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Record W2317067815 · doi:10.2118/177019-ms

The Progression of Fracture Stimulations in Horizontal Wells Targeting the Montney Formation in the Heritage Field, British Columbia, Western Canada

2015· article· en· W2317067815 on OpenAlexaboutno aff
Kyle Christie, Dave Russum, Megan Fitzmaurice, Aaron Quinton

Bibliographic record

VenueSPE Asia Pacific Unconventional Resources Conference and Exhibition · 2015
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTimelineHydraulic fracturingFracture (geology)Natural gas fieldWell stimulationPetroleum engineeringGeologyTight gasEngineeringGeotechnical engineeringReservoir engineeringPaleontologyArchaeologyNatural gasGeographyPetroleum

Abstract

fetched live from OpenAlex

Abstract Producing unconventional resources through hydraulic fracturing is a continuous learning process where the fracture stimulation design can change throughout the life of the field development. The well completion technique can change because of many reasons such as lessons learned from previous stimulations, new technology, and the heterogeneous nature of the reservoir. This paper focusses on real world data of hydraulic fracture stimulation properties sourced from public databases which is reported from wells drilled in the Heritage Field near Dawson, British Columbia. These wells are licensed as horizontal wells targeting gas production from the Montney Formation. By plotting and mapping the fracture stimulation data collected from the area and focusing on a set of operators with major operations in the area, this paper demonstrates how the fracture stimulation has changed over time and illustrates the design changes of the fracture stimulations for each operator over their own development timeline. This paper also discusses the collaboration initiatives that were implemented as the development progressed to highlight how industry and regulators can work together to responsibly produce the resource most effectively while still maintaining healthy competition.

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 score0.658
Threshold uncertainty score0.843

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.009
GPT teacher head0.213
Teacher spread0.203 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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