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Record W2144656689 · doi:10.1306/01270605130

The controls on the composition of biodegraded oils in the deep subsurface: Part II—Geological controls on subsurface biodegradation fluxes and constraints on reservoir-fluid property prediction

2006· article· en· W2144656689 on OpenAlexaffabout
Steve Larter, Haiping Huang, Jennifer Adams, Barry Bennett, Olufemi Jokanola, Thomas B. P. Oldenburg, Martin Jones, Ian M. Head, C. L. Riediger, Martin G. Fowler

Bibliographic record

VenueAAPG Bulletin · 2006
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsGeological Survey of CanadaAlberta EnergyUniversity of Calgary
Fundersnot available
KeywordsGeologyBiodegradationPetroleum engineeringGeochemistryPetrologyMining engineering

Abstract

fetched live from OpenAlex

Abstract The principal controls on the fluid properties of biodegraded oil systems have been determined by a combination of petroleum geochemistry, numerical modeling of oil biodegradation in reservoirs, and analysis of oil property data sets from a variety of geological settings. Petroleum biodegradation proceeds under anaerobic conditions in any reservoir that has a water leg and has not been heated to temperatures more than 80°C. In most reservoirs with low concentrations of aqueous sulfate, methanogenic degradation is a primary mechanism of petroleum degradation, whereas in waters containing abundant sulfate, sulfate reduction and sulfide production may dominate. Net degradation of petroleum fractions in reservoirs is primarily controlled by the reservoir temperature, the chemical compounds being degraded, and relationships between the oil-water contact (OWC) area and oil volume. The relative volumes of water leg to oil leg, prior level of oil biodegradation, and reservoir water salinity act as second-order controls on the process. Typically, degradation fluxes (kilograms of petroleum destroyed per square meter of oil-water contact area per year or kg petroleum m−2 OWC yr−1) for fresh petroleum in clastic reservoirs are in the range of 10−3–10−4 kg petroleum m−2 OWC yr−1 and increase with decreasing reservoir temperature, from zero near 80°C, to a maximum flux at the OWC of less than 10−3 kg petroleum m−2 OWC yr−1 at a temperature less than 40°C. At very low reservoir temperatures and with severely degraded oils, such as are seen in the near-surface Canadian tar sands at the present day, the net degradation fluxes are much less than maximum values. Nutrient supply from the aquifer and adjacent shales, mostly buffered by mineral dissolution, probably provides the ultimate control on the range of degradation flux values. Oil compositional gradients and resulting oil viscosity variations are common on both reservoir thickness and field scales in biodegraded oil reservoirs and are a defining characteristic of heavy oil fields produced by crude-oil biodegradation. Continuous vertical gradients in the oil columns document episodic degradation for many millions of years, suggesting that the time scales of oil-field degradation and petroleum charging are similar. The flux-temperature relationship we have derived, coupled with typical reservoir charge histories, defines the range of variation of fluid properties seen in many biodegraded oil provinces and identifies oil charge, mixing of biodegraded and fresh oils, and reservoir-temperature history as the primary controls on fluid properties. These flux-temperature relationships are easily integrated into prospect charge modeling procedures; sensitivity analyses show that the limiting factor in fluid property predictions, using even this first-level approach, are ultimately constrained by the accuracy of current oil-charge modeling estimates. The absence today of any functional geochemical proxies for assessing oil-residence time in oil fields and the substantial uncertainty in petroleum-charging times estimated by forward basin modeling is a major obstacle to more accurate fluid-property predictions that needs to be addressed.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.014
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.203
Teacher spread0.189 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations293
Published2006
Admission routes2
Has abstractyes

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