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Record W4242712922 · doi:10.2118/2001-051-ea

Simulation of Sand Production in Unconsolidated Heavy Oil Reservoir

2001· article· en· W4242712922 on OpenAlexaboutno aff
X. Yi

Bibliographic record

VenueCanadian International Petroleum Conference · 2001
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringProduction (economics)Oil productionGeologyReservoir simulationOil sandsEnvironmental scienceGeotechnical engineeringMaterials science

Abstract

fetched live from OpenAlex

Abstract Previous researches on sand production prediction were focusing on when sand will be produced during depletion basing on some mechanics analyses but the amount of sand production was ignored. Recently more and more researches are focusing on the simulation of heavy oil sand production processes. For 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 formation collapse while too less sand production may not maximize well productivity. In view of this, based on both fluid flow modeling 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 formation collapse 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. Because the production of sand may leads to the change of formation flow parameters such as permeability and porosity and mechanical parameters such as cohesion. It also causes near wellbore stress redistribution. So, 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. Wang [1] first developed a model to predict sand production in heavy-oil reservoir in Frog Lake and Lloydminster, Canada. It is believed that reservoir depletion induces stress concentration around the wellbore and large drawdown causes foamy oil zone, in which large drawdown and seepage force are created and causes sand production. A fully coupled geomechanical, foamy oil now model was developed. Sand production is assumed to start when the effective radial stress is equal to the tensile strength. Later Wang [2] also 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 (I) by the reservoir porosity and permeability improvement after a large amount of sand is produced, and (2) by higher mobility of the nuid due to the movement of the sand particles. Once the reservoir formations yield 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 similar models to simulate sand production. Later Papamichos et al [5] applied 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 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.958
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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
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

Citations0
Published2001
Admission routes1
Has abstractyes

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