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Record W2089932932 · doi:10.2118/162138-ms

Impact of Mechanical Anisotropy on Design of Hydraulic Fracturing in Shales

2012· article· en· W2089932932 on OpenAlexaboutno aff
Safdar Khan, Rick Williams, Sajjad Ansari, Nader Khosravi

Bibliographic record

VenueAbu Dhabi International Petroleum Conference and Exhibition · 2012
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsAnisotropyHydraulic fracturingFracture (geology)GeologyOil shalePerforationCompletion (oil and gas wells)TortuosityStress fieldGeotechnical engineeringPetroleum engineeringMaterials scienceStructural engineeringPorosityEngineeringFinite element methodComposite material

Abstract

fetched live from OpenAlex

Abstract Shale formations have laminated structures that result in directionally dependent mechanical properties. Conventional completion design approaches do not consider the material anisotropy or the laminated nature of shales. This can result in an underestimation of stresses, and lead to incorrect conclusions about the lateral landing points and the perforation intervals. In this paper, the authors demonstrate the importance of considering the anisotropy in the completion design using a case study from the Horn River Basin (HRB), the largest shale gas play in Canada. Shale formations in the HRB are strongly anisotropic with horizontal to vertical Young's modulus ratios varying from 1.2 to 3.5. Field data from the HRB is examined to evaluate the impact of mechanical anisotropy on break down pressure, fracture initiation and fracture containment. Numerical simulation of the completion design was conducted using a planar 3D fracture model. Results of the numerical simulation indicate that the mechanical anisotropy greatly influences the minimum horizontal stress which in turn impacts the fracture containment and fracture geometry. Strong mechanical anisotropy results in lower fracture initiation pressures and lower tortuosity at the wellbore face. Consequently, selecting the landing point in sections with high anisotropy will minimize the fracture initiation problems. The authors conclude that the heterogeneous and anisotropic nature of shales needs to be properly characterized and taken into account when making decisions on lateral landing points and completion design.

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 score0.320
Threshold uncertainty score0.453

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 head0.019
GPT teacher head0.260
Teacher spread0.241 · 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

Citations23
Published2012
Admission routes1
Has abstractyes

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