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Record W2510033592 · doi:10.2118/183636-pa

Physical and Numerical Simulations of Subsurface Upgrading by Use of Solvent Deasphalting in a Heavy-Crude-Oil Reservoir

2016· article· en· W2510033592 on OpenAlexaff
César Ovalles, Estrella Rogel, Hussein Alboudwarej, Art Inouye, Ian Benson, P. Vaca

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2016
Typearticle
Languageen
FieldChemistry
TopicPetroleum Processing and Analysis
Canadian institutionsAcceleware (Canada)
Fundersnot available
KeywordsAsphaltenePropaneLight crude oilSolventSynthetic crudeFraction (chemistry)Petroleum engineeringChemistrySteam-assisted gravity drainageCrude oilSteam injectionPetroleumOil in placeOil sandsHeptaneChromatographyOrganic chemistryShale oilMaterials scienceGeologyAsphaltComposite material

Abstract

fetched live from OpenAlex

Summary Physical and numerical simulations of subsurface upgrading by use of solvent deasphalting (SSU-SDA) at laboratory conditions will be presented with a heavy crude oil and propane as a solvent. In this work, 1D propane-flood experiments were performed in a live-crude-oil-saturated (8.8°API) sand at 120°F and 1,000 psi (7.58 MPa). The results showed oil recovery of 85 wt%, with increases of °API value up to 14°API for the produced crude oil. By use of laboratory-characterization data, a new asphaltene-precipitation model was developed that involves four pseudocomponents (deasphalted oil, heavy fraction, and soluble and solid asphaltenes) and three pseudochemical reactions to numerically simulate the laboratory experiments. [In this text, asphaltenes are the fraction of the crude oil that precipitates in paraffins (propane or heptane) and are soluble in aromatics or chlorine-containing solvents (CH2Cl2)]. History match showed very good agreement between the experimental and calculated oil and gas rates and cumulative oil. Also, reasonably good match between laboratory and theoretical °API value of the produced oils was found throughout the propane-flood experiments. By use of this model, a field-scale well pair in steam-assisted-gravity-drainage (SAGD) configuration was simulated for steam only and two steam/propane cases [10:1- and 1:1-vol% ratio, as measured by liquid volume of solvent per cold water equivalent (CWE) of steam] in a typical heavy-crude-oil reservoir. Results showed accelerated oil production and higher °API values of crude in the presence of propane in comparison with the steam-only case. For the 1:1 steam/propane case, the model predicted that the oil quality improved enough to make the oil transportable through a pipeline. This work finds that SSU-SDA continues to show promise as a viable oil-recovery and -upgrading process when the complex downhole physics is modeled. It is predicted that higher propane/steam ratios are needed during SSU-SDA compared with historical solvent-based enhanced-oil-recovery field pilots to capture both the oil-recovery and -upgrading benefits of this process.

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)0.000
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 score0.013
Threshold uncertainty score0.026

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.035
GPT teacher head0.299
Teacher spread0.264 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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