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Record W2047824050 · doi:10.2118/04-09-05

Mathematical Modelling of Transport Processes During Wellbore Heating in Tar Sand and Bituminous Reservoirs

2004· article· en· W2047824050 on OpenAlexaff
Kulada Karmaker, Brij Maini, Ayodeji A. Jeje

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2004
Typearticle
Languageen
FieldEngineering
TopicEnhanced Oil Recovery Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsOil sandsAsphaltPetroleum engineeringPermeability (electromagnetism)Steam-assisted gravity drainageSteam injectionViscosityGeologyGeotechnical engineeringMaterials scienceChemistryComposite material

Abstract

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Abstract Enhanced oil recovery (EOR) processes sometimes involve wellbore heating by means of electromagnetic radiation, electrical heating, or steam injection. When a wellbore is heated in tar sands and bituminous reservoirs, many thermo-physical properties of the bitumen and rock matrix can change significantly and thereby alter the fluids and heat flow behaviour. This paper presents the development of a mathematical formulation that describes the flow behaviours of heat and fluids around a wellbore during the initial period of heating in tar sands and bituminous reservoirs. The model shows that the transient temperature distribution is not affected significantly by convection. However, the pressure distribution exhibits an abnormally high pressure build-up resulting from the relative thermal expansion of in situ bitumen in the region where the temperature is slightly higher than the reservoir temperature. The mathematical model can be used to obtain predictions of pressure-temperature distributions around heated wellbores, well communication time required in the steam assisted gravity drainage (SAGD) process, and well stimulation and absolute permeability improvement resulting from progressive shear dilation in low permeability bitumen and tar sand reservoirs. Introduction The recovery of oil from tar sands and bitumen reservoirs is difficult because of the very high viscosity at reservoir conditions. For instance, the in situ viscosity of bitumen in the Athabasca oilsand deposit has been estimated to be more than 106 cp(1). The basic mechanism of thermal recovery processes is to increase the reservoir temperature and thereby reduce the oil viscosity to make it mobile. Besides the viscosity, other physical properties of the bitumen and rock matrix can also change considerably during formation heating. Based on a study with geomechanical/thermal reservoir simulation, Collins et al.(2) have shown that the injection of high pressure steam into the oil sands during the SAGD process induces stress changes, and these cause an increase in permeability. Laboratory test results have shown that the structural damage to rocks may result from differential expansion of mineral constituents(3). In tight reservoirs, localized fractures might be created due to the expansion of fluids in dead-end pores. An increase in temperature affects reservoir production mechanisms through changes in the mobility of fluids and interfacial tension. The changes in fluid mobility occur not only due to the expected dependence of fluid viscosity on temperature but also due to changes in the rock properties. It is important while heating a wellbore in a reservoir with a certain type of lithology, to know whether or not such changes in formation physics are likely to occur. In many circumstances, such thermo-physical changes could also induce formation damage and alter the reservoir flow behaviour irreversibly. The predictions of reservoir flow behaviour and parametric changes for the system require an accurate model for temperature and pressure distributions in the formation during heating. However, such predictive models are still lacking in literature. Particularly in low permeability bituminous reservoirs, the pressure and temperature distributions are interdependent due to the fact that:the thermal expansion of bitumen is an order of magnitude greater than that of reservoir rock matrix; and,the viscosity f bitumen changes dramatically with temperature.

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)0.001
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: none
Teacher disagreement score0.986
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
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 head0.008
GPT teacher head0.192
Teacher spread0.184 · 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".

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Citations0
Published2004
Admission routes1
Has abstractyes

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