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Record W2123016866 · doi:10.2118/08-01-52

The Origin, Prediction and Impact of Oil Viscosity Heterogeneity on the Production Characteristics of Tar Sand and Heavy Oil Reservoirs

2008· article· en· W2123016866 on OpenAlexafffund
Steve Larter, Jennifer Adams, Ian D. Gates, Barry Bennett, Haiping Huang

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOil sandsViscosityPetroleum engineeringGeologyEnvironmental scienceMixing (physics)Materials scienceAsphalt

Abstract

fetched live from OpenAlex

Abstract A defining characteristic of heavy and super heavy oilfields is the large spatial variation in fluid properties, such as oil viscosity, commonly seen within the reservoirs. Traditional heavy oil and tar sand exploration and production strategies rely significantly on the characterization of key reservoir heterogeneities and assessment of fluid saturations. While it is important to understand how these properties vary, the spatial distribution of fluid properties can often dominate production behaviour but surprisingly, are usually ignored! Heavy oil and tar sands are formed by microbial degradation of conventional crude oils over geological timescales. Constraints such as oil charge mixing, reservoir-temperature dependant biodegradation rate and supply of water and nutrients to the organismsultimately dictate the final distribution of API gravity and viscosity found in heavy oil fields. Large-scale lateral and smallscale vertical variations in fluid properties due to interaction of biodegradation and charge mixing are common, with up to orders of magnitude variation in viscosity over the thickness of a reservoir. These variations are often predictable and can be integrated into reservoir simulation models in a manner similar to specifying geological heterogeneity. In this work, we describe and illustrate quantitative geological controls on fluid property variations and show how petroleum geochemistry can be used to rapidly produce high resolution fluid property images of tar sand and heavy oil reservoirs. The impact of viscosity variations in a heavy oil reservoir on production depends on recovery method. Numerical thermal reservoir simulations reveal that oil viscosity heterogeneity (i.e. a vertical viscosity profile in the reservoir) lowers the oil production volumes from steam assisted gravity drainage (SAGD) in geologically realistic reservoirs compared to results from equivalent models run with uniform average viscosity profiles. Similar results are found for the cyclic steam stimulation (CSS) process. In cases with viscosity profiles, the relatively high viscosity at the base of the reservoir slows the growth of the steam chambers relative to that in uniform viscosity reservoirs. We also describe how the chemical fluid heterogeneities can be used to predict oil viscosity from well cuttings and/or core or to de-mix produced oils into zonal contributions from different parts of the production well. Introduction There are over six trillion barrels of heavy oil and tar reserves on Earth, but average recoveries remain tantalizingly low (5 to 15% for cold heavy oil production and 40 to 85% for steam assisted gravity drainage operations)(1). We consider that a defining characteristic of heavy and super heavy oilfields is the large spatial variation in fluid properties, such as oil viscosity, commonly seen within the reservoirs. Traditional heavy oil and tar sand exploration and production strategies rely significantly on characterization of key reservoir heterogeneities and distributions of porosity and permeability and assessments of fluid saturations. While these reservoir characteristics are important controlling factors, variations in fluid properties can often dominate production behaviour, but are usually ignored. According to Darcy's law, reservoir permeability and fluid viscosity contribute equally to controlling the net flowrate of oil during production under a given fluid potential gradient.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.986
Threshold uncertainty score0.028

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.013
GPT teacher head0.216
Teacher spread0.203 · 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 designObservational
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

Citations160
Published2008
Admission routes2
Has abstractyes

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