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Record W2068739706 · doi:10.2118/148804-ms

A Parametric Simulation Study for Solvent Co-injection Process in Bitumen Deposits

2011· article· en· W2068739706 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

aboutThe title or abstract carries a Canadian signal from the geographic lexicon.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Unconventional Resources Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsnot available
FundersStatoil
KeywordsSteam-assisted gravity drainagePetroleum engineeringAsphaltProcess engineeringSolventSteam injectionOil sandsProcess (computing)Environmental scienceParametric statisticsComputer scienceMaterials scienceEngineeringChemistryMathematics

Abstract

fetched live from OpenAlex

Abstract In situ extraction of ultra viscous deposits from the vast bitumen resources in western Alberta requires significant water and energy usage which consequently lead to green house gas emissions. Currently proven steam-based recovery schemes include Cyclic Steam Stimulation, Steamflooding, and Steam Assisted Gravity Drainage (SAGD) processes, which are accompanied by many economical and environmental challenges. Co-injection of solvent with steam is a technology that has the potential to improve the efficiency of steam processes as well as reduce the energy use and CO2 emissions. In recent years, researchers and industry have attempted to further develop the process by conducting fundamental research as well as field pilot trials with varying degrees of success. However, the current level of understanding of the process and knowledge around the fundamental physics and mechanisms involved are not fully satisfactory. In this paper, a parametric simulation study was performed to address the key aspects of the solvent co-injection (SCI) process that contribute to further understanding and development of the process. Simulation observations were verified with experimental evidence where available to support the results and conclusions. Effects of several operational and geological parameters were evaluated on the performance of the SCI process and the relative performance benefits were assessed over normal SAGD operations. These parameters included solvent type, solvent concentration, initial solution gas-oil ratio, relative permeability curves, pay thickness, and presence of a low quality top layer. The results revealed that the optimum solvent should not only be chosen on the basis of mobility improvement capability, but should also consider other operational, phase and flow behavioral and/or geological conditions that are set or present. Higher concentrations of solvents showed more energy saving upsides than rate acceleration benefits. It was also observed that the reservoir steam intake rate is still likely to be the prime performance indicator of the SCI process. In addition, it was found that the potential exists with the SCI process for accessing more resources, particularly below the producer level. Furthermore, steam trap control on the producer seems to be problematic when utilized for SCI simulation. With the current well control capacity of simulators, a higher degree of subcool is likely to be needed to avoid live vapor phase production from the producer.

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.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.897

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.049
GPT teacher head0.283
Teacher spread0.234 · 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