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Record W3166486804 · doi:10.2118/0421-0048-jpt

Technology Focus: Heavy Oil (April 2021)

2021· article· en· W3166486804 on OpenAlexaffabout
Tayfun Babadagli

Bibliographic record

VenueJournal of Petroleum Technology · 2021
Typearticle
Languageen
FieldEngineering
TopicOil and Gas Production Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPetroleum industryNatural resource economicsGreenhouse gasFossil fuelOil reservesPetroleumBusinessEnvironmental scienceEngineeringWaste managementEconomicsEnvironmental engineeringChemistryGeology

Abstract

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After serving two terms for a total of 6 years, my time writing this column is coming to an end. This issue of JPT marks my last opportunity to share my thoughts, recap my observations, and make note of some final touch-ups to the research conducted over this 6-year period with regard to recent heavy oil practices. Here are some highlights to keep in our minds over the coming years. Despite all the recent negative and serious changes affecting the whole world and our industry, life goes on and we will increasingly be needing energy. One should recall that statistics predict oil will continue to be the main source of energy for the next 2 decades, with heavy oil constituting a great portion of that. That means that, while the oil industry is going through unprecedented and even unpredictable economic downturns, the status of heavy oil is still unquestionable. However, we have to face the fact that this energy should be tapped in a cheap, clean, and sustainable way. The best aspect of this effort is that heavy oil technologies have been established and tested over a long period of time, unlike other unconventional resources. Lowered steam consumption, down to zero if possible, has been under consideration to minimize the emission of greenhouse gases (GHGs) while simultaneously producing heavy oil. This green effort leads us to nonsteam techniques such as the use of water with chemicals (mainly polymer) and noncondensable gases and certain unconventional methods such as solvent injection and electromagnetic heating, the latter being unavoidable especially for extraheavy oil and bitumen. These areas have been critically considered by researchers and practitioners with a considerable number of applications existing at the field scale. At the same time, the oil industry must deal with mature steam projects in the near future. We have accumulated so much heat energy over the decades, yet a substantial amount of oil remains in these reservoirs. What can be done to reuse this energy? Can we recover different forms of energies using methods with no GHG emission? The current practices encountered in field-scale operations to improve the heavy oil recovery in mature steam applications use noncondensable gases; mainly, these techniques serve to pressurize steam-assisted gravity drainage wells, improve sweep and microscopic displacement by adding chemical additives to the steam (or hot water), and re-engineer well designs (flow control for efficient heating and sweep). My final example highlighting new practices is the increasing trend of offshore heavy oil practices. Of particular interest is polymer injection through vertical and horizontal wells and pilot steam applications, methods that are effective even if they occur at the pilot stage of the process. Recommended additional reading at OnePetro: www.onepetro.org. SPE 199947 - Enhanced Oil Recovery in Post-Cold Heavy Oil Production With Sand Heavy Oil Reservoirs of Alberta and Saskatchewan Part 2: Field Piloting of Cycling Solvent Injection by Gokhan Coskuner, Consultant, et al. SPE 199925 - Scalable Steam Additives for Enhancing In-Situ Bitumen Recovery in SAGD Process by Armin Hassanzadeh, Dow, et al. SPE 199927 - The Myth of Residual Oil Saturation in SAGD - Simulations Against Reality by Subodh Gupta, Cenovus Energy, 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 score0.875
Threshold uncertainty score0.804

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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".

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Citations0
Published2021
Admission routes2
Has abstractyes

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