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Record W3093258400 · doi:10.2118/203844-pa

A Coupled Flow–Geomechanical Modeling of Out-of-Sequence Fracturing Using a Dual-Lattice Implementation of Synthetic-Rock-Mass Approach

2020· article· en· W3093258400 on OpenAlexaboutno aff
Benyamin Yadali Jamaloei

Bibliographic record

VenueSPE Production & Operations · 2020
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyAnisotropyFracture (geology)SlippageHydraulic fracturingSlip (aerodynamics)Shear (geology)MechanicsGeotechnical engineeringPetrologyStructural engineeringEngineeringPhysics

Abstract

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Summary In out-of-sequence (OOS) pinpoint fracturing, Stage 1 is fractured, followed by Stage 3, after which Stage 2 (center fracture) is placed between Stages 1 and 3 (outside fractures). The center fracture can exploit the reduced stress anisotropy to activate planes of weakness (e.g., fissures) and create branch fractures that can connect hydraulic fractures to stress-relief fractures, ultimately enhancing fracture connectivity and complexity. It has been trialed in western Siberia (2014) and western Canada (2017 to 2019) with overall operational and production performance success. Previous fracture-modeling works calibrated by OOS fracturing trials have either used shear-decoupled planar-fracture models (in which slippage along the shear planes restricts the displacement to a limited area because of displacement damping)—which are unable to reproduce out-of-plane fracture complexity, and to dynamically track the change in stress anisotropy and orientation—or discrete-fracture-network (DFN) models, which often exaggerate the fracture-network connectivity, and reproduce unrealistically high fracture-network-extension pressures in the stimulated reservoir volume (SRV). This work attempts to resolve the issues in planar-fracture and DFN models by more realistically addressing the dominant mechanisms of OOS fracturing, dynamic changes in the stress anisotropy and orientation, activation of pre-existing planes of weaknesses, and poroelasticity using an iteratively coupled flow–geomechanical model that uses the dual-lattice implementation of the synthetic-rock-mass (SRM) model with a robust, fully coupled, iterative flow/stress solution to capture the following: Nonlinear deformations caused by induced tensile- and shear-fracture-complexity propagation Induced stress shadowing in and around the SRV Sliding of opened, pre-existing joints, fractures, and fissures using the smooth-joint model (SJM) Propagation of the hydraulic fracture as an aggregate of intact matrix fracturing and opening and slip of pre-existing fluid-filled planes of weakness (e.g., joints, fractures, fissures) Permeability enhancement in the main tensile and complex fractures following the updated deformation aperture from the coupled solution The results (fracture geometries and treatment pressures) of the three models (planar-fracture, DFN, and SRM with lattice models) are compared after using each model for treatment-pressure history matching of an OOS-fracturing trial. The calibrated, coupled SRM with lattice model more reasonably reproduces the measured fracture-extension pressures and end-of-job pressures from OOS pinpoint fracturing treatments, and it reveals the following: The dynamic change in the stress-field orientation and magnitude during OOS fracturing leads to a reduction in stress anisotropy and complex out-of-plane fracturing in the SRV for center fractures. Center fractures tend to be narrower and shorter if sufficient out-of-zone growth is attained in the absence of strong vertical containment, making OOS fracturing an option for penetrating multistacked zones in one treatment. Where center fractures are shorter or near-well fracture complexity is generated, OOS fracturing can be considered in treating the child wells to reduce fracture hits. Compared with planar-fracture and DFN models, this coupling technique achieves the following: Accounts for dominant mechanisms of complex shear and tensile fracturing Renders fast computation in simulating large 3D models with dual-lattice implementation of SRM with SJM Reproduces fracture surface area and SRV permeability more realistically Leads to a more reasonable history match of the measured OOS-fracturing pressures

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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: none
Teacher disagreement score0.561
Threshold uncertainty score0.565

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 head0.051
GPT teacher head0.294
Teacher spread0.243 · 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

Citations5
Published2020
Admission routes1
Has abstractyes

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