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Record W2771595160 · doi:10.3390/en10121999

Numerical Simulation Study on Steam-Assisted Gravity Drainage Performance in a Heavy Oil Reservoir with a Bottom Water Zone

2017· article· en· W2771595160 on OpenAlexaffabout
Junjun Ni, Xiang Zhou, Qingwang Yuan, Xinqian Lu, Fanhua Zeng, Keliu Wu

Bibliographic record

VenueEnergies · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of CalgaryUniversity of Regina
Fundersnot available
KeywordsBottom waterPetroleum engineeringGeologySaturation (graph theory)Steam-assisted gravity drainageComputer simulationEnvironmental scienceSink (geography)Oil fieldOil productionSteam injectionOil in placeOil sandsPetroleumMaterials scienceEngineeringOceanographyAsphalt

Abstract

fetched live from OpenAlex

In the Pikes Peak oil field near Lloydminster, Canada, a significant amount of heavy oil reserves is located in reservoirs with a bottom water zone. The properties of the bottom water zone and the operation parameters significantly affect oil production performance via the steam-assisted gravity drainage (SAGD) process. Thus, in order to develop this type of heavy oil resource, a full understanding of the effects of these properties is necessary. In this study, the numerical simulation approach was applied to study the effects of properties in the bottom water zone in the SAGD process, such as the initial gas oil ratio, the thickness of the reservoir, and oil saturation of the bottom water zone. In addition, some operation parameters were studied including the injection pressure, the SAGD well pair location, and five different well patterns: (1) two corner wells, (2) triple wells, (3) downhole water sink well, (4) vertical injectors with a horizontal producer, and (5) fishbone well. The numerical simulation results suggest that the properties of the bottom water zone affect production performance extremely. First, both positive and negative effects were observed when solution gas exists in the heavy oil. Second, a logarithmical relationship was investigated between the bottom water production ratio and the thickness of the bottom water zone. Third, a non-linear relation was obtained between the oil recovery factor and oil saturation in the bottom water zone, and a peak oil recovery was achieved at the oil saturation rate of 30% in the bottom water zone. Furthermore, the operation parameters affected the heavy oil production performance. Comparison of the well patterns showed that the two corner wells and the triple wells patterns obtained the highest oil recovery factors of 74.71% and 77.19%, respectively, which are almost twice the oil recovery factors gained in the conventional SAGD process (47.84%). This indicates that the optimized SAGD process with the two corner wells and the triple wells pattern is able to improve SAGD production performance in a heavy oil reservoir with a bottom water zone.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.033
Threshold uncertainty score0.066

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0010.000

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 head0.016
GPT teacher head0.263
Teacher spread0.246 · 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

Citations12
Published2017
Admission routes2
Has abstractyes

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