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Record W2049102847 · doi:10.2118/09-01-36

Steam Injection Pressure and the SAGD Ramp-Up Process

2009· article· en· W2049102847 on OpenAlexaffabout
P. Li, M.Y. Chan, W. Froehlich

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2009
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsPetro-Canada
Fundersnot available
KeywordsSteam injectionPetroleum engineeringSteam-assisted gravity drainageOil sandsOil fieldGeomechanicsWater injection (oil production)GeologyGeotechnical engineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract Most SAGD projects require about one to two years for ramp-up. Over this period of time, oil rate will be below peak oil rate and SOR will be higher than long-term steady-state SOR. This paper discusses the effect of steam injection pressure on SAGD ramp-up time, the associated geomechanical effects and optimization of the ramp-up phase of SAGD. Different steam injection pressures induce different reservoir geomechanical behaviour in oil sands. Higher steam injection pressure is capable of inducing more favourable reservoir geomechanical effects (such as shear dilation and isotropic unloading), improving the reservoir permeability, and subsequently, benefiting the long-term SAGD operation. This paper indicates that the ramp-up time can be reduced due to the favourable geomechanical effects. A coupled reservoir geomechanical simulation technique was applied for this investigation. In addition, cap rock integrity concerns when applying high injection pressure are also addressed. It is recommended that during or following the ramp-up phase, the injection pressure be lowered to a safe operating pressure to ensure cap rock integrity. The effects of low and high steam chamber pressures on SAGD oil rate are also discussed. Introduction The steam-assisted gravity drainage (SAGD) process has been proven to be the most promising technology for developing the Athabasca oil sands reserves in northern Alberta. In 2001, only four commercial SAGD projects were operating in the field(1). By March 2006, the number of active SAGD projects increased to 15(2). Field SAGD operation experiences and numerical simulation show that the steam injection pressure plays an important role in SAGD production performance. In general, higher steam injection pressure helps lift fluid from downhole to the surface, increases the oil production rate, reduces the overall well life and improves the ultimate oil recovery. Some of these enhancements could result from geomechanical effects induced by higher steam chamber pressure. For the unconsolidated oil sands reservoir under certain in situ stress conditions, higher steam injection pressure tends to induce larger volumetric strain associated with shear dilation, thermal expansion, and even, tensile failure. As a result, reservoir permeability can be improved and oil recovery will be accelerated. Higher steam injection pressure, however, can also have undesirable effects. It may cause the reservoir cap rock to be breached due to geomechanical behaviour, and then result in a very high steam-oil ratio (SOR). Therefore, the time for applying high steam injection pressure needs to be optimized for maximum geomechanical benefits without causing cap rock failure. From the beginning of steam injection to the time when the partially drained zone approaches the cap rock, higher injection pressure may be safely applied. This strategy will accelerate the oil production rate for the ramp-up phase and will attain the peak SAGD oil rate sooner than otherwise possible. This paper applies a coupled reservoir geomechanical simulation (called "coupled simulation") technique(3) to investigate the relationship between the steam injection pressure and the SAGD ramp-up 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)0.000
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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.003
GPT teacher head0.187
Teacher spread0.184 · 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

Citations32
Published2009
Admission routes2
Has abstractyes

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