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Record W4241458306 · doi:10.2118/2004-277

A Multiphase Flow Approach to Modelling Sand Production Using Finite Elements

2004· article· en· W4241458306 on OpenAlexaff
Richard Wan, Y. Liu, J. Wang

Bibliographic record

VenueCanadian International Petroleum Conference · 2004
Typearticle
Languageen
FieldEngineering
TopicGranular flow and fluidized beds
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMultiphase flowProduction (economics)Computer scienceFlow (mathematics)Petroleum engineeringGeologyMechanicsPhysics

Abstract

fetched live from OpenAlex

Abstract The paper presents an extension of a theoretical and numerical model that the authors have developed previously to address sand production as an erosion problem coupled with hydro- and geo-mechanical effects. A gas phase contribution is added to the governing equations within the framework of mixture theory and multiphase flow in a four component system, namely solids, fluidized solids, oil and gas. It is known that solution gas drive effects play an active role in the physics of sand production in that gas flow tends to increase the propensity of grain particle detachment from the sand matrix and subsequent fluid transport. The finite element formulation and essential algorithmic implementations are discussed. A one-dimensional numerical example of sand production in an oil reservoir undergoing pressure drawdown is presented to provide an understanding of the role of a gas phase in sand production. Introduction A long-standing problem in the oil recovery industry is sand production in unconsolidated oilsands and weak sandstones. Basically, during the oil recovery process, the pumping of the fluid induces huge drag forces that dislodge sand particles from the solid skeleton as the mechanical (inter-granular) strength of the formation is exceeded. The sand fragments are carried intothe wellbore where they can block the flow, damage pumps and pipes, and contaminate the produced fluid. With time, sand production creates cavities in the formation that continually increase in size and eventually lead to wellbore instability and failure. Each year, sanding issues cost the oil industry hundreds of millions of dollars to cope with well repair, pumping device replacement, and environmental issues. There have been various works described in the literature on the study of sand production from both mechanistic and fluid flow standpoints(1–5). For instance, Vardoulakis et al. (1) formulated the sand production problem as an erosional process in which sand grains are dislodged from the sand matrix under the influence of hydrodynamics. In view of solving real initial boundary value problems such as petroleum reservoirs, Wan and Wang (6–7) extended the work to consider a deformable sand matrix. Also, the authors proposed a robust computational model that solves the highly non-linear governing equations dominated by large convection terms within the framework of stabilized finite elements. However, what is missing in all the above-mentioned models is probably the consideration of a gas phase, which could be a driver for sand production mechanisms. For example, the gas phase can modify the dynamics of sand production by way of gas ex-solution. Due to a drop in reservoir pressure, gas comes out of the oil phase in the form of tiny gas bubbles. As a large number of these gas bubbles nucleate, there is formation of an emulsion that increases the mobility of the oil phase. From the solid skeleton viewpoint, additional drag forces act on the sand particles, which ultimately tend to increase the propensity to sand production. This paper extends previous works built on coupled erosion mechanics and hydrodynamics by including the presence of a gas phase in the formulation so as to capture the physics associated to gas ex-solution.

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: none
Teacher disagreement score0.539
Threshold uncertainty score0.999

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 head0.032
GPT teacher head0.228
Teacher spread0.195 · 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

Citations2
Published2004
Admission routes1
Has abstractyes

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