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Record W2586436052 · doi:10.2118/185010-ms

Development of a Non-Equilibrium Pore Scale Reservoir Simulator

2017· article· en· W2586436052 on OpenAlexafffundabout
Bita Bayestehparvin, Jalal Abedi, S.M. Farouq Ali

Bibliographic record

VenueSPE Canada Heavy Oil Technical Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsSteam injectionDissolutionPetroleum engineeringSolventAsphaltDrop (telecommunication)Reservoir simulationThermodynamicsWork (physics)ThermalHeat transferMaterials scienceChemistryGeologyMechanical engineeringEngineeringOrganic chemistryPhysics

Abstract

fetched live from OpenAlex

Abstract Solvent injection alone, or in combination with steam, is currently receiving considerable attention as an emerging in-situ oil sands technology in Alberta. The goal of solvent and steam assisted recovery processes is to reduce energy consumption and greenhouse gas emissions over the use of steam alone. Pilot studies in the field indicate a significant difference between reservoir simulation predictions, and field performance and previous studies have demonstrated that the assumption of instantaneous equilibrium is not valid for solvent-bitumen interactions and can introduce significant inaccuracies to the modelling results. In addition to assuming non-equilibrium, this study seeks to determine the mechanism of solvent dissolution in bitumen and the expected improvement in oil recovery, if any, when a solvent is injected with steam. The unique aspect of this work is that instantaneous phase equilibrium is not assumed, as is typical of numerical simulations for solvent-steam applications. Partial equilibrium was not based on an empirical factor or concept. Rather, this study bases phase equilibrium on an analytical model of the dissolution and mobilization of a drop of bitumen inside a pore, by solvent and heat. The analytical solution indicates that the time required for a drop of bitumen to mobilize towards the production well is at least three times greater for solvent via diffusion and dispersion than by heat conduction. The analytical solution was developed for solvent injection, heat injection, and co-injection for several boundary conditions. The single drop model is built into a new thermal compositional simulator developed for this study. Thermal solvent injection processes were investigated for non-equilibrium phase behaviour. The results were compared with the case of instantaneous equilibrium, showing the reason for the previous lack of accurate predictions for solvent injection in oil sands reservoirs. The results and extensions of this work will be of interest in heavy oil production because they serve to explain the unexpected performance and frequent lack of success of these processes. This model is capable of precisely predicting solvent injection concentration, flow rate, and recovery at the field scale, making it possible to determine whether or not solvent injection is appropriate in any given situation.

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: Methods · Consensus signal: Methods
Teacher disagreement score0.008
Threshold uncertainty score0.017

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 studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.020
GPT teacher head0.258
Teacher spread0.239 · 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
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

Citations5
Published2017
Admission routes3
Has abstractyes

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