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Record W3013928842 · doi:10.2118/0420-0059-jpt

Technology Focus: Heavy Oil (April 2020)

2020· article· en· W3013928842 on OpenAlexaffabout
Tayfun Babadagli

Bibliographic record

VenueJournal of Petroleum Technology · 2020
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsSteam-assisted gravity drainagePetroleum engineeringProcess engineeringMicroemulsionEnhanced oil recoverySteam injectionEnvironmental scienceChemistryWaste managementChemical engineeringOil sandsMaterials scienceEngineering

Abstract

fetched live from OpenAlex

Technology Focus Reviewing a myriad of papers presented at different conferences during the past year and attending some of those to hear the oral presentation versions, I can group the current trends in heavy-oil operations and research into two major categories: Process optimization and use of chemicals as additives to steam and water. Here are some highlights from those categories. Several papers focused on purely computational methods—including multi-objective optimization and proxy models—to optimize the steam-assisted gravity drainage (SAGD) method with specific attention on preferential steam allocation. Real-life examples of these applications consist of inflow- and outflow-control devices and steam splitters developed for diverting the steam to preferential zones, thus minimizing the use of steam. As an indirect meth-od to provide supplemental data for the optimized solutions, seismic data were also used. Applications of seismic waves include the depiction of steam-chamber development and monitoring wellbore integrity. Considering that many SAGD operations are reaching maturity, these applications will help maintain the steam/oil ratios at low levels, therefore extending the life of the processes. In another group of studies, chemical additives were tested at the laboratory scale to improve cold (microemulsion formulations) and thermal (to reduce steam/oil ratio) heavy-oil recovery. Examples of newly trialed chemicals include ionic liquids, foam additives, anionic and hydrophobic surfactants, switchable-hydrophilicity tertiary amines, nano-fluids (dispersed SiO2 and ZrO2), ethers, alcohols, and chelating agents as steam additives and microemulsion formulations using SiO2, anionic and nonionic surfactants as well as deep eutectic solvents for cold production. Although most of the listed optimization studies (specific examples reported include the Mackay River, Firebag, and Surmont fields) and chemical tests were done for SAGD conditions (especially at the mature level), these new technologies and applications will be a guidance to the other types of steam operations (cyclic steam injection and steamflooding) using horizontal injectors and producers. In addition to the discussed studies and practices, a plethora of field-scale activities from the Middle East, far east Asia, and the Americas have been reported on consistently. Most of those are early-stage practices or pilots, but a few are related to mature heavy-oil recovery operations. Examples include Kuwait (data collection and development plans for new heavy-oil potentials), Oman (steam management surveillance in the Mukhaizna field), Brazil (development options for the offshore Atalanta field), Venezuela (ongoing operations in the Orinoco belt), Alaska (field-scale polymer-flooding test), China (practices in the Liaohe and Bohai Bay fields), Canada (cold production using methane/ propane mixture and electrical heating pilots in Alberta), Colombia (applications of autonomous inflow-control devices in the Rubiales field), and Indonesia (identification of rod-pump failures in the Duri field). Recommended additional reading at OnePetro: www.onepetro.org. SPE 196072 Laboratory and Field Testing of a Steam-Limiting Flow-Control Device Developed for Thermal Applications by Marty Lastiwka, Suncor Energy, et al. SPE 197321 Surveillance Approach of Steam Management in a Heavy-Oil Reservoir, Oman by Hajer Al Mahruqi, Occidental Petroleum SPE 198697 Effective Reservoir Management With Flow-Control Devices for SAGD Producer Wells in the Mackay River by Giuseppe Rosi, RGL Reservoir Management, 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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.783

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
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 head0.010
GPT teacher head0.243
Teacher spread0.233 · 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 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

Citations0
Published2020
Admission routes2
Has abstractyes

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