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Record W2063129310 · doi:10.2118/140654-ms

Advancements in Efficiency in Horn River Shale Stimulation

2011· article· en· W2063129310 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSPE Hydraulic Fracturing Technology Conference · 2011
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsApache (Canada)
Fundersnot available
KeywordsWellheadSCADAFrench hornDrillingOil shalePetroleum engineeringDirectional drillingComputer scienceEngineeringEnvironmental scienceElectrical engineeringMechanical engineeringWaste management

Abstract

fetched live from OpenAlex

Abstract In the remote Horn River Shale of North East British Columbia, Canada the key challenge operators’ face is high costs related to completions in the horizontal wells. A strategy was developed to focus on efficiency improvements to make a positive impact on the economics. Previous projects were examined and a two prong approach was developed: first we needed to procure as many extra resources as could be anticipated to ensure continuous operations, and second we would have to have more than one critical path operation simultaneously so that costly activities could continue uninterrupted. The resource planning includes the drilling of multiple wells on the pad and having most of the wells available for operations during the stimulation campaign. It also includes specially designed equipment such as bulk sand handling equipment, water handling, slurry handling, wellhead protection, SCADA systems and custom flow back equipment. In addition, new ways of managing the human resources at the site were implemented. To manage the critical path activities, a protocol was developed to manage surface and down hole interactions. A safety system was developed to support the integration of these challenging operations. An offsite real time operations room was employed were all the sensor data from the site was available, including frac data, water and sand supply, pressure and H2S. The resulting impact on efficiency will be discussed in detail, including reaching a field frac efficiency record for the Horn River.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.181
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.017
GPT teacher head0.228
Teacher spread0.211 · 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