Multi-Faceted Approach for Optimizing a Microbial Control Program
Bibliographic record
Abstract
Abstract Microorganisms are a major challenge for the petroleum industry and can lead to microbiologically influenced corrosion (MIC), reservoir souring, or biofouling. Typically used standard methods for microbial identification (i.e. MPN) have limited success at identifying problematic organisms and providing timely feedback. Consequently, it is important to develop a multi-faceted approach that provides faster and more insightful results on the risk of microbial difficulties. A three-part approach was developed and implemented at Shell's Ursa platform to understand whether recent failures on the production system were the result of MIC and the efficiency of the biocontrol strategy on the recently installed waterflood to prevent system failure and reservoir souring. The approach involved 2nd generation ATP quantification for rapid enumeration of actively-growing microorganisms, as well as microbial enumeration and speciation using molecular methods. Recent failures on the production system were believed to be the result of under-deposit corrosion and microbial attack. However, culture-based methods revealed low numbers of problematic microbes, suggesting that the failures were not from MIC. Using ATP quantification, the presence of active microbes was observed at the manifolds and shown to increase throughout the production system. Speciation and enumeration results confirmed that problematic microorganisms were present and this additional information allowed for identification of optimized treatment locations in the production system to minimize the risk of MIC. For the waterflood, the new microbial analysis approach confirmed that the biocide program is successfully minimizing the risk of fouling, equipment failure and reservoir souring. ATP testing revealed occasional locations of elevated microbial growth and their potential for downstream contamination, allowing for further optimization, but overall demonstrated the biocide regime to be effective. The success of the biocide regime was determined by a reduction in active microorganisms and a population shift to Bacteria that are not likely to contribute to reservoir souring. This audit revealed a clear understanding of the microbial risks at Ursa, including areas that were performing well and those that needed required changes. This process is an excellent tool for microbial risk identification and biocide program optimization.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".