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Record W3130629917 · doi:10.2118/203866-ms

Investigating the Performance of Various FCD Geometries for SAGD Applications

2021· article· en· W3130629917 on OpenAlexaff
Kousha Gohari, J. D. Ortiz, Anson Abraham, Oscar Becerra Moreno, Mazda Irani, Kristian Nespor, Javier Díaz Sánchez, Andrés Betancur, Jeromin Bilic, Khoi Duong, Farzad Bashtani

Bibliographic record

VenueSPE Thermal Well Integrity and Design Symposium · 2021
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of CalgaryConocoPhillips (Canada)
Fundersnot available
KeywordsSteam-assisted gravity drainageNozzleSteam injectionProcess (computing)Computer scienceFlow (mathematics)Petroleum engineeringOil sandsAsphaltSimulationMechanical engineeringEngineeringMaterials scienceMechanics

Abstract

fetched live from OpenAlex

Abstract Steam-Assisted Gravity Drainage (SAGD) is a complex process that often requires more control relative to conventional applications during production operations. Flow Control Devices (FCDs) have been identified as one of the technologies that offer improved downhole steam utilization and injection/production efficiency. The first FCD completions, with a helical geometry, were installed in SAGD wells at the ConocoPhillips Surmont project over a decade ago. The installations have shown improved steam chamber conformance and reduced steam-oil ratio (SOR) while accelerating bitumen production. Since then, various FCD geometries have been investigated and used, with several of them explicitly designed with a steam blocking capability. This study used a numerical simulator to investigate the performance of these various FCD geometries. This comprehensive study started testing several geometries in a flow loop and using the data obtained to develop a mechanistic model to characterize the flow performance of the FCDs and finally evaluating their performance in a holistic manner via a numerical simulator. By using mechanistic modeling, it was ensured that the performance of the devices was accurately represented, and the physics of the process were considered. The analysis used a commercially available numerical simulator to evaluate the performance of the various FCD geometries in SAGD operation. Three sector models representing different reservoir qualities observed in Surmont were used for the analysis. Additionally, various operating strategies were investigated for each sector model to ensure that a comprehensive understanding of each FCD geometry was achieved. The results of this study showed that FCD flow resistance setting or nozzle size played a significant role in the production performance of the wells in liner deployed FCD applications. Additionally, the steam blocking geometries resulted in increased cumulative production and lower SOR relative to other geometries. The FCD geometry did also impact the development of the steam chamber. Nevertheless, if the FCD completions are configured with the proper flow resistance setting or nozzle size, they provide a proactive measure, which leads to significantly better performance compared to a non-FCD completion. With lower subcool, the geometry of the FCD has a greater impact on the performance of the well. It was also confirmed that an aggressive operating strategy results in better performance of the FCD completions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.630
Threshold uncertainty score0.369

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.259
Teacher spread0.231 · 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 teacher head, 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

Citations4
Published2021
Admission routes1
Has abstractyes

Explore more

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