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Record W2583656358 · doi:10.2118/0616-0028-jpt

EOR-For-Shale Ideas to Boost Output Gain Traction

2016· article· en· W2583656358 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2016
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsOil shalePetroleum engineeringTight oilEnhanced oil recoveryPetroleumWork (physics)Shale oilCompletion (oil and gas wells)Unconventional oilEngineeringGeologyNatural resource economicsWaste managementEconomicsMechanical engineering

Abstract

fetched live from OpenAlex

Low oil prices may not be the biggest threat to the long-term sustainability of the North American shale business. Some are more concerned about the low recovery rates of horizontal shale wells, estimated to be about 7% on average— far short of the 40% achieved through primary and secondary (waterflooding) production in conventional reservoirs. Refracturing has been touted as the next big thing to improve ultimate recovery, but such operations remain relatively expensive and may only temporarily reset production to initial rates once or twice in a well’s life. To see long-term results and a doubling or tripling of current recovery rates, a number of experts say enhanced oil recovery (EOR) technologies must be developed to work in tight shale reservoirs. And due to persistently low natural gas prices, current efforts appear to be exclusively focused on oil and condensate producing wells. It is early days for this area of EOR research. There is no consensus on which approaches will work best, how much they may cost, what the most pressing challenges are, or exactly when an EOR operation should begin. “Our understanding is really small,” said Todd Hoffman, an assistant professor of petroleum engineering at Montana Tech University. “We’re coming from the conventional world where ‘this’ is how we did EOR and we may just have to throw all that out.” Hoffman is one of several researchers trying to figure out how EOR methodologies can be adapted or reinvented for the oil-rich shale fields of North Dakota, Texas, and Canada. One of the most popular ideas being studied is a huff-and-puff approach that uses a single horizontal well to alternate between producing oil and injecting natural gas or CO2 to re-pressurize the reservoir and displace oil. Another idea is to apply continuous injections into one well and use an offset well as the producer. Others are looking into flooding the wells with surfactants and possibly acid to stimulate production. In May, EOG Resources laid claim to the first economic demonstration of an injection-based EOR technology for tight oil in the US. The company said the development may have long-term production benefits and is competitive with drilling new wells. But other than making it clear that the process has been successful and uses dry gas produced in the same field, EOG is withholding key operational details such as whether it involves the huff-and-puff technique or continuous injections. A number of other innovative shale producers including Statoil, Nexen Energy, Continental Resources, and Marathon Oil have also either funded research or are known to be running pilots, but have not made their results public. As most shale producers remain silent about their EOR efforts, there are a growing number of technical papers being published by university petroleum departments and reservoir engineering consultants. They are using computer models and corefloods to test their theories and have produced promising numbers that suggest there may be several practical ways to implement EOR strategies for shale.

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)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.044
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0050.012
Open science0.0020.004
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0440.014

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 head0.014
GPT teacher head0.270
Teacher spread0.256 · 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.

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

Citations16
Published2016
Admission routes1
Has abstractyes

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