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Record W1987965493 · doi:10.2118/0605-0062-jpt

Overview: Heavy Oil (June 2005)

2005· article· en· W1987965493 on OpenAlexaboutno aff
Tony Kovscek

Bibliographic record

VenueJournal of Petroleum Technology · 2005
Typearticle
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsnot available
Fundersnot available
KeywordsAPI gravityAsphalteneOil reservesPetroleum engineeringEnvironmental scienceProductivitySteam injectionPetroleumUnconventional oilPeak oilEnhanced oil recoveryResource (disambiguation)Waste managementFossil fuelCrude oilGeologyEngineeringClimate change

Abstract

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What is heavy oil, and why do you care about it? These are important questions with answers that are simple, but subtle. Heavy hydrocarbons exhibit gravity less than 20°API. They are characterized by high viscosity that increases as API gravity decreases, low hydrogen/carbon ratios, low gas/oil ratio, as well as significant sulfur, asphaltenes, and heavy metals. In short, heavy-oil reservoirs generally present low-energy and low-productivity wells. These characteristics make recovery challenging, yet the volume of heavy hydrocarbons in the world warrants a thorough look. Canada and Venezuela each possess heavy-oil resources of approximately 3 trillion bbl. In comparison, the heavy-oil resource of the U.S. is less than 200 billion bbl. This does, however, represent a more-than-40-year supply at current U.S. oil-consumption rates. Significant heavy-hydrocarbon resources also are found in Indonesia, Russia, and China, as well as elsewhere. A second reason for interest is the difficulty in attaining economical exploitation. It suggests a need for research and development activities. Thermal recovery, and steam injection in particular, is tremendously successful. The addition of heat reduces oil-phase viscosity significantly. Nevertheless, conventional steam-injection candidates are limited to onshore, relatively shallow, thick, and permeable sands. Given the oil volumes in place, the range of reservoir settings, and the difficulties of extraction, a suite of heavy-oil recovery options is needed. Consider an incomplete list of research and development opportunities. First, primary recovery that uses horizontal and multilateral wells is possible and profitable. The production mechanisms of the heavy-oil solution-gas drive process are, however, not completely elucidated, and performance cannot be simulated by conventional techniques. Second, waterflooding of heavy oil is summarily dismissed because of adverse mobility ratios, but is similarly not well understood. In cold and/or offshore environments, waterflooding may present the only viable recovery option following primary production. Third, steam injection is relatively mature, but steam is considerably less viscous and dense than oil. Cost-effective mobility and profile control by use of aqueous-phase surfactants, gels, or advanced well completions remains an open question. Fourth, in-situ combustion achieved by air injection is technically and economically feasible, especially for deeper, thinner, higher-pressure reservoirs. Combustion has seen less field application than steam because of the difficulty in its description and control. Finally, combustion presents possibilities for in-situ upgrading and sulfur removal. In short, the cumulative production totals of heavy oil are on the order of billions of barrels, but this is only a small fraction of the oil in place. Whether the potential and promise of heavy oil are realized depends on the collective action of industry, academia, and governments to deliver a suite of technologies appropriate for the wide range of reservoir and oil-phase conditions. Additional Heavy Oil Technical Papers Available at the SPE eLibrary: www.spe.org SPE 93881 Toward an Adaptive, High-Resolution Simulator for Steam-Injection Processes SPE 93894 A New Reservoir-Simulation Model for Understanding Reservoir Performance in the McKittrick Steamdrive

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 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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.953
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.011
GPT teacher head0.278
Teacher spread0.267 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations1
Published2005
Admission routes1
Has abstractyes

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