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Record W2974154820 · doi:10.2118/196026-ms

Unconventional Shale Hydraulic Fracturing Under True Triaxial Laboratory Conditions, the Value of Understanding Your Reservoir

2019· article· en· W2974154820 on OpenAlexaffabout
Aly Abdelaziz, Johnson Ha, Hani Abul Khair, Matthew Adams, Chee Phuat Tan, Ikhwanul Hafizi Musa, Giovanni Grasselli

Bibliographic record

VenueSPE Annual Technical Conference and Exhibition · 2019
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsGeologyHydraulic fracturingGeotechnical engineeringBeddingOil shaleBoreholeOutcropRock mechanicsSedimentary rockTriaxial shear testPetrologyGeomorphologyShear (geology)Geochemistry

Abstract

fetched live from OpenAlex

Abstract The Montney Formation of the Western Canadian Sedimentary Basin has emerged as one of the most prolific unconventional resource plays in the North American unconventional space. The authors propose a novel method to better understand the failure mechanics associated with hydraulic fracturing through laboratory testing under true triaxial conditions. This adds essential fundamentals with respect to upscaled field hydraulic fracturing operations in the formation. A representative source rock block recovered from outcrop was prepared into a cube and hydraulically fractured in the laboratory under true triaxial stress conditions. Field outcrop mapping of this quarry has confirmed that samples collected are of the same geological time and spatially equivalent to the source rock (Zelazny et al. 2018). This novel laboratory experiment mimics a single stage open hole hydraulic fracturing using a slickwater system, composed of surfactant, friction reducer, and biocide as the injection fluid. Micro-computed tomography (μCT) scans were used to identify the presence of preexisting fractures and bedding planes. A mini-well was drilled to the center of the cube, parallel to the direction of the minimum principal stress (σ3) and along the strike of the bedding planes, such that there is a 5 mm long down-hole open cavity. The existing true triaxial test system at the University of Toronto was retrofitted to accommodate a custom designed mini-packer system. Stresses were applied hydrostatically, and then differentially until the stress regime, replicating the field observed reservoir depth at about 2 km depth, was reached. The bottom hole was subsequently pressurized by pumping the injection fluid through the mini-packer. The test was numerically modeled in three-dimensions using the hybrid finite-discrete element method (FDEM) with the mechanical properties input determined through a series of standard laboratory rock mechanics tests discussed within. Post-test μCT of the tested cube revealed a fracture trace, and scan contrast was enhanced by injecting the cube with 5% wt potassium iodide solution. Interestingly, the highest fluid pressure recorded is slightly higher than σ3 whilst the plane of failure is normal to the intermediate principal stress (σ2) direction, which is parallel to the bedding planes. The results of the mechanical tests and hydraulic fracturing under true triaxial stress conditions reveal the significance and dominance of the macroscopic features and material anisotropy in dictating the overall strength and fracture plane orientation. Features which were unaccounted for in classical reservoir mechanics and the numerical model simulation, resulted in higher than predicted fracture initiation and propagation pressures than the laboratory experiment. This laboratory test approach allows a convenient and flexible method to capture the influence of the reservoir stress regime and its interaction with the sample anisotropy. Coupled with numerical simulations that encompass such features, this framework can benefit the industry by reproducing typical behavior observed in the field; thus, enhancing, improving, and increasing the efficiency of hydrocarbon recovery.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.370
Threshold uncertainty score0.555

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 head0.025
GPT teacher head0.262
Teacher spread0.237 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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