MétaCan
Menu
Back to cohort
Record W4234290571 · doi:10.2118/2008-156

Mechanisms of Heavy Oil Recovery by Low Rate Waterflooding

2008· article· en· W4234290571 on OpenAlexafffundabout
A. Mai, Apostolos Kantzas

Bibliographic record

VenueCanadian International Petroleum Conference · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsLaricina Energy (Canada)
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsPetroleum engineeringEnvironmental scienceGeology

Abstract

fetched live from OpenAlex

Abstract At the conclusion of primary heavy oil production, significant volumes of oil still remain in the reservoir under depleted reservoir pressure. Waterfloods are often consideredfor additional oil recovery. It is accepted that conventional oil waterflooding theory is not applicable for heavy oil. However, there is a lack of understanding of how waterfloods should perform in these reservoirs, particularly after water breakthrough. In this study, waterfloods were performed at multiple rates in cores containing heavy oil and connate water. In some cores oil was initially free of solution gas, and waterfloods were a primary recovery process. In other cores, waterfloods were performed after primary production. Experiments were performed in linear systems for a high viscosity oil (11,500 mPa?s), at different injection rates. The influence of viscous and capillary forces is studied in primary vs. secondary recovery systems. A common misconception is that capillary forces are negligible in heavy oil; however this work shows that these forces are significant, and that water imbibition after water breakthrough can lead to improved oil recovery in both primary and secondary waterfloods. Introduction The Canadian deposits of heavy oil and bitumen are some of the largest in the world. Recent estimates by the AEUB1 suggest that this resource could exceed 270 billion m3 in Alberta alone, with a significant portion of this oil located in reservoirs where expensive thermal operations will not be economic for recovering the oil. Heavy oil is a special class of this unconventional oil, which has viscosity ranging from 50 – 50,000 mPa?s (cP), and low API gravity. Heavy oil reservoirs are often found in high porosity, high permeability, unconsolidated sand deposits. Permeability of the sand averages in the range of 3 D2, but oil does not flow easily due to its high viscosity. At the initial reservoir temperature andressure, the oil may contain dissolved solution gas, thus a fraction of the oil can be recovered using the energy from heavy oil solution gas drive. Primary production can recover around 5% of the oil in place1, leaving significant oil volumes in the reservoir for potential secondary recovery. Waterflooding is a common technique for secondary oil recovery in conventional oil reservoirs. In heavy oil systems, the extremely high oil viscosities lead to adverse mobility ratio conditions, thus water will tend to "finger" through the oil, and recoveries are expected to be extremely low3,4. Despite the poor recoveries predicted theoretically, there have been numerous reports of heavy oil waterfloods performed in the literature5-8. All of these studies reported poor sweep efficiencies and overall recovery. However it is significant that in all cases some oil was recovered despite the highly adverse mobility ratios in the waterfloods. Laboratory studies of waterflooding in heavy oil systems also demonstrate that some oil can be recovered by controlled injection of water. Although heavy oil waterflood responses cannot be readily predicted through theory5, waterfloods have still been carried out in heavy oil reservoirs in Alberta and Saskatchewan for the past 50 years8,9.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.223
Teacher spread0.205 · 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 designSimulation or modeling
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

Citations14
Published2008
Admission routes3
Has abstractyes

Explore more

Same venueCanadian International Petroleum ConferenceSame topicReservoir Engineering and Simulation MethodsFrench-language works237,207