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Record W2017419126 · doi:10.2118/96899-ms

Assessment and Development of the Dry Horseshoe Canyon CBM Play in Canada

2005· article· en· W2017419126 on OpenAlexaboutno aff
P. A. Bastian, O. F. R. Wirth, Long Wang, G. W. Voneiff

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2005
Typearticle
Languageen
FieldEngineering
TopicReservoir Engineering and Simulation Methods
Canadian institutionsnot available
Fundersnot available
KeywordsCanyonHorseshoe (symbol)PetroleumDrillingGeologyJoint ventureCoalbed methaneJoint (building)Resource (disambiguation)Mining engineeringCoalEngineeringCoal miningCivil engineeringComputer sciencePaleontologyBusinessWaste management

Abstract

fetched live from OpenAlex

Abstract This paper discusses how the Horseshoe Canyon coalbed methane (CBM) play in Western Canada was converted from an under-explored, non-commercial resource to a major commercial play through the application and modification of technology and analysis techniques from other basins, and how this play is being developed today. As of December 2004, production from the Horseshoe Canyon CBM play is estimated to be over 100 MMscfd of gas, with future production expected to grow significantly. The first commercial developments were completed in 2003 and 2004, and drilling is increasing and expected to exceed 3,000 wells per year in 2005.1 The Horseshoe Canyon CBM play covers a large geographic area of 200 miles by 50 miles, and exists in a large, complex vertical section with numerous coal, sand, silt, shale and mudstone layers. In addition, the play is naturally under-pressured, and the coal beds are mostly dry. Because of these complex and unique characteristics, assessment of commercial viability and development optimization can be confusing and only applicable over small parts of the play. MGV Energy, Inc. (a wholly-owned subsidiary of Ft. Worth-based Quicksilver Resources, Inc.) and its initial joint venture (JV) partner, PanCanadian Petroleum Limited (now EnCana Corp.), discovered the techniques to achieve commerciality and pioneered many of the practices used by industry today for Horseshoe Canyon CBM development. In this paper, we discuss identification of the reservoir opportunity, including data acquisition and analysis. We describe geologic and reservoir models, and production forecasting methods. We also cover completion and production practices, spacing optimization and reserves estimation procedures.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score0.227

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.002
Science and technology studies0.0020.001
Scholarly communication0.0020.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.270
Teacher spread0.251 · 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 designObservational
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

Citations26
Published2005
Admission routes1
Has abstractyes

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