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Record W4247883126 · doi:10.2118/2002-170

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

2002· article· en· W4247883126 on OpenAlexaffabout
Y. Wang, Chang‐Hao Chen

Bibliographic record

VenueCanadian International Petroleum Conference · 2002
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsPetro Geotech (Canada)
Fundersnot available
KeywordsPetroleum engineeringWormholeProduction (economics)Environmental scienceOil sandsOil productionGeologyMaterials sciencePhysics

Abstract

fetched live from OpenAlex

Abstract Continuous sand production and foamy oil behavior are both believed to be key factors for the enhanced nonthermal fluid production in unconsolidated heavy oil reservoirs in Canada (Alberta and Saskatchewan). The same mechanisms are likely to be active in similar heavy oil strata in Venezuela (Faja del Orinoco), Oman, China (Bohai Bay), and elsewhere. Field experience indicates that fundamental understanding of sand production mechanisms, reservoir fabric alteration, foamy oil behavior, pressure gradient changes, and stress changes are key to successful operations involving massive continuous sanding. Inter-relating these factors requires coupling of geomechanics and fluid flow processes. An integrated approach incorporating a three-phase, three-dimensional black-oil model coupled with a simplified slurry transport 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. An elastoplasticmodel is imposed near the wormhole tip 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 tip pressure gradient is reached. A material balance equation is established between solids enter into each wormhole and those correspond to the enlarged wormhole. Field data from Frog Lake, Alberta are used to validate the model, and it appears that the simulation can match the field data remarkably well. Introduction An operational strategy of producing heavy oil with sand has been widely used for a decade in Alberta and Saskatchewan1–7 and the productivity can be significantly improved as a high mobility near a wellbore or inside the reservoir formation may be achieved after sand production. However, 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. Improving these aspects, particularly the understanding of this enhanced production mechanisms, can be vital to 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 to 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 the enhanced oil production have also been calculated by a coupled geomechanics model8–10. No wormhole network is postulated in these models, sand production and the enhanced oil production are contributed by the development of a continuous sanding zone adjacent to the wellbore. Attempting to simulate the cold heavy-oil reservoir production (CP) in Northwestern Canada, where some evidences indicated that large-scale wormholes or high-permeability channels exist inside the reservoir formations4, a wormhole model is proposed. The proposed model is developed based on the hypothesis that the reservoir formation is poorly consolidated thus wormholes development under a critical flow velocity or pressure gradient is possible.

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 score0.058
Threshold uncertainty score0.830

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 head0.027
GPT teacher head0.239
Teacher spread0.212 · 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

Citations3
Published2002
Admission routes2
Has abstractyes

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