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Record W2065172093 · doi:10.2118/2005-214

Finite Element Modelling of Sand Production Under Foamy Oil Flow in Heavy Oil Reservoirs

2005· article· en· W2065172093 on OpenAlexafffundabout
Richard Wan, Y. Liu

Bibliographic record

VenueCanadian International Petroleum Conference · 2005
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersUniversity of Calgary
KeywordsFinite element methodPetroleum engineeringFlow (mathematics)Environmental scienceOil productionProduction (economics)Geotechnical engineeringGeologyEngineeringMechanicsPhysicsStructural engineering

Abstract

fetched live from OpenAlex

Abstract The motivation for studying sand production and foamy oil flow stems from the fact that a number of heavy oil solution gas drive reservoirs in Western Canada and Venezuela have consistently shown anomalously good primary performance, high oil production rates, and high primary recovery factors. We thus propose a mathematical model in which gas exsolution and gas bubble dispersions are described in a macroscopic manner through equations of states and relaxation, whereas sand production is viewed as an erosional hydrodynamics problem. The latter aspect has been investigated at length by the first author in a series of publications[9]–[12]. The formulation of the proposed model leads to a set of highly non-linear governing equations with primary field unknowns such as reservoir pressure, volume fraction of dispersed gas bubbles, concentration of fluidized solid and porosity. The numerical solutions of these equations are challenging and require special treatment such as least-squares finite element techniques in order to ensure stability and accuracy in results. Foamy oil flow with sand production around a wellbore is investigated using the proposed model. The numerical results are very consistent with the physics of gas exsolution and sand production as the reservoir pressure is depleted. For instance, oil production can be improved by increasing the depletion rate in terms of having higher volume fractios of dispersed gas bubble and produced sand. Exsoluted gas in the form of tiny dispersed bubbles has an effect of maintaining a high pressure gradient near the wellbore, hence enhancing the oil recovery factor. Since the pressure is decreased considerably by sand production, the increase in oil production when sand is produced is attributed to a permeability increase, according to Darcy's law. In trying to explain the improvement of oil production in a heavy oil solution gas drive reservoir, we claim that the effect of pressure maintenance caused by dispersed gas bubbles is much more significant than that due to a permeability increase by sand production. Introduction A number of heavy oil solution gas drive reservoirs in Western Canada and Venezuela have shown good performance and it has been observed that well-head samples coming from these reservoirs showed a foamy oil phase [1]. Several possible causes for this anomalous production behaviour of heavy oil reservoirs have been suggested. We will focus on the following two issues in order to introduce the motivations of this paper.Increase of the effective well radius due to geomechanical effects such as sand dilation and the development of wormholes or cavities around the wellbore both naturally present and created by sand production: At the same time, sand dilation results into an increase in absolute permeability due to the production of substantial volume of sand with the oil. Moreover, continued sand movement prevents the formation of pore blockages through fines trapping, asphaltene precipitation, and other nearwellbore mechanical skin effects.Enhancement of oil mobility by the nucleation of a large number of micro-bubbles to lead to the in-situ formation of continuous foam: During production, a non-equilibrium foamy (compressible) oil phase is generated that helps in maintaining the reservoir pressure, and hence provides the driving force for primary 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 score0.156
Threshold uncertainty score0.983

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
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 head0.021
GPT teacher head0.221
Teacher spread0.200 · 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

Citations5
Published2005
Admission routes3
Has abstractyes

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