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Record W2916240377 · doi:10.2118/0316-0080-jpt

Technology Focus: Heavy Oil (March 2016)

2016· article· en· W2916240377 on OpenAlexaffabout
Tayfun Babadagli

Bibliographic record

VenueJournal of Petroleum Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsProcess engineeringEnvironmental scienceEnhanced oil recoveryPetroleumProduction (economics)Oil productionComputer scienceSteam injectionWaste managementBiochemical engineeringPetroleum engineeringEngineeringChemistry

Abstract

fetched live from OpenAlex

Technology Focus The next few years will be quite challenging for costly heavy-oil production because of the recent slump in oil prices. To sustain its current level, which comprises roughly 12–15% of total production, process efficiency should be enhanced. Cost control is one of the ways to achieve this. I selected a few example papers on heavy-oil production by improved waterflooding at a relatively low cost (SPE 169490 and SPE 174491) and on practical management applications to enhance the primary production (SPE 177914 and SPE 169396) as additional-reading suggestions. An alternative for improving the efficiency of “flowable-heavy-oil” recovery is to use traditional chemicals (typically polymer/surfactant/alkaline combinations). SPE 169715, as synopsized in the next pages, covers the Canadian experiences of chemical applications. Ongoing thermal applications cannot be stopped because of technical reasons, but they need to be optimized to reduce the cost or be supported by auxiliary methods to improve recovery. The use of chemicals is one method to achieve the latter. SPE 176703 was selected as an interesting field application of steam-stimulation efficiency improvement by better sweep and water shutoff using gels. There are, on the other hand, cases that entail more-advanced and -expensive applications. Without “solventing” or heating them, no production is possible to recover extra-heavy oil or bitumen. Another work selected as a synopsis paper for this issue (SPE 175414) reports the pilot results of a solvent-enhanced steam application. Despite its extreme cost, this combination is inevitable in the case of bitumen, especially in un favorable geologies such as carbonates. One of the reading suggestions is SPE 169035, which reports the initial observations on a steam-heating pilot application in such a geological environment. Before closing, I would like to bring your attention to two critical points as we experience one of the more severe economic downturns in the oil industry. First, research on technology for heavy-oil recovery must go on one way or another. A great deal of effort has been devoted to new applications such as solvent use with thermal processes, electromagnetic heating, and nanomaterials at the laboratory or field scale during the last decade. They should be concluded for the next expected cycle of higher oil prices. Second, cost-effective solutions should be sought and materialized immediately to sustain many ongoing heavy-oil (especially thermal) operations. One option is to search for cost-effective chemicals. Selection of these temperature-resistant chemicals requires additional research efforts to carry the results over to the field for cost-effective applications as quickly as possible. Similar chemical-selection processes may be considered to enhance ongoing waterflooding applications for relatively “lighter” heavy-oil cases. JPT Recommended additional reading at OnePetro: www.onepetro.org. SPE 169035 Design, Implementation, and Early Operating Results of Steam-Injection Pilot in Already-CO2-Flooded, Deep, Heavy-Oil, Fractured Carbonate Reservoir of Bati Raman Field, Turkey by Secaeddin Sahin, Turkish Petroleum Corporation, et al. SPE 169396 Boscan Field—Highly Deviated Wells To Avoid Water Production in a Heavy-Oil Reservoir Influenced by an Active Aquifer by R. Plaza, Petroboscan, et al. SPE 169490 Alkali-Metal Silicides: A New Material for Heavy-Oil Production Processes by Paul H. Krumrine, SiGNa Chemistry, et al. SPE 174491 Successful Application of Hot-Water Circulation in the Pelican Lake Field: Results and Analyses of the E29 Hot-Water-Injection Pilot by Kirk Duval, Cenovus Energy, et al. SPE 177914 Successful Chemical Water- Shutoff Treatment in an Omani Field Heavy-Oil Well by Guillaume Dupuis, Poweltec, 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 score0.873
Threshold uncertainty score0.570

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
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 head0.006
GPT teacher head0.217
Teacher spread0.211 · 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

Citations0
Published2016
Admission routes2
Has abstractyes

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