Bibliographic record
Abstract
Technology Tomorrow - This is the second-to-last installment in this yearlong series examining R&D needs and potential. This article explores the challenges of applying biotechnology to the oil and gas sector and hints at the possibility of genetically modified organisms to achieve superior results. The current scientific uncertainty on just how future organisms will be included in the global energy picture suggests full speed ahead on the science, with the understanding that the issue of industry acceptance will need to be properly evaluated and managed. The Technology Tomorrow Series, comprising articles published in JPT during 2006 and 2007, is available as a collection on OnePetro (SPE-160929-JPT). Editor's note: This is the second-to-last installment in this yearlong series examining R&D needs and potential. This article explores the challenges of applying biotechnology to the oil and gas sector and hints at the possibility of genetically modified organisms to achieve superior results. The current scientific uncertainty on just how future organisms will be included in the global energy picture suggests full speed ahead on the science, with the understanding that the issue of industry acceptance will need to be properly evaluated and managed. The Technology Tomorrow Series, comprising articles published in JPT during 2006 and 2007, is available as a collection on OnePetro (SPE-160929-JPT). Today the impact of biotechnology is most noticeable in the health and pharmaceutical sectors, chemical industry, and in environmental technologies. The time is coming when it also will play a role in the energy sector. This is the result of progress in two major areas that are likely to have important consequences. The first one is the growing awareness of the major chemical fluxes fueled in the depths of the Earth's crust by the deep biosphere: microorganisms that live without oxygen and form the basis of previously unrecognized element cycles completely different from those that take place at the surface, driven by solar energy and oxygen. The simultaneous presence of minerals, gases, hydrocarbons, and bacteria in the anoxic deep biosphere is the basis of new speculation on carbon cycling in anoxic regions. Microbiologists now have new tools to detect and monitor biological activity in the environment, among which are the powerful techniques of specific DNA amplification and detection, which are best known to nonexperts for their applications in crime forensics. In just about the same way, specific types of bacteria can be detected in minimal amounts from any type of environment and recognized and functionally identified—for example, sulfate recycling bacteria (associated with the souring of reservoirs and oil-extraction systems) living at extraordinary depths and temperatures. The second major field of influence will be biological fuel synthesis and carbon capture from renewables. Interest in these possibilities blossomed and waned in the 1970s, but the area is now experiencing renewed interest, funding, and revival by the exploitation of fast-progressing genomics and bioengineering techniques. This article addresses the relevance and potential of biotechnology applications to the energy industry, envisioning the areas where biotechnology science progress might affect present and future business. Black Energy: Oil, Gas, and Bugs Oil and gas have been part of this planet for so long that the smallest living cells on the surface and in the deep biosphere have evolved ways to feed on them and gain energy, however unpalatable the ingredients might be. Bacterial degradation of crude oil in the reservoir is the cause of the poor quality of oil found in many areas, from the Canadian tar sands to offshore fields. Some of the biochemical pathways of oil hydrocarbon transformation are known, but many remain to be clarified, including mechanisms that might be in action in the reservoirs where temperature is compatible with bacterial life. Recent indications support the possibility of in-situ bioconversion processes of oil to methane and maybe even to other gaseous hydrocarbons. In similar ways to biological conversion of some types of coal to methane, exploitation and acceleration of the natural phenomena could be an interesting alternative to other traditional technologies of energy recovery from difficult sources.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.011 | 0.017 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.014 |
| Insufficient payload (model declined to judge) | 0.042 | 0.018 |
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".