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Record W1987102986 · doi:10.2118/171050-ms

Factors Affecting SAGD Performance in Dipped Bed Extra Heavy Oil Reservoirs

2014· article· en· W1987102986 on OpenAlexfundno aff
L. Andarcia, Joseph Bermudez, Yilena Montero Reyes, H.. Caycedo, A.. Suárez

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
FundersAlberta Innovates - Technology Futures
KeywordsSteam-assisted gravity drainagePetroleum engineeringPermeability (electromagnetism)GeologyOil sandsAPI gravityAsphaltSteam injectionOil viscositySaturation (graph theory)Crude oilViscosityMaterials science

Abstract

fetched live from OpenAlex

Abstract In reservoirs with certain dip, the steam chamber advances different than in conventional SAGD projects. Since the horizontal permeability is normally higher than vertical permeability and because reservoir pressure is lower toward up-dip; an early lateral growth of the steam chamber should be promoted to the uppermost reservoir position. If horizontal permeability controls the gravity drainage instead of vertical permeability, it is thought that a good gravity drainage process in dipped reservoirs could be achieved, even in cases were vertical transmissibility is poor. However, if there is mobile water within the reservoir, then the steam easily flows up-dip and away from the producer well diminishing the hot oil draining to the producer and affecting the SAGD well's performance. The factors affecting SAGD efficiency in a dipped-bed extra heavy oil reservoir (6.5°API) located in southern llanos Colombia have been analyzed. An important extra heavy oil accumulation toward the outcrop of main production sands was encountered. The reservoir temperature is low (100°F) so bitumen viscosity is high (>100.000cp). In addition, oil saturation could be lower than is the standard for commercial projects in some areas. A reservoir characterization was done using data from 18 wells. Information from basic and special logs, routine and special core analyses as well as displacement tests was used to build a robust numerical model. Several numerical simulations were run to address the performance of SAGD under such conditions. Particularly, the amount of mobile water and dip, play a major role in SAGD efficiency since the steam chamber behaves accordingly to both parameters. Numerical simulations done in this work have shown that SAGD thermal efficiency in dipped bed reservoirs is dramatically affected by energy losses toward the uppermost reservoir position since the steam tends to flow up dip far from producer wells. This effect is amplified when mobile water is within the porous medium. Different strategies to improve SAGD efficiency were explored by numerical simulation. Well configuration was changed from parallel to staggered; electromagnetic heating was evaluated as well as hybrid processes. Hybrid processes could certainly open doors for economic development of highly viscous heavy oil reservoirs with mobile water saturation.

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 score0.012
Threshold uncertainty score0.023

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 communication0.0010.000
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 head0.029
GPT teacher head0.267
Teacher spread0.238 · 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".

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Citations0
Published2014
Admission routes1
Has abstractyes

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