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Record W2002383245 · doi:10.2118/08-04-06

Oil Recovery and Technology Sequencing

2008· article· en· W2002383245 on OpenAlexaff
Maurice B. Dusseault

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsProfitability indexSteam-assisted gravity drainageProduction (economics)Petroleum engineeringProcess engineeringEngineeringComputer scienceEnvironmental scienceBusinessEconomicsMaterials science

Abstract

fetched live from OpenAlex

Introduction Steam-Assisted Gravity Drainage (SAGD) has attracted much attention since its inception in 1990 to 2000. However, developments have also occurred in other production technologies. Table 1 lists commercialized production technologies for viscous oils (μ> 100 cP in situ), along with suggested screening criteria for use as the first and major extraction method in a reservoir. These guidelines are approximate only; there are other important criteria, and each reservoir must be evaluated before a production approach is chosen. For example, IGI and VAPEX are gravity drainage methods that can give high RF; however, in viscous oils, production rates may be a small fraction (10 – 30%) of those for a thermal process. Nevertheless, these methods will become more widely used, particularly for the range μ< 1,000 cP, once reservoir engineers acknowledge the advantages of no heat costs and high RF. This article recommends deliberate technology sequencing planning at the beginning of a project(1). Of course, everyone wants low costs, high RF and high rates. More realistically, technology sequencing could give high early profitability followed by a long production life that eventually achieves a high life-cycle RF because low production rates can be tolerated if OPEX is low (i.e. non-thermal). Choosing a single exploitation technology now seems simplistic in view of possibilities for technology sequencing over the productive life span of a reservoir. Flow Instabilities To serve as an introduction to sequencing, the three classes of instabilities are reviewed: gravitational (vertical phase segregation), viscous (mobility-ratio issues) and capillary (multiphasic surface tension effects) instabilities. Gravitational instabilities, such as steam or gas override in steam drive and water underride in WAG methods, lead to impairment of recovery efficiency. However, gravity drainage methods using long horizontal wells (IGI, SAGD, VAPEX, etc.) now exploit vertical phase segregation to achieve high RF values. Viscous instabilities associated with pressure gradients and viscosity differences include coning, fingering, channeling and hydraulic fracturing. To reduce or avoid them, viscosity differences can be reduced (solvents, steam) or production can be undertaken at gravity drainage conditions with no significant pressure gradients. Capillary instabilities arise because of interfacial tensions between two fluids, restricting entry of the displacing fluid into a small pore throat. However, this does not occur if the continuityTable 1: Viscous oil production technologies. Available in Full PaperFIGURE 1: Maintaining oil films in IGI to achieve high RF. Available in Full Paper of the oil phase through the pore throat is maintained. In gravity drainage, high pressure gradients are avoided, thereby eliminating pinch-off and ganglia formation. Exploiting these instabilities means high RF values become possible; ignoring them leads to low RF, although not necessarily low rates. For example, CP is a solution gas drive approach: in viscous oil, as pressure is dropped, gas exsolves and eventually severely degrades permeability to oil because gas bubbles are immobilized in pores by interfacial forces. This leads to the low projected RF values for this technology (RF = 0.10 – 0.15) when applied to thick, high permeability, 1,000 – 3,500 cP reservoirs in the Venezuelan Faja del Orinoco(2).

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: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0120.007

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 head0.007
GPT teacher head0.180
Teacher spread0.173 · 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
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

Citations6
Published2008
Admission routes1
Has abstractyes

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