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Record W2083108397 · doi:10.2118/04-02-05

Insights Into Some Key Issues With Solvent Aided Process

2004· article· en· W2083108397 on OpenAlexaffabout
Suraj Gupta, Simon Gittins, P. Picherack

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsSolventPetroleum engineeringProcess (computing)Work (physics)Process engineeringEnhanced oil recoveryViscosityDilutionSteam injectionChemistryEnvironmental scienceWaste managementBiochemical engineeringMaterials scienceComputer scienceEngineeringOrganic chemistryMechanical engineeringThermodynamics

Abstract

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Abstract Gravity drainage processes, such as SAGD and VAPEX, aim at exploiting viscosity reduction of the target oil, either through thermal diffusion or dilution. Thermal diffusion being much faster than molecular diffusion, production rates from a steam process are expected to be higher than a solvent-alone process. Despite this apparent drawback, solvent-alone processes promise to be attractive owing to lower heat losses and energy requirements, lesser impact to the environment, possible downhole upgrading, etc. One is naturally led to thinking of combining the benefits of SAGD with that of the solvent-alone process. The resultant process, aimed to improve the performance of SAGD by introducing hydrocarbon solvent additives to the injected steam, is the subject matter of this paper and is called the " Solvent Aided Process" (SAP). With the combined potential benefits come the combined challenges of the two processes. Although substantial understanding has been developed around SAGD in recent years, a number of unknowns associated with solvent processes exist. This paper, drawing heavily from the authors' extensive investigation of SAP, dwells on the estimated benefits of SAP over SAGD, discussing possible mechanisms at work, optimal design of injectant (involving lighter alkane additives), and operational aspects and issues such as solvent retention, etc. Introduction The concept of adding solvents to the injected steam for improving the performance of steam-based recovery processes is not new. Various authors(1–8) have described and analyzed the benefits of adding hydrocarbon solvents to primarily steam flood processes. Their work mainly focused on the oil-recovery enhancements that solvent addition brings to steam-flood(1–3, 5–8) or steam stimulation(4). With the advent of SAGD(9, 10), exploitation of the vast heavy oil and bitumen resources of the Western Canadian Sedimentary Basin is now feasible. However, owing to a highly energy-intensive process and the nature of the target product (heavy oil), the economics of SAGD is very susceptible to fuel prices and heavy oil market forces. Use of solvents in place of steam in gravity drainage for heavy oil recovery(11–14) promises to be a more energy-efficient process but suffers from poor (estimated) rates of recovery(12,13). This is because molecular diffusion, the mechanism responsible for the dilution of heavy oil which reduces its viscosity in a solvent-alone process, is much slower than its counterpart, thermal diffusion, in the case of SAGD. Among other things, modification of the drainage geometry(15) has been suggested for providing a large contact area to compensate for a low rate of diffusion and to raise the rates of production. Given earlier efforts(1–8) to improve the steam-flood process with the use of solvents, the combination of solvents and steam in conjunction with the concept of gravity drainage seems to be a natural progression from SAGD and VAPEX. A few investigators, ncluding Butler and Yee(16) and Palmgren and Edmunds(17), have suggested the combination of thermal effects and solvent dilution. Viscosity reduction, as pointed out by Butler et al.(11), has a direct impact on the rate of production from a gravity drainage process.

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)0.003
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.027

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0060.008
Open science0.0020.002
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0080.001

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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 designNot applicable
Domainnot available
GenreOther

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

Citations66
Published2004
Admission routes2
Has abstractyes

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