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Record W3010775699 · doi:10.2118/199978-ms

Geomechanical Characterization and Modeling in the Montney for Hydraulic Fracturing Optimization

2020· article· en· W3010775699 on OpenAlexaboutno aff
Qiuguo Li, Oscar Perez Michi, Drazenko Boskovic, Alexey Zhmodik, Majid M. Faskhoodi, Giselle Garcia Ferrer, Venkateshwaran Ramanathan, Taofeek Ogunyemi, Raouf Ameuri

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeomechanicsGeologyHydraulic fracturingGeotechnical engineeringPore water pressurePetroleum engineering

Abstract

fetched live from OpenAlex

Abstract Geomechanics plays a significant role in hydraulic fracture initiation and propagation and in the interaction between hydraulic fractures and natural fractures, especially in unconventional reservoirs. This paper provides a detailed description of a geomechanical characterization and modeling study for evaluating the impact of geomechanics on completions and hydraulic fracturing stimulations optimization in the Montney resource play, Canada. Following an integrated workflow, 1D mechanical earth models (MEM) for ten wells were constructed in the study area. These 1D MEMs include elastic and strength properties, pore pressure, direction and magnitude of in-situ stresses. Extensive rock mechanics core testing data were used to calibrate the elastic and strength properties. Pore pressure and fracture closure pressure data from diagnostic fracture injection tests were also available to calibrate pore pressure and minimum in-situ stress. Maximum horizontal stress was constrained by modeling wellbore stability and matching it with caliper logs and wellbore stability features on wellbore image. A 3D mechanical earth model was subsequently constructed using a 3D geological model, the 1D MEMs, and seismic inversion data. Elastic properties from seismic inversion were used to populate mechanical properties in the 3D model. In-situ stresses were numerically simulated to account for the impact of faults and structural and mechanical property variation on in-situ stress distribution. The geomechanical analysis shows that there is a decreasing trend in Young’s modulus from upper Montney to lower Montney while Poisson’s ratio is relatively constant in the Montney. The pore pressure in some parts of the field is high and varies across the field. Stress regime is predominantly strike-slip with relatively large stress anisotropy, and this has implications on the hydraulic fracture network that would be simulated, shearing of natural fractures and the stimulated reservoir volume. Rock elastic and strength properties, pore pressure, and in-situ stresses were found to be heterogenous across the whole field. The relatively large variation in pore pressure in the study area and the structural complexities have large impact on the distribution of stresses. Faults alter the stress distribution locally and could affect hydraulic fracture propagation. Hydraulic fracture simulations were subsequently performed, and the geometry of the simulated hydraulic fractures and the stimulated reservoir volume were validated with microseismic events. The effects of geomechanics on fracture geometry and ultimately reservoir production were evaluated. Because of the significant impact of geomechanics on hydraulic fracturing, it is critical to characterize and model geomechanics accurately. This paper provides a comprehensive approach and application to a field in the Montney, showcasing the integrated method of geomechanical characterization and hydraulic fracture simulation and production modeling using various data. The analysis provides an interrelationship among geomechanical parameters, microseismicity and stimulated reservoir volume.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.430
Threshold uncertainty score0.854

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.024
GPT teacher head0.218
Teacher spread0.194 · 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 source (direct Gemma or distilled Codex), 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

Citations6
Published2020
Admission routes1
Has abstractyes

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