Conversion of crude oil to methane by a microbial consortium enriched from oil reservoir production waters
Bibliographic record
Abstract
The methanogenic biodegradation of crude oil is an important process occurring in petroleum reservoirs and other oil-containing environments such as contaminated aquifers. In this process, syntrophic bacteria degrade hydrocarbon substrates to products such as acetate, and/or H2 and CO2 that are then used by methanogens to produce methane in a thermodynamically dependent manner. We enriched a methanogenic crude oil-degrading consortium from production waters sampled from a low temperature heavy oil reservoir. Alkylsuccinates indicative of fumarate addition to C5 and C6 n-alkanes were identified in the culture (above levels found in controls), corresponding to the detection of an alkyl succinate synthase encoding gene (assA/masA) in the culture. In addition, the enrichment culture was tested for its ability to produce methane from residual oil in a sandstone-packed column system simulating a mature field. Methane production rates of up to 5.8 μmol CH4/g of oil/day were measured in the column system. Amounts of produced methane were in relatively good agreement with hydrocarbon loss showing depletion of more than 50% of saturate and aromatic hydrocarbons. Microbial community analysis revealed that the enrichment culture was dominated by members of the genus Smithella, Methanosaeta, and Methanoculleus. However, a shift in microbial community occurred following incubation of the enrichment in the sandstone columns. Here, Methanobacterium sp. were most abundant, as were bacterial members of the genus Pseudomonas and other known biofilm forming organisms. Our findings show that microorganisms enriched from petroleum reservoir waters can bioconvert crude oil components to methane both planktonically and in sandstone-packed columns as test systems. Further, the results suggest that different organisms may contribute to oil biodegradation within different phases (e.g., planktonic vs. sessile) within a subsurface crude oil reservoir.
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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 | 0.001 | 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) | 0.001 | 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 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".