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Record W2006898610 · doi:10.2118/04-04-03

Simulating Cold Heavy Oil Production With Sand by Reservoir-Wormhole Model

2004· article· en· W2006898610 on OpenAlexaffabout
Y. Wang, Chang‐Hao Chen

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsPetro Geotech (Canada)
Fundersnot available
KeywordsGeomechanicsWormholePetroleum engineeringGeologyPressure gradientResidual oilOil fieldOil productionProduction rateGeotechnical engineeringEnvironmental scienceEngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Continuous sand production and foamy oil behaviour are both believed to be key factors for the enhanced non-thermal fluid production in unconsolidated heavy oil reservoirs in Canada (Alberta and Saskatchewan). The same mechanisms are plausible to be active in similar heavy oil strata in Venezuela (Faja del Orinoco), Oman, and China. Field experience indicates that a fundamental understanding of sand production mechanisms, foamy oil behaviour, pressure gradient changes, and stress changes are essential to successful operations involving massive continuous sanding. Inter-relating these factors requires the coupling of geomechanics and fluid flow processes. An integrated approach incorporating a three-phase, threedimensional, black-oil model coupled with a geomechanics model is introduced in this article. Piping channels (wormholes) are postulated to develop from perforations when pressure gradients exceed the residual cohesion of the sand formations. An elastoplastic constitutive model is extended to describe the reservoir material before seepage forces liquefy and suspend the sand particles at the advancing tips of wormholes. The hemispherical wormhole tip is postulated to propagate as long as a critical tipressure gradient is exceeded. A slurry transport model is usedo describe the flow inside the wormholes. Field data from Frog ake, Alberta are used to validate the model, and it appears that the simulation and the field data are well-matched. Introduction An operational strategy of producing heavy oil with sand has been widely used for a decade in Alberta and Saskatchewan(1–6). However, a poor understanding of this production process, a recovery rate limited to ˜12 - 20% in appropriately screened reservoirs, and difficulties in well management (i.e., repeated workovers) have been the weak points for this technology(7). Improving these aspects, particularly the understanding of this enhanced production mechanisms, can be vital to achieving direct economic benefits. This article introduces a model to address reservoir fluid mobility changes arising from sanding, and pressure drive changes arising from foamy-oil flow. Simulations are based on a general three-dimensional, four-phase, black-oil production model coupled with a slurry flow model (i.e., solid phase is the fourth phase simulated). The latter model represents the wormholes network (or slurry transport zone), and the material balance for solids transport is established. Alternatively, the volumetric sand production and enhanced oil production have also been calculated by a coupled geomechanics model(7–9), in which fluid flow is simultaneously solved with a solid deformation system. Sand production and enhanced oil production contribute to the development of a continuous sanding zone adjacent to the wellbore. In an attempt to simulate cold heavy-oil reservoir production (CP) in Northwestern Canada, where some evidence indicates that large-scale wormholes or high-permeability channels exist inside the reservoir formations(3), a wormhole model is proposed. The proposed model is developed based on the hypothesis that the reservoir formation is poorly consolidated, thus wormhole development under a critical flow velocity or pressure gradient is possible. A detailed discussion on the wormhole model can also be found elsewhere(10). Background Sand production has long been considered undesirable(11, 12), but it is associated with high drawdown and production rates, which are required for faster and more profitable oil production.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.520
Threshold uncertainty score0.972

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.005
GPT teacher head0.190
Teacher spread0.184 · 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
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

Citations20
Published2004
Admission routes2
Has abstractyes

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