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Record W2790738581 · doi:10.2118/189744-ms

Generalized Approach to Predict k-Values of Hydrocarbon Solvent/Bitumen Mixtures

2018· article· en· W2790738581 on OpenAlex
Bahareh Azinfar, Ali Haddadnia, Mohsen Zirrahi, Hassan Hassanzadeh, Jalal Abedi

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2018
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaSuncor Energy Incorporated
KeywordsAsphaltPropaneSolubilityBoiling pointMethaneHydrocarbonButaneHildebrand solubility parameterSolventDistillationThermodynamicsChemistryVacuum distillationOrganic chemistryMaterials scienceComposite material

Abstract

fetched live from OpenAlex

Abstract A generalized model is presented to calculate the k-values of methane/bitumen, ethane/bitumen, propane/bitumen, and butane/bitumen systems. These data are required for phase behaviour modeling and simulation of solvent-aided bitumen recovery processes. The proposed model is evaluated by comparing the calculated results with the measured experimental k-values. The proposed model provides generalized binary interaction parameters between each hydrocarbon solvent (methane, ethane, propane, and butane) and the defined components in bitumen and calculates the k-values of solvent/bitumen systems. Unlike the existing common approaches, experimental solubility data are not required to tune the model. The boiling point or carbon number distribution of bitumen or heavy oil obtained by simulated distillation (SimDist) test is the only required data to characterize and define the components of heavy oil or bitumen. The SimDist test is a very fast test and much less expensive than the common solubility measurements. This model has been developed based on the experimental fractionation of bitumen. The Athabasca bitumen was experimentally fractioned to four bitumen cuts applying vacuum distillation method and the solubility of solvent in each bitumen cut were measured at wide ranges of temperature and pressure. The measured solubility data of methane, ethane, propane, and butane in each bitumen cut have been used to tune the PR-EoS and the generalized binary interaction parameter coefficients for each solvent and bitumen components have been found. To calculate the k-values of solvent/bitumen mixtures, the bitumen is defined as a mixture of n-alkanes based on simulated distillation results. The properties of n-alkanes have been assumed for each component. Employing the obtained binary interaction parameters in PR-EoS using experimental data of solvent/bitumen cut systems and considering the defined bitumen components as input to the proposed model, the k-values of solvent and any bitumen or heavy oil mixtures are calculated. The validity of the proposed model has been confirmed by calculating the k-values of methane, ethane, propane, and butane with two different bitumen samples with an average deviation of less than 3.0 %. The proposed model predicts the k-values of methane/bitumen, ethane/bitumen, propane/bitumen, and butane/bitumen mixtures without requiring the time and cost intensive experimental PVT data for tuning. Providing the simulated distillation results of any bitumen or heavy oil samples, the k-values of hydrocarbon solvent/bitumen or heavy oil can be calculated in wide ranges of temperature and pressure. The outputs of this model can be directly used as k-values in simulation of solvent-aided thermal recovery processes.

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: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.629
Threshold uncertainty score0.976

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 head0.014
GPT teacher head0.224
Teacher spread0.210 · 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