Chemical Prevention of Corrosion-Induced Premature Well Failures Using a Novel Lubricity Mitigation Strategy
Bibliographic record
Abstract
Abstract The sucker rod systems and tubing strings play a crucial role in the mechanical oil recovery process; their stability and reliability are desirable factors in optimizing oil production from a well. Although durable, these metallic downhole components can experience failure during oil recovery. These failures usually stem from accumulated fatigue induced by corrosion mechanisms. The dilapidation of these costly crucial components often leads to expensive downtimes, lost production, and unnecessary maintenance costs, which can all be exacerbated by frequent failures. It is imperative to develop a mitigation strategy to extend the run life of wells and occlude premature component failures, thereby providing cost savings. This paper presents a field-proven methodology used to extend the run life of high-failure frequency wells in Western Canada, involving the continuous application of lubricious corrosion inhibitors. The corrosion inhibitors' Coefficient of Friction (CoF) was quantified using a Tribometer. This bifurcate study focuses on two major aspects: a root cause analysis of the high-frequency failures and the corresponding chemical mitigation strategy to address these rampant failures. The root cause analysis showed that wellbore configuration significantly affects downhole equipment failures and hints at the inimical role of friction in inducing rampant failures, prompting the need to incorporate lubricity in the chemical mitigation strategy, especially for complex directional wells. This successful approach has been implemented in over 3000 wells and has eliminated frequent component failures while extending the run-life of wells from 700-900 days to 1650 days (over an 80% increase), resulting in cost savings. This paper showcases and elucidates an economical and pragmatic chemical mitigation strategy to address frequent well failures and optimize oil production while providing operational recommendations during drilling that are key to improving a well’s long-term productivity.
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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 | 0.000 |
| 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 | 0.001 | 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".