. This magnetic-powered RSS technology, initially designed for the offshore drilling market, has proven to be an effective solution to the increasingly complex challenges in the land-based market. Although durable, as with other mechanical devices, equipment failure and malfunction may occur during drilling operations. The impairments of these expensive high-end systems while drilling often lead to costly trips and NPTs, which can be avoided with regular maintenance practices. Apart from these regular maintenance practices, it is also paramount to devise proactive techniques while drilling that will enhance the life cycle of these systems and prevent rampant and uneconomical trips. This paper presents a proven methodology that was used to eliminate the rampant RSS tool failures encountered on multiple rigs in Southern Alberta, Canada. While RSS tool failures have traditionally been attributed to the barite and mud system, scientific root cause analysis showed that ferromagnetic iron metal generated from different sources while drilling induced these failures. Ferromagnetic Iron has the potential to cause interference with downhole magnetic tools, causing them to fail and have solids entrapped in them. An ingenious operational procedure was devised and implemented using strategically generated magnetic fields in the mud circulation system at different locations. These magnetic fields strip the mud system of ferromagnetic materials to prevent damage to RSS tools. This procedure was also backed up with a novel testing technique that identifies and quantifies the presence of ferromagnetic materials in the mud system, which can be tracked on the daily drilling report or posted on a digital database. The test results help engineers detect the buildup of ferromagnetic iron in the mud system (indicating the strength of the magnetic fields) and the appropriate mitigation strategy to employ, which may include strengthening the magnetic fields and using centrifuges depending on the scenario. This successful approach eliminated RSS tool failures on multiple rigs and reduced Tool-Failure NPTs drastically by over 47% on average. This paper breaks down, showcases, and elucidates a practical engineering solution to a prevalent drilling problem, with easy-to-follow steps that can be replicated by mud engineers and technicians anywhere in the world.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".