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Record W1986411058 · doi:10.2118/0313-0134-jpt

Technology Focus: Heavy Oil (March 2013)

2013· article· en· W1986411058 on OpenAlexaboutno aff
Cam Matthews

Bibliographic record

VenueJournal of Petroleum Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleum engineeringCaprockSteam injectionEnhanced oil recoveryAsphaltOil fieldEnvironmental scienceGeologyCarbonateMaterials scienceMetallurgy

Abstract

fetched live from OpenAlex

Technology Focus With continued favorable oil prices, it has been another busy year in terms of heavy-oil discoveries and field developments throughout the world. Both onshore and offshore, many of these new projects are of major scale, requiring significant investment and ranking high in terms of complexity and challenge from a development and recovery standpoint. Many of the commonly used heavy-oil recovery processes are known to introduce substantial physical or chemical changes to the reservoir—some changes produce desirable outcomes, while others can cause highly undesirable effects. This applies generally to both primary- (cold) and enhanced-recovery operations, especially those involving some form of thermal recovery (e.g., various steam-injection methods, in-situ combustion, and electrical heating). The desirable or positive changes may include significant viscosity reduction of the heavy oil or bitumen through thermal or solvent effects; substantial permeability and porosity increases from formation and fluid thermal-expansion effects (e.g., shear dilation), mineral dissolution (e.g., in the case of water or steam injection into carbonate formations), or dendritic wormhole system development in cold-heavy-oil-production-with-sand recovery operations; and favorable changes in wettability (primarily carbonate formations). Understanding and quantifying these positive effects are often crucial to the successful exploitation of heavy-oil reservoirs. Undesirable changes may include significant permeability reduction or plugging (e.g., from asphaltene precipitation, scaling, mineral deposition, shale swelling, or fines migration); large reservoir and overburden deformations and in-situ-stress changes that can lead to well impairments, integrity loss, or caprock failures; and significant CO2 and H2S generation from aquathermolysis, which increases safety and environmental concerns. Two of the papers selected for this feature address physical and chemical effects of steam injection into carbonate and sandstone reservoirs, respectively. The third presents an example of the sophistication inherent to many recent heavy-oil developments in terms of the use of advanced simulation techniques, complex well designs and architectures, and state-of-the-art recovery operation monitoring and control systems. The additional-reading papers reflect the diverse nature of the heavy-oil developments being pursued around the world. They also highlight a few of the numerous innovations and ongoing technology advancements that are helping the industry continually improve recovery performance, lessen environmental effects, and enhance the economic attractiveness of these developments. Recommended additional reading at OnePetro: www.onepetro.org. SPE 157918 SAGD Startup: Leaving the Heat in the Reservoir by M.T.I. Anderson, Suncor Energy, et al. SPE 157865 A Quarter-Century of Progress in the Application of CO2 Immiscible EOR Project in Bati Raman Heavy-Oil Field in Turkey by Secaeddin Sahin, Turkish Petroleum, et al. SPE 159437 An Approach To Model Cold Heavy-Oil Production With Sand (CHOPS) and Post-CHOPS Applications by A. Rangriz Shokri, University of Alberta, et al. SPE 154627 Enhanced Computer-Assisted Model Calibration of Mukhaizna Heavy-Oil Field by Taruna Pillai, Occidental Petroleum, et al.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.350
Threshold uncertainty score0.928

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0040.002
Open science0.0010.001
Research integrity0.0030.001
Insufficient payload (model declined to judge)0.3500.183

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 head0.009
GPT teacher head0.247
Teacher spread0.238 · 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.

Study designNot applicable
Domainnot available
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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