A Comprehensive Microbial Assessment of Hydraulic Fracturing Stimulations using State-of-The-Art Molecular Methods: Three Western-Canadian Case Studies
Bibliographic record
Abstract
Abstract Experience has shown that hydraulic fracturing operations can introduce and/or stimulate microbial populations in the wellbore that in turn may lead to undesired corrosion, souring or other production issues. Biocides are applied to prevent the establishment of problematic microbes. Characterizing and quantifying which microbes will be introduced to a well using molecular techniques allows for optimized or even proactive treatment and prevention strategies to be implemented, whereas, traditional microbial testing methods have proven insufficient. Once the standard for microbial assessments in the oil and gas industry, culture media bottles are now just one of many available tests. Tests vary by their resolution (culturable, active and living, total microbes), and the information they yield. Some tests target very specific microbial subgroups of concern (culture media, qPCR), while others evaluate all microbes within the sample (ATP, qPCR, 16S rRNA sequencing). In the case studies presented, water and produced fluids were collected from all pertinent frac sample points (source waters, pre- and post-chem and post completions) and were assessed using the suite of microbial methods stated above. Three case studies are presented with several noteworthy observations regarding the value microbial tests provide to frac operations. First, culture media-based testing consistently resulted in incoherent and confusing data that failed to correlate with the remaining testing technologies. Second, ATP technology provided efficient and timely testing which lent itself well to on-site, evidence-based decision making. During one of the fracs, ATP results were used to modify and optimize a microbial control program on-the-fly. Third, DNA-based testing (qPCR and 16S rRNA sequencing) provided the most comprehensive insight into the microbial communities exposed to the well, and those that established post-completions. Overall, holistic microbial testing offers the user key information required to design and implement successful microbial control programs for frac. Without it, microbial issues plagued production efforts. Culture media tests provided limited and unreliable information and were deemed not suitable for frac operations. ATP provided a useful microbial load in real-time but could not elucidate the types of microbes present. DNA testing filled this gap by providing quantities and types of microbes present. Apart from assessing microbial control programs during the frac, monitoring the production fluids is essential to assuring continued well performance. The acknowledgment of the role microbes play in well completions, and the testing technology to evaluate oilfield microbes is rapidly advancing. Here we present some of the first case studies highlighting the use of molecular, DNA-based technology for assessing hydraulic fracturing operations and showing the fallacy of culture media-based testing which is the current industry standard.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".