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Record W2088924883 · doi:10.2118/0414-0052-jpt

Renewing Mature Shale Wells Through Refracturing

2014· article· en· W2088924883 on OpenAlexaboutno aff
Trent Jacobs

Bibliographic record

VenueJournal of Petroleum Technology · 2014
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsOil shalePetroleum engineeringGeologyCompletion (oil and gas wells)Mining engineeringShale gasTight oilPaleontology

Abstract

fetched live from OpenAlex

Refracturing opportunities In what may become Act Two for the North American “shale revolution,” some operators are returning to their mature shale wells to refracture, or restimulate, the rock to accelerate the rate of production and enhance the ultimate recovery of trapped hydrocarbons. Refracturing is not a new technique and has been applied for many years in tight rock and vertical wells. But now producers want to apply refracturing to a large inventory of unconventional wells suffering from low production because of ineffective initial completions. Refracturing could also serve as a countermeasure against the characteristically steep decline rates of shale wells. A few years after coming on-stream, most horizontal shale wells produce at a fraction of their initial rate, yet large volumes of oil and gas remain in the rock that could be produced through refracturing. Those involved expect shale well refracturing activity in the United States and Canada to increase steadily as companies figure out how to optimize the mechanics of the operation. Their optimism is based on some early success stories, and the sheer number of possible refracturing opportunities that exist. “Over these next 2 years, the industry will be sharpening their pencils on how and where they are going to (refracture), and then they are going to do it because the potential is tremendous,” said Ibrahim Abou- Sayed, founder of a Houston-based company called i-Stimulation Solutions that offers upstream engineering and consulting services. However, some companies are sitting it out until newer technology overcomes some of the challenges involved with refracturing to make it an easier operation to carry out. Tim Leshchyshyn, founder of a Calgary-based company called FracKnowledge that maintains a database of fracturing information, characterizes refracturing as a “large, complicated topic” that needs more research and development to become a reliable technique. “For many of the candidates that need to be refractured, the industry is short on technology to do so,” he said. “I think there are some tools out there that help, but there is still a lot of room for technology development to make it easy and highly successful.”

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)0.002
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.019
Threshold uncertainty score0.065

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0030.004
Open science0.0010.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0190.004

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 head0.004
GPT teacher head0.205
Teacher spread0.200 · 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 designBench or experimental
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

Citations32
Published2014
Admission routes1
Has abstractyes

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