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Record W1983991449 · doi:10.2118/133456-ms

Thirty Years of Gas Shale Fracturing: What Have We Learned?

2010· article· en· W1983991449 on OpenAlexaff
George E. King

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2010
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsOil shalePetroleum engineeringShale gasGeologyFracture (geology)Tight gasNatural gasHydraulic fracturingFracturing fluidMining engineeringEngineeringGeotechnical engineeringWaste management

Abstract

fetched live from OpenAlex

Abstract Although high gas flow rates from shales are a relatively recent phenomenon, the knowledge bases of shale-specific well completions, fracturing and shale well operations have actually been growing for more than three decades and shale gas production reaches back almost one hundred ninety years. During the last decade of gas shale development, projected recovery of shale gas-in-place has increased from about 2% to estimates of about 50%; mainly through the development and adaptation of technologies to fit shale gas developments. Adapting technologies, including multi-stage fracturing of horizontal wells, slickwater fluids with minimum viscosity and simultaneous fracturing, have evolved to increase formation-face contact of the fracture system into the range of 9.2 million m2 (100 million ft2) in a very localized area of the reservoir by opening natural fractures. These technologies have made possible development of enormous gas reserves that were completely unavailable only a few years ago. Current and next generation technologies promise even more energy availability with advances in hybrid fracs, fracture complexity, fracture flow stability and methods of re-using water used in fracturing. This work surveyed over 350 shale completion, fracturing and operations publications, linking geosciences and engineering information together to relay learnings that will identify both intriguing information on selective opening and stabilizing of micro-fracture systems within the shales and new fields of endeavor needed to achieve the next level of shale development advancement.

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)0.008
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.008
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.003
Science and technology studies0.0020.003
Scholarly communication0.0060.011
Open science0.0010.002
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0080.002

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 head0.015
GPT teacher head0.249
Teacher spread0.234 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations718
Published2010
Admission routes1
Has abstractyes

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