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Record W1972794471 · doi:10.2118/08-09-40

The Impact of Oil Viscosity Heterogeneity on the Production Characteristics of Tar Sand and Heavy Oil Reservoirs. Part II: Intelligent, Geotailored Recovery Processes in Compositionally Graded Reservoirs

2008· article· en· W1972794471 on OpenAlexafffund
Ian D. Gates, Jennifer Adams, Steve Larter

Bibliographic record

VenueJournal of Canadian Petroleum Technology · 2008
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsOil sandsPetroleum engineeringEnhanced oil recoverySteam injectionAsphaltSteam-assisted gravity drainageEnvironmental scienceThermalGeologyViscosityMaterials science

Abstract

fetched live from OpenAlex

Abstract Compositional and fluid property gradients are common and documented in conventional heavy oilfields and in super heavy oil occurrences such as oil sand reservoirs. In the severely biodegraded oils of both Athabasca and Peace River oil sand reservoirs, highly non-linear vertical and lateral chemical compositional and fluid viscosity gradients are common and have been shown to dramatically impact existing generation recovery processes such as SAGD and CSS. The fluid and geological heterogeneities at a variety of spatial scales in heavy oil and bitumen reservoirs, combined with the dynamic evolution of produced fluids during solvent or thermal recovery, should be integrated into recovery methods tailored to each reservoir which are operated using time-variant production strategies informed by produced fluid composition, flow rates and detailed history matching. We describe here, two new approaches of a new generation of transitional and initial thermal recovery processes that take advantage of mobility gradients: JAGD (J-well and Gravity Drainage) and gSAGD (mobility ratio optimized SAGD), which demonstrate significant improvements in recovery, economics and, thus, carbon dioxide emissions over existing thermal methods. Well configurations tailored to specific reservoir geometries and properties as well as fluid property distributions for primary thermal recovery increase initial production by 50 to 100%. Substantial cost savings are achieved in transitional cold primary to thermal secondary recovery methods (JAGD) by using a production J-well placed below what is initially a CHOPS production well, which is then later used to inject steam, as in SAGD. Three-dimensional reservoir simulations predict 25% more oil recovery with up to a 50% decrease in cumulative steam-oil ratio compared to standard SAGD in an identical reservoir. The JAGD process has many similarities to SAGD, such as steam trap control and potential for low pressure and solvent-assisted operation. Such geotailored processes (processes tailored, operated and optimized to reservoir fluid and geological heterogeneities) are expected to outperform conventional 'off the shelf' well placement designs and operating strategies. Introduction The bulk of the world's petroleum resources are stored in heavy oil and oil sand reservoirs. While some of this resource can be recovered by geotolerant (tolerant of unfavourable geology) recovery processes such as mining, these procedures are only suitable for shallow resources, are very costly and have high carbon dioxide emissions and other environmental penalties. Most of the world's heavy oil and bitumen resources are too deep to mine and so in situ recovery methods predominate. In situ recovery of viscous and poor quality oils currently relies on either high pressure primary production, as in cold heavy oil production, or thermal and/or solvent-based methods to mobilize the oil by reducing its viscosity. Average recoveries from heavy oil and oil sand reservoirs are typically low ranging from 5 to 15% for cold heavy oil production and from 30 to 85% for steam-based in situ processes. However, such processes are not very geotolerant. Also, profit margins are small because of high capital and operational costs.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
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 head0.023
GPT teacher head0.250
Teacher spread0.227 · 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

Citations54
Published2008
Admission routes2
Has abstractyes

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