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Record W3011471618 · doi:10.2118/199927-ms

The Myth of Residual Oil Saturation in SAGD - Simulations Against Reality

2020· article· en· W3011471618 on OpenAlexaffabout
Subodh Gupta, Simon Gittins, Javad Oskouei, Samuel Quiroga, Joel Christiansen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsCenovus Energy (Canada)
Fundersnot available
KeywordsRelative permeabilityResidualSaturation (graph theory)Permeability (electromagnetism)Residual oilPetroleum engineeringReservoir simulationOil fieldSoil scienceGeologyMathematicsMechanicsChemistryAlgorithmGeotechnical engineeringPhysicsPorosity

Abstract

fetched live from OpenAlex

Abstract Understanding how SAGD works is important as most of the in-situ production of bitumen in Alberta is using SAGD technology. A key parameter in simulation-based SAGD performance prediction is the residual oil saturation, which usually in SAGD simulations is a fixed number based on typical two-phase end-point relative permeability curves, combined with Stone's model to yield oil's relative permeability. Therefore, in simulations residual oil saturation (Sor) never goes below a certain number (typically 0.15-0.2). In reality, based on retrieved cores, there is evidence of Sor continuously decreasing to as low as 0.03 and below. This paper explains the reason for this discrepancy and suggests modifications to the relative permeability model for more realistic simulations. Observations from retrieved cores suggest the residual oil saturation is dependent on the length of SAGD operation. This is also supported by Cardwell-Parsons correlation albeit for a two-phase system. The discrepancy between the current simulation results and actual observations point to the inadequacy of the relative permeability models containing the end points where krog and krog vanish at abscissas Sw, Sg less than 1. In this work effect of extending mobility of the oil phase all the way to Sw, Sg = 1 and krw, krg all the way to Sw, Sg = 0 is examined. First, a column drainage is numerically simulated, and relevant curve parameters are retrieved by comparing results against field results. These curves are then extended to simulate SAGD. The results show that the modified relative permeability curves mimic the observed behavior better with residual oil phase saturation progressively decreasing with time, rather than remaining constant after a certain point. Improved correlation with observed saturations obtained using modified curves suggest that the fixed residual saturations resulting from current models are a myth. When extended to solvent aided processes, the model reinforces the benefit of solvent additives even further. To the best of the authors’ knowledge most current SAGD simulations are done in a manner resulting in a fixed residual oil saturation. The proposed method presents an opportunity for better prediction of oil saturation with time and location and the corresponding performance of the SAGD 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)0.002
metaresearch head score (Gemma)0.011
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 score0.009
Threshold uncertainty score0.017

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.245
Teacher spread0.226 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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