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Record W2460687120 · doi:10.2118/0315-0032-jpt

Technology Update: New Microbial Method Shows Promise in EOR

2015· article· en· W2460687120 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueJournal of Petroleum Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsEnhanced oil recoveryEnvironmental scienceMicrobial enhanced oil recoveryProduction (economics)Petroleum engineeringFlooding (psychology)Oil fieldBiochemical engineeringNutrientWaste managementEngineeringEcologyGeologyMicroorganismBiology

Abstract

fetched live from OpenAlex

Technology Update Tertiary oil recovery technologies can extend the economic life of maturing waterflooded reservoirs. This article describes the results from a biologically based enhanced oil recovery (EOR) technology that has improved waterflood efficiency by increasing oil production and decreasing the decline rates, thereby significantly increasing the recovery factor. Traditional tertiary recovery processes such as thermal methods, CO2 flooding, and chemical flooding require significant changes to field infrastructure and usually involve relatively high operating expenditures of up to USD 50 per incremental barrel of oil produced. Called Activated Environment for the Recovery of Oil (AERO), the technology used in this project represents a breakthrough in biologically based EOR by using a continuous injection of inorganic nutrients to stimulate indigenous microbes. The use of continuous injection (water and nutrients), which differentiates AERO from most previously attempted microbial EOR methods, prevents production disruptions and makes it easier to accurately measure, assess, and document the production benefits. The key advantages of this method are Decreased decline rates, enabling significant reserve gains Increased oil production Low capital expenditures Low operating costs Rapid response Biological EOR uses inorganic nutrients to activate indigenous microbes, those native to the field. Because no organic carbon is introduced, the microbial growth is restricted to the interface between the injection water and the oil, the carbon source for growing the microbes. The use of indigenous microbes is advantageous because they are perfectly suited to the local conditions and, unlike externally originated organisms, are neither costly to produce nor prone to rapid death in the reservoir and are thus in need of replacement. In addition, the concentrations of the nutrients required are relatively low, a major reason for the feasibility of continuous injection and stimulation. The technology is among the most inexpensive tertiary recovery methods available and requires only minimal changes to waterflood facilities for deployment. It can likewise be used with relatively minor modifications in fields without an operating waterflood, such as fields with a natural waterdrive or very mature fields where waterflooding has ceased. The biological EOR technology is producing a growing body of positive results, such as the following example from Canada.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.541
Threshold uncertainty score0.791

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.016
GPT teacher head0.284
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it