Application of a Biocide Water Treatment Program to Prevent Coiled Tubing Corrosion: A Case Study
Bibliographic record
Abstract
Abstract From December 2013 to February 2014, a major Canadian coiled tubing (CT) service company experienced several premature CT failures that all occurred in the same geographic region of northeastern, British Columbia. The failed CT samples were analyzed both by the manufacturer and a third party to determine the failure mechanism. Upon inspection, internal corrosion on the base material and preferential corrosion of the bias weld filler material was, in multiple cases, determined to be the cause of failure. It was concluded that microbial induced corrosion (MIC) likely caused localized corrosion and pit initiation leading to crack propagation and sudden failure. When MIC was identified as a potential cause of failure, the service company's engineering team initiated an extensive water testing program to determine a preventative solution. The base fluid used for CT operations in the specified geographical region typically consisted of produced water, often recycled multiple times from fracturing operations. The CT service provider was not responsible for the water supply, nor did it have historical information on the supplied water quality. In several occurrences, the fluid tested positive for the presence of harmful acid producing, and sulfate reducing bacteria, which further supported the presence of MIC. To eliminate or minimize the potential for harmful corrosive bacteria, the service company implemented a biocide water treatment program. The service company explored several methods of applying both liquid and powdered biocide establishing a best practice for pre-treating the supplied water and treating it throughout the operation. Field testing proved the application of biocide dramatically decreased bacteria count. To date the biocide treatment program has been successful and no additional premature failures due to corrosion have been recorded. This has allowed the engineering team to optimize biocide loadings and revise the CT retirement criteria. While the subject of microbial induced corrosion in CT has been widely discussed and published throughout industry literature, the problem has not been prevalent among CT operations in Canada. This paper discusses a specific premature CT failure due to corrosion, an analysis of the base fluid in the area that was believed to cause the corrosion, and the biocide treatment program used to prevent similar CT failures in the future.
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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".