Advances in Tools to Monitor Souring and Corrosion in Oil and Gas Fields
Bibliographic record
Abstract
Abstract Microorganisms contribute to souring and corrosion in oil and gas field systems. Biocides and/or nitrate can be used to mitigate the negative effects associated with these microbial activities. In order to determine the success of or the need for these measures we use a number of analytical tools on aqueous or solid field samples: (i) spectrophotometric and HPLC assays are used to monitor key analytes (sulfate, sulfide, nitrate, nitrite and others), (ii) microbial assays are used to determine numbers and activities of key microbes and (iii) sequencing of PCR amplicons, typically of a portion of the 16S rRNA genes is used to determine microbial community compositions in field samples. The trick is to combine the information to arrive at a comprehensive view of what is happening and what action may be needed. For instance, a shale gas and a shale oil field in North West Canada, appear to have similar water chemistry. Both are highly saline but halophilic (salt loving) SRB were only found in samples from the shale oil not in those from the shale gas field, which appears related to the different temperatures in these fields of 30-35°C and 75-100°C, respectively. Hence, mitigation measures aimed at killing bacteria downhole may be appropriate for these shale oil but not for these shale gas environments.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".