A Field Study into the Mitigation of Severe Downhole Microbiologically Influenced Corrosion of Oxygen Contaminated Hydro-Fracked Unconventional Oil Reservoirs
Bibliographic record
Abstract
Abstract The mitigation of downhole, dissolved oxygen (DO) accelerated, microbiologically influenced corrosion (MIC) in hydro-fracked, oxygen contaminated, relatively high temperature, unconventional reservoirs is challenging. Additionally, little research has been published in the area. The mitigation of downhole MIC in this type of well is complicated by the reality that mechanical cleaning is not yet possible; therefore, downhole MIC mitigation is solely dependent on chemical applications. DO-accelerated, downhole corrosion is also more difficult to control in high TDS (total dissolved solids) producing wells with complex water chemistries. Furthermore, the hydrofracking process typically introduces oxygen which accelerates corrosion. In this case-study, an oilfield in Alberta, which had been experiencing severe downhole corrosion, was the subject of a comprehensive corrosion investigation, which identified MIC as the most significant corrosion mechanism. Downhole water chemistry, oxygen contamination levels, ATP determination, and 16S DNA sequencing were used to identify those specific wells which had quantifiable, carbon steel, MIC degradation issues. Several wells had high concentrations of sulfate-reducing bacteria (SRB) and methanogenic microorganisms as well as elevated dissolved sulfide concentrations. Once the problematic wells were identified, biocide and corrosion inhibitor programs were optimized in cooperation with the client and their oilfield chemical provider to effectively mitigate downhole corrosion.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".