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Record W1971321814 · doi:10.2118/0115-0078-jpt

Pelican Lake: First Successful Application of Polymer Flooding in a Heavy-Oil Reservoir

2015· article· en· W1971321814 on OpenAlexaboutno aff
Adam Wilson

Bibliographic record

VenueJournal of Petroleum Technology · 2015
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsPelicanOil in placePetroleum engineeringFlooding (psychology)Enhanced oil recoveryOil fieldEnvironmental scienceLight crude oilGeologyPetroleumFishery

Abstract

fetched live from OpenAlex

This article, written by Special Publications Editor Adam Wilson, contains highlights of paper SPE 165234, ’Pelican Lake Field: First Successful Application of Polymer Flooding in a Heavy-Oil Reservoir,’ by Eric Delamaide, SPE, IFP Technologies; and Alain Zaitoun, SPE, Gerard Renard, SPE, and Rene Tabary, SPE, IFP Energies Nouvelles, prepared for the 2013 SPE Enhanced Oil Recovery Conference, Kuala Lumpur, 2-4 July. The paper was peer reviewed and published in the August 2014 SPE Reservoir Evaluation & Engineering journal, p. 340. Initially, polymer flooding had not been considered as a viable enhanced-oil- recover (EOR) technology for Pelican Lake in northern Alberta, Canada, because of the high viscosity of the oil until it was considered in combination with horizontal wells. Polymer flooding generally has been applied in light- or medium-gravity oil, and, even today, standard industry screening criteria limit its use to viscosities up to 150 cp. Pelican Lake is the site of the first successful application of polymer flooding in much-higher-viscosity oil (1,000–2,500 cp). Introduction The Pelican Lake field, approximately 250 km north of Edmonton, Alberta, Canada (Fig. 1), was discovered in 1978 and started producing in 1980. With more than 6 billion bbl of oil originally in place (OOIP) and a primary recovery estimated at less than 7%, it presents a significant target for EOR. But it is also a challenging reservoir with high-viscosity oil in a thin formation. Early History The reservoir-depletion mechanism is solution-gas drive, but initial reservoir pressure was low and there is very little dissolved gas, so there is little energy in the reservoir. Because the oil is also viscous (from 600 to 80,000 cp), primary recovery is low, approximately 5 to 10% of OOIP. In addition, the reservoir is thin (an average thickness of 5 m). As a result, the first wells drilled in 1980–81 were not economic. Horizontal Drilling in Pelican Lake CS Resources drilled its first horizontal wells in the Winter pool in Saskatchewan and then turned to Pelican Lake in 1987. Horizontal drilling is well-adapted to Pelican Lake, provided that the well can be maintained in the pay zone. Because the reservoir is so thin, a horizontal well can increase the reservoir exposure tremendously. The production performances of the horizontal wells were markedly better than those of the vertical wells and seemed to correlate reasonably well with the length of the horizontal drain in the reservoir. In 1991, CS Resources drilled its first openhole lateral arm from a main horizontal drain. Then, in 1993, it went one step further and drilled two multilateral wells with a new tool, the lateral-tieback system. The use of multilaterals would greatly expand in the years to come. Screening of EOR Methods for Pelican Lake Despite the improvement in recovery and overall economics resulting from the use of horizontal and multilateral wells, it was clear that primary recovery would be limited to 5–10% of OOIP, and other options were considered to increase recovery.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.239
Teacher spread0.230 · 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

Citations10
Published2015
Admission routes1
Has abstractyes

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