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Record W2016475463 · doi:10.2118/04-10-03

Preliminary Laboratory Evaluation of Cold and Post-Cold Production Methods for Heavy Oil Reservoirs Part A: Ambient Conditions

2004· article· en· W2016475463 on OpenAlexafffundabout
Apostolos Kantzas, G. Brook

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsNexen (Canada)University of Calgary
FundersCanada Research ChairsPorous Media Laboratory
KeywordsOil reservesPetroleum engineeringOil in placeOil productionEnvironmental scienceWork (physics)Fossil fuelOil sandsGeologyPetroleumMining engineeringWaste managementEngineeringGeographyAsphaltArchaeologyPaleontology

Abstract

fetched live from OpenAlex

Abstract The problem of post-cold production for conventional heavy oil (Lloydminster type) reservoirs is tackled. The topic is broken into two papers. In the current paper, laboratory work at ambient conditions is addressed. In Part B of the paper(1), parallel work at reservoir conditions is presented. First, an overview of related literature material is included. Then several experimental methods for additional heavy oil recovery are attempted in laboratory models. The methods tested include water flooding and polymer flooding. This work is offered as stimulus to consider the state of all cold produced heavy oil reservoirs and focus on the possible alternatives for this significant Canadian reserve. Introduction The heavy oil and oil sand deposits of Western Canada represent one of the largest hydrocarbon accumulations in the world with a resource base of nearly 1.7 trillion bbls(2). This is 1.5 times larger than the proven reserves of the entire Middle East. The bulk of the reserves are contained in three major geologically distinct regions. These areas are the conventional heavy oil of the Lloydminster area, the Carbonate Triangle, and the oil sands deposits. This work focuses on conventional heavy oil formations. Enhancement of primary production of heavy oil through the so-called cold production mechanism has been a popular topic in the heavy oil industry for the past 15 years. However, cold production alone cannot produce more than an estimated 10 - 15% of the original oil in place (OOIP). This paper addresses the issue of post-cold production or, more specifically, post-primary production of heavy oil via different injection techniques that include water flooding and polymer flooding. Several independent sets of experiments were run that included: one cold production experiment in a large cylindrical model with a central production well (radial production geometry), two linear core floods, four experiments at ambient conditions in rectangular geometry physical models, and six experiments at reservoir conditions in rectangular geometry physical models (one including sand production). The ambient conditions experiments are described here while in the accompanying paper(1), experiments at reservoir conditions are presented. Literature Survey A large variety of heavy oil EOR methods are proposed in the literature(3). Earlier work on non-thermal recovery of heavy oil did not take into account cold production mechanisms and thus it is questionable how such work will apply to the current state of the Canadian heavy oil fields. Notable is the work performed at the University of Alberta by Professor Farouq Ali et al.(4, 5). Wellbore productivity can be potentially improved by the creation of oil-in-water emulsions downhole. This can be achieved through the injection of a surfactant solution in the wellbore. This was tested with encouraging results, as reported by Norcen and SRC, in production wells that were hampered by water-in-oil emulsions(6). Carlson et al.(3) discussed a number of alternative methods for low cost recovery of heavy oil, describing a methodology for the screening of oil reservoirs and associated processes and listing a number of examples. Immiscible gas injection was one of the examples considered.

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)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
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 head0.021
GPT teacher head0.305
Teacher spread0.284 · 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 designBench or experimental
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

Citations15
Published2004
Admission routes3
Has abstractyes

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