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Record W2615647784 · doi:10.2118/185516-ms

A Practical Approach for Scale-Up of Solvent Transport Mechanisms in Post-CHOPS EOR Applications

2017· article· en· W2615647784 on OpenAlexaffabout
J. J. Martinez-Gamboa, MengYuan Wang, Juliana Y. Leung

Bibliographic record

VenueSPE Latin America and Caribbean Petroleum Engineering Conference · 2017
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWormholeEnhanced oil recoveryScalingPorous mediumPermeability (electromagnetism)SolventPetrophysicsPetroleum engineeringComputer scienceMaterials sciencePorosityGeologyChemistryGeotechnical engineeringPhysicsMathematicsGeometry

Abstract

fetched live from OpenAlex

Abstract Cold Heavy Oil Production with Sand (CHOPS) is widely used as a primary non-thermal production technique in thin heavy oil reservoirs in Western Canada and the Orinoco Heavy Oil Belt in Venezuela. Several solvent and hybrid steam/solvent schemes have been proposed to increase the recovery factor from these deposits. Development of the complex wormhole networks renders the scalability of these processes from laboratory measurements to field applications challenging. In this paper, numerical simulation is used to analyze how scaling of solvent transport and dispersion would vary with developed wormhole characteristics. It proposes a practical workflow to a scale up these mechanisms for field-scale simulation. First, a series of mechanistic compositional simulation models at the lab scale is constructed to model a cyclic solvent injection scheme (CSI). These models are calibrated against experimental measurements of solvent diffusion measured in porous media. Next, a set of detailed high-resolution (fine-scale) simulation models, where both matrix and high-permeability wormholes (modeled as fractal networks) are represented explicitly in the computational domain, is constructed to model how the solvent propagates away from the wormholes and into the bypassed matrix. Flows of solvent and oil in the matrix and wormholes are directly simulated. Following this, a dual-permeability approach is adopted to facilitate the scale-up analysis, where wormhole intensity is correlated to shape factor and apparent dispersivity. Characteristics at different averaging scales (i.e. scale-up level) are examined. Field-scale simulation are constructed using average petrophysical and fluid properties extracted from several CHOPS reservoirs in Saskatchewan, which are, to some extent, similar to those found in the Orinoco Belt. The initial conditions in terms of fluid saturations, pressure distribution and wormhole development are representative of those commonly encountered at the end of CHOPS. Solvent transport and mixing in the wormhole networks can be captured by parameters such as shape factor and apparent dispersivity in an equivalent coarse-scale dual-permeability system. Effective dispersivity increases with averaging scale and wormhole intensity. Considering identical surface solvent injection rate, effective dispersivity would enhance oil production and reduce gas production due to an increase in mixing between solvent and oil. Several solvent injection blends are evaluated to maximize recovery efficiency. Field-scale simulations are typically performed with grid block sizes that are much larger than the wormhole scale, and numerical analysis is often performed by arbitrary adjustment of dispersivity. This work offers a practical way to scale up solvent transport mechanisms in post-CHOPS applications. It facilitates more efficient and accurate assessment of solvent transport from lab measurements to field applications. This work serves as a starting point for formulating a systematic workflow to simulate solvent processes in wormhole networks that span over multiple scales.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.927
Threshold uncertainty score0.896

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 head0.012
GPT teacher head0.239
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 teacher head, 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

Citations4
Published2017
Admission routes2
Has abstractyes

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