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Record W2013645705 · doi:10.2118/09-03-27

Insights Into Non-Thermal Recovery of Heavy Oil

2009· article· en· W2013645705 on OpenAlexafffundabout
A. Mai, J. Bryan, N. Goodarzi, Apostolos Kantzas

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of CalgaryLaricina Energy (Canada)
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPetroleum engineeringEnvironmental scienceEnhanced oil recoveryOil productionFossil fuelPeak oilThermalWaste managementGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract Many heavy oil reservoirs contain oil that has some limited mobility under reservoir conditions. In these reservoirs, a small fraction of the oil-in-place can be recovered using the internal reservoir energy through heavy oil solution gas drive (primary production). An integral part of this process is the so-called 'foamy oil mechanism', whereby oil is produced as a gas-in-oil dispersion. At the end of primary production, the bulk of the oil is still in place, while the natural energy of the reservoir has been depleted. This remaining oil is still mostly continuous and presents a valuable target for further recovery. Many of these reservoirs are relatively small or thin, or may be contacted by overlying gas or underlying water. As such, they are poor candidates for thermal oil recovery methods, so any additional oil recovery after primary production must be non-thermal. In this work, we present experimental results of foamy oil depletion at two different length scales and varying depletion rates. Tests were conducted in the absence of sand production, and the results from the depletion experiments are interpreted in terms of viscous forces. At the conclusion of primary recovery, the potential for further non-thermal exploitation of these reservoirs is explored. Results for waterflooding and chemical flooding are presented, demonstrating the viability of these techniques for heavy oil EOR. Several displacement mechanisms are identified through the secondary and tertiary processes that contribute to significant (although potentially slow) incremental recovery of heavy oil. Introduction Many countries have heavy oil reservoirs. Canada and Venezuela in particular contain some of the largest heavy oil and bitumen resources in the world. Rising energy demands, coupled with a decline in conventional oil reserves, has led to increased interest in heavy oil recovery in recent years. The size of these heavy oil deposits is considerable, and with volatile crude oil prices making it difficult to produce from some higher viscosity bitumen reservoirs, production of heavy oil could potentially be very important in years to come. Understanding the mechanisms by which heavy oil can be displaced in reservoirs is crucial to the successful recovery of this resource base. Heavy oil can be defined as a class of oils with viscosity ranging from 50 mPa.s up to around 50,000 mPa.s. This oil has limited mobility under reservoir temperature and pressure, and Darcy's Law predicts that the oil can flow slowly under high applied pressure gradients. However, it has been observed that in these reservoirs, solution gas drive leads to significantly higher rates and recoveries than what was expected by conventional understanding of gas-oil relative permeability behaviour(1). This behaviour, first reported in Canadian heavy oil, has since been observed in many other reservoirs around the world including South America, China and Albania. Investigations into the causes of this abnormal, but fortuitous, primary production response have been the focus of many publications in the past 25 years. The recovery from primary production in heavy oil reservoirs may be as high as 20%(2), but is usually lower.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.004
GPT teacher head0.192
Teacher spread0.188 · 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

Citations126
Published2009
Admission routes3
Has abstractyes

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