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Record W2093867626 · doi:10.2118/151752-ms

Understanding Shale Heterogeneity - Key to Minimizing Drilling Problems in Horn River Basin

2012· article· en· W2093867626 on OpenAlexaboutno aff
Safdar Khan, Sajjad Ansari, Hongue Han, Nader Khosravi

Bibliographic record

VenueIADC/SPE Drilling Conference and Exhibition · 2012
Typearticle
Languageen
FieldEngineering
TopicDrilling and Well Engineering
Canadian institutionsnot available
Fundersnot available
KeywordsBoreholeGeologyOil shaleLost circulationDrillingDrilling fluidAnisotropyDirectional drillingPetroleum engineeringIsotropyGeotechnical engineeringPore water pressureWellboreTrajectoryCompletion (oil and gas wells)AzimuthStress (linguistics)EngineeringGeometryPaleontology

Abstract

fetched live from OpenAlex

Abstract More than 70% of nonproductive time (NPT) and increases in drilling cost are related to wellbore instabilities. Shale formations are the primary sources (90%) for wellbore instabilities. Numerous wellbore instability problems have been reported in the Horn River basin (HRB), the largest shalegas play in Canada. As a consequence of the depositional environment, shale formations have laminated structure that results in anisotropic mechanical properties and horizontal stresses. Failure to consider this characteristic of shale can have severe consequences on drilling. Traditional isotropic stress calculation approaches typically used in wellbore stability analysis do not consider 3D azimuthal anisotropy present in shales. Ignoring anisotropy generally results in underestimation of stresses, which can lead to incorrect safe trajectory or mud-weights predictions. In this paper, drilling problems experienced in 15 wells in two different areas of the HRB were examined. Some of these wells had severe wellbore instabilitis due to high pore pressure, mud losses or lost circulation, tight hole/stuck pipe/pack off, or combination of these events. Three borehole assemblies (BHA) were lost in these wells which required side tracking. Most of these problems were experienced in Fort Simpson and upper Muskwa formations. An in-depth postmortem analysis of these wells indicated that shale heterogeneity was not properly characterized (anisotropic horizontal stresses were not considered in the prespud analysis which resulted in incorrect mud-weight predictions and trajectory calculations.

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 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 score0.350
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.073
GPT teacher head0.236
Teacher spread0.163 · 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.

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

Citations9
Published2012
Admission routes1
Has abstractyes

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