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Record W2002885432 · doi:10.2118/02-03-tn

Simulation of Sand Production in Unconsolidated Heavy Oil Reservoirs

2002· article· en· W2002885432 on OpenAlexaboutno aff
X. Yi

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2002
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringOil sandsEnvironmental scienceCohesion (chemistry)Geotechnical engineeringPermeability (electromagnetism)GeologyProductivityMaterials science

Abstract

fetched live from OpenAlex

Abstract Previous research on sand production prediction focused on when sand will be produced during depletion based on some mechanics analyses, but the amount of sand production was ignored. Recently, more and more researchers are focussing on the simulation of heavy oil sand production processes. For an unconsolidated heavy oil reservoir which employs sand production to enhance production, the amount of sand production is of great importance, because too much sand production may cause near wellbore instability, while too little sand production may not maximize well productivity. In view of this, based on both fluid flow modelling and reservoir mechanics concepts, a coupled heavy oil/sand particulate flow/reservoir elasto-plastic deformation model is used to simulate sand production, oil production, and reservoir deformation. With this model, we can determine an optimum flow rate which will not cause near wellbore instability while maximizing well productivity. Introduction Heavy oil sand production as an important production enhancement measure has been used in the primary development of heavy oil reservoirs in Canada for a long time. The production of sand may lead to the change of formation flow-related parameters such as permeability, porosity and mechanical parameters such as cohesion, and it also causes near wellbore stress redistribution. Thus, sand production is a very complicated process involving both fluid flow and geomechanical problems. In order to simulate the effect of sand production and productivity enhancement, simulation of the physical process needs to be done. Because of the long history of cold production, the simulation of cold production is becoming mature and a lot of excellent work has been done by experts in Canada and elsewhere around the world(1–5). Wang(1) developed a model to predict sand production in a heavy oil reservoir in Frog Lake at Lloydminster, Canada. It is believed that reservoir depletion induces stress concentrations around the wellbore, and large drawdown causes a foamy oil zone, in which large drawdown and seepage forces are created which causes sand production. A fully coupled geomechanical, foamy oil flow model was developed. Sand production is assumed to start when the effective radial stress is equal to the tensile strength. Later Wang(2) developed a coupled reservoir-geomechanical model to simulate the enhanced production phenomena in both heavy oil reservoirs (Northwestern Canada) and conventional oil reservoirs (North Sea). It is believed that the production enhancement is contributed:by the reservoir porosity and permeability improvement after a large amount of sand is produced, andby the higher mobility of the fluid due to the movement of the sand particles. Once the reservoir formation yields plastically, loose sand particles can be generated. Sand production has been postulated as a critical condition when the effective radial stress reaches the tensile strength or when the plastic strain reaches the critical plastic strain. Recently, Papamichos et al.(3) and Stavropoulou et al.(4) also provided their model to simulate sand production. Later Papamichos et al.(5) applied successfully this model to interpret sand production from a North Sea reservoir.

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 score0.113
Threshold uncertainty score0.968

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.001
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 head0.010
GPT teacher head0.208
Teacher spread0.198 · 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

Citations11
Published2002
Admission routes1
Has abstractyes

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