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Record W2284790583 · doi:10.2118/177286-ms

A Simulation Model for Accurate Prediction of Uneven Proppant Distribution in the Marcellus Shale Coupled with Reservoir Geomechanics

2015· article· en· W2284790583 on OpenAlexafffund
Sheng Yang, Zhangxin Chen, Yizheng Wei, Keliu Wu, Lei Shao, Wei Wu

Bibliographic record

VenueSPE Eastern Regional Meeting · 2015
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsGeomechanicsHydraulic fracturingPetroleum engineeringGeologyPermeability (electromagnetism)Oil shaleReservoir simulationGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract Proppant distribution directly affects the effectiveness of hydraulic fracturing treatment, and significantly impact well production. In most situations, proppants are not uniformly distributed in hydraulic fractures and most proppants cannot be effectively transported into the far ends of fractures from wellbore. As a result of uneven proppant distribution and the ductile and soft geomechanical properties of the Marcellus shale, the reduction in hydraulic fracture conductivity varies greatly from cluster to cluster in a shale reservoir during reservoir pressure depletion. However, the influences of accurate prediction of uneven proppant distribution in hydraulic fractures and geomechanics on well productivity have been ignored in previous simulation studies. In this study, a reservoir simulation model for accurate prediction of uneven proppant distribution is developed. This dual-permeability model is coupled with reservoir geomechanics to illustrate the interaction of stress changing and multiphase flow within hydraulic fractures. History matching from a production well in the Marcellus shale is performed to validate our model. In addition, simulations using different matrix permeability are performed to demonstrate the impact of matrix permeability, along with the effect of proppant distributions and geomechanical properties on well performance. The simulation results show that gas production varies significantly due to different proppant distribution and geomechanical properties. Coupled geomechanical simulation results clearly indicate that the proppant embedding effects are also noticeable due to soft rock mechanical properties and uneven proppant distribution. This paper provides operators with a clear insight of influences of uneven proppant distribution and geomechanics on shale gas performance, optimization of a well treatment design, and an extensive view about the long-term production behavior for the Marcellus shale.

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)0.001
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 score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.052
GPT teacher head0.255
Teacher spread0.203 · 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

Citations13
Published2015
Admission routes2
Has abstractyes

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