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Record W4200342982 · doi:10.2118/208615-pa

Dimethyl Ether as a Novel Solvent for Bitumen Recovery: Mechanisms of Improved Mass Transfer and Energy Efficiency

2021· article· en· W4200342982 on OpenAlexaff
Min Yang, Maojie Chai, Rundong Qi, Zhangxin Chen, Linyang Zhang, Xinran Yu

Bibliographic record

VenueSPE Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMass transferButaneSolventAsphaltDimethyl etherMaterials scienceChemical engineeringEnhanced oil recoveryChemistryPetroleum engineeringOrganic chemistryChromatographyComposite material

Abstract

fetched live from OpenAlex

Summary A solvent-based thermal recovery process has the advantages of low capital expenditure, less energy consumption, and less greenhouse gas emission. Dimethyl ether (DME), as a renewable solvent, has been considered as a novel additive in the thermal bitumen recovery process. Being soluble in both water and oil phases, DME has the potential to enhance mass transfer and improve oil production. In this work, a phase behavior model of the DME-bitumen-water system is first developed considering DME partitioning between oil and water. A field-scale numerical simulation model with fine gridblocks is developed to investigate the heat and mass transfer mechanisms between DME and bitumen in the interface of a DME vapor chamber. The numerical model is validated with physical experiment results. The close agreement between measured and simulated production profiles indicates that the mechanisms are adequately captured. Meanwhile, various simulation scenarios are performed to evaluate the production performance and the energy efficiency, which is defined as the energy/oil ratio. It is found that the oil production rate in DME injection is 15% higher than that in butane injection at the early stage of production. The solvent penetration depth in DME injection is larger than that in butane injection. This is attributed to the enhanced mass transfer between DME and bitumen caused by the high diffusion of DME in the water phase and preferential partitioning of DME into the oil phase. Furthermore, energy consumption in the warm DME injection process is 48% less than that in warm butane injection and 75% less than that in steam-assisted gravity drainage (SAGD). This is because DME injection can be operated at a lower-temperature condition, leading to less energy transferred to heat reservoir rock/fluids and less heat loss to over/underburden. Therefore, DME is proved to be a technically promising and environmentally friendly solvent to enhance bitumen recovery. The DME-based thermal recovery technique exhibits superior advantages in unlocking poor-quality reservoirs, especially in high water saturation reservoirs and thin reservoirs.

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)0.000
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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.630
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.008
GPT teacher head0.222
Teacher spread0.214 · 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 designBench or experimental
Domainnot available
GenreMethods

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

Citations24
Published2021
Admission routes1
Has abstractyes

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