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Record W4235679801 · doi:10.2118/2001-089

Numerical Study and Economic Evaluation of SAGD Wind-Down Methods

2001· article· en· W4235679801 on OpenAlexaboutno aff
L. Zhao, D.H.S. Law, R. Coates

Bibliographic record

VenueCanadian International Petroleum Conference · 2001
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeophysics and Gravity Measurements
Canadian institutionsnot available
Fundersnot available
KeywordsCitationLibrary scienceOperations researchComputer sciencePolitical scienceEngineering

Abstract

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Abstract At a certain point in a SAGD operation, it is no longer economic to continue steam injection when the steam-oil ratio (SOR) becomes too high. A less energy intensive gas injection process following the SAGD process can prolong oil production by using the energy in-place. Numerical studies of the injection of noncondensable gas, and mixture of steam and noncondensable gas following a SAGD operation were conducted using the CMG's STARS reservoir simulator. The simulation results suggest that after 3 to 5 years of SAGD operation, when three quarters of the targeted reservoir is hot, it is appropriate to start a wind-down process. Injecting non-condensable gas results in a much lower production cost compared to continued steam injection; however, the oil production is reduced. Coinjection of steam with gas produces more oil than the gas-only injection process without substantially increasing production cost. This is probably the desired wind-down process. Optimization is needed for the coinjection process. The choice of non-condensable gas depends on the cost and availability. Introduction The huge heavy oil and bitumen resources in Western Canada have motivated efforts to develop suitable recovery processes. One of the results of these efforts is the Steam-Assisted Gravity-Drainage (SAGD) process1, which was proposed 20 years ago. The process utilizes horizontal wells and steam injection process. The slow gravity drainage is compensated for by using long horizontal wells, resulting in a reasonable production rate. In addition, the overall recovery rate may be as high as 70% of the original oil-in-place (OOIP). Much research has been conducted on the process in order to obtain a better understanding of the process under various reservoir conditions, to improve the accuracy on performance prediction, and to solve operational problems. After the process was pilot tested in AOSTRA's Underground Test Facility (UTF) and showed great promise2,3, SAGD has been regarded as one of the leading in-situ recovery processes for heavy oil and bitumen resources. Many SAGD projects are in operation, under construction, or in the planning stages in Western Canada. In the SAGD process, as the steam chamber grows, oil is gradually recovered, accompanied by an increasing steam-oil ratio. At a certain point, it is no longer economic to continue steam injection; however, the reservoir is still hot, and the energy in-place can be utilized. Non-condensable gas (NCG) or mixture of NCG and steam injection has been proposed as a wind-down process. A less energy-intensive gas injection process may maintain reservoir pressure, utilize the energy inplace, and prolong oil production. The purpose of this study is to evaluate various possible SAGD wind-down processes, and to find the appropriate time for starting a wind-down process. Using the reservoir simulator, STARS (Computer Modelling Group Ltd.), wind-down processes of the injection of NCG, and mixture of steam and NCG were numerically investigated. It was found that injecting NCG produces a considerable amount of oil. Although the oil production is lower than that of a steam-only injection process, the economics are greatly improved.

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)0.002
metaresearch head score (Gemma)0.007
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.009
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.062
GPT teacher head0.306
Teacher spread0.244 · 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

Citations6
Published2001
Admission routes1
Has abstractyes

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