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Record W2009043640 · doi:10.2118/2004-222

Evaluation of SAGD Performance in the Presence of Non-Condensable Gases

2004· article· en· W2009043640 on OpenAlexaff
Serhat Canbolat, Serhat Akın, M. Polikar

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicGNSS positioning and interference
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetroleum engineeringComputer scienceProcess engineeringGeologyEngineering

Abstract

fetched live from OpenAlex

Abstract In the steam assisted gravity drainage (SAGD) process, the addition of small amounts of non-condensable gases to steam may improve oil recovery. The gas accumulates at the top of the reservoir where it provides an insulation effect and forces the steam chamber to spread laterally. The result is a more efficient use of steam and the potential for greater recovery of oil. Six experiments were conducted in two different geometries to study the effect of non-condensable gas on the performance of SAGD. These experiments consisted of steam-only, steamcarbon dioxide and steam-n-butane injection. Three SAGD experiments were carried out in a scaled 3-D model packed with crushed limestone premixed with a 12.4 °API heavy crude. In these experiments, the steam-only case had the highest recovery, as expected. However, using carbon dioxide or nbutane with steam reduced the steam consumption. In both those cases, recovery was lower than the steam-only case. The other SAGD experiments were carried out using limestone core plugs saturated with the same heavy oil. Similar trends were observed for core plug experiments; however, the recovery was better when n-butane was added to steam. The presence of n-butane had a positive effect on the oil recovery and required less steam consumption than the other two cases. ifferences between the experiments were identified by means of analytical modeling. All the experiments were modeled with respect to Butler's SAGD theory and Reis' linear model. The results of the 3-D experiments were matched by all models, whereas the results of the core scaled experiments were better represented with Reis' linear model because of the heterogeneity present in the core plugs. The addition of non-condensable gas to steam in a SAGD operation was evaluated using physical models of different geometries. The experimental results indicated that for both geometries, steam consumption was reduced by using either carbon dioxide or n-butane. More experimental studies are needed to asses the effect of non-condensable gas addition on increasing oil recovery. Introduction The basic mechanism of the SAGD process for heavy oils was initially proposed and demonstrated by Butler. It is based on simple physical concepts: rising steam heats the formation and hot liquids flow downward. Generally a large portion of the original oil in place can be produced by gravity drainage, resulting in low residual oil saturation. In this process, parallel horizontal wells are used for both injection and production due to the large contact area that they provide for the process. The steam is injected through the upper well, where it condenses on the cold sections and heats the oil. The viscosity of heavy oil decreases the oil becomes mobile and drains by gravity with the condensed steam to the lower production well1. A major limitation of SAGD is the requirement for a large amount of steam, particularly in thin, low quality reservoirs. This means that high energy is required for production of continuous steam. Das and Butler proposed that this limitation could be avoided or decreased in two ways:

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 score0.003

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.243
Teacher spread0.218 · 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

Citations26
Published2004
Admission routes1
Has abstractyes

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