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Record W2916465752 · doi:10.2118/0311-0086-jpt

Technology Focus: Heavy Oil (March 2011)

2011· article· en· W2916465752 on OpenAlexaboutno aff
Cam Matthews

Bibliographic record

VenueJournal of Petroleum Technology · 2011
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsnot available
Fundersnot available
KeywordsPetroleumOil reservesPetroleum engineeringUnconventional oilChinaFossil fuelCrude oilOil sandsEnvironmental scienceGeologyEngineeringWaste managementGeographyArchaeologyPaleontology

Abstract

fetched live from OpenAlex

Technology Focus With interest in heavy oil continuing to grow rapidly worldwide, SPE has responded to the need for increased dissemination of technical information related to the exploitation of these unconventional hydrocarbon resources. In one new initiative, SPE joined forces with the Canadian Society for Unconventional Gas to organize the 2010 Canadian Unconventional Resources and International Petroleum Conference in Calgary, in October. SPE also organized heavy-oil advanced-technology workshops and expanded heavy-oil content of regularly scheduled conferences in several Middle East countries, Russia, and China in 2010, signaling the growing importance of heavy-oil reserves to these regions. I often get asked: “What is the best recovery method to use for this heavy-oil reservoir?” The answer can vary substantially, depending on the reservoir setting and fluid properties. Therefore, as with any new field, the process of determining the preferred development strategy begins with a detailed and accurate characterization of the reservoir and fluid properties. However, in the case of heavy oil, unanticipated technical challenges are encountered routinely in accomplishing this basic exercise. For example, while tools and equipment are readily available and proved for capturing live downhole-fluid samples in conventional-oil reservoirs, this is not the case for heavy oils, especially those with in-situ viscosities exceeding several hundred centipoises, let alone thousands or tens of thousands of centipoises. Many heavy-oil reservoirs consist of unconsolidated sand formations, which also makes it difficult to acquire either fluid or undisturbed core samples and then to obtain accurate permeability and porosity data. For thermal projects, determining accurate rock and fluid properties as a function of temperature is important but is not an easy task. Specialized equipment and field-sampling/laboratory-testing techniques along with ample experience typically are required to obtain reliable data. It is also worth noting that the trend over the past few years has been to give much more attention during initial development planning to the sequencing of different enhanced-oil-recovery (EOR) strategies to maximize recovery from heavy-oil reservoirs. On the basis of the many papers written this past year related to polymer flooding of heavy-oil reservoirs, it appears that recent technological advancements and application successes have led to this becoming a viable EOR alternative for a wide range of in-situ fluid viscosities. Finally, the need for conducting pilot operations to establish actual reservoir and well performance and to validate expectations cannot be emphasized enough. Heavy Oil additional reading available at OnePetro: www.onepetro.org SPE 137639 “Thermal Properties of Formations From Core Analysis: Evolution in Measurement Methods, Equipment, and Experimental Data in Relation to Thermal EOR” by Y.A. Popov, Schlumberger, et al. SPE 134849 “In-Situ Heavy-Oil Fluid-Density and -Viscosity Determination Using Wireline Formation Testers in Carbonates Drilled With Water-Based Mud” by Ridvan Akkurt, Saudi Aramco, et al. SPE 136665 “Viscosity of Foamy Oils” by A.B. Alshmakhy, SPE, Weatherford, 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 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: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.621
Threshold uncertainty score0.665

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.013
GPT teacher head0.217
Teacher spread0.204 · 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 designOther design
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

Citations1
Published2011
Admission routes1
Has abstractyes

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