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Record W3026878428 · doi:10.2118/201201-pa

Numerical Analysis of the Effects of Apparent-Permeability Modeling and Secondary-Fracture Distribution for Hydraulic-Fractured Shale-Gas Production Analysis

2020· article· en· W3026878428 on OpenAlexaff
Chuanyao Zhong, Juliana Y. Leung

Bibliographic record

VenueSPE Reservoir Evaluation & Engineering · 2020
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsPermeability (electromagnetism)Knudsen diffusionDarcy's lawPetroleum engineeringMechanicsGeologyCapillary pressureOil shaleFluid dynamicsPetrophysicsReservoir simulationPorosityRelative permeabilityTight gasGeotechnical engineeringHydraulic fracturingPorous mediumChemistry

Abstract

fetched live from OpenAlex

Summary Complex flow mechanisms, such as Knudsen diffusion, are encountered in the shale matrix because of the presence of nanopores. Numerous apparent-permeability models have been proposed to capture the ensuing non-Darcy flow behavior. However, these models are not readily available in most commercial reservoir simulators, and ignoring these mechanisms can potentially underestimate the overall matrix conductivity. This work implements an explicit coupling strategy for integrating a pressure-dependent apparent-permeability model in reservoir simulation. The numerical models are subsequently used to study the effects of apparent-permeability modeling and natural-fracture distribution on gas production and water loss during flowback. The effects of multiphase-flow functions on fluid retention are also assessed. A set of 3D reservoir models are constructed using field data obtained from the Horn River shale-gas reservoir. First, stochastic 3D discrete-fracture-network (DFN) models are scaled up into equivalent continuum dual-porosity/dual-permeability models. An apparent-permeability (Kapp) model accounting for contributions of slip flow, Knudsen diffusion, and surface pore roughness is applied at each gridblock. A novel coupling scheme is formulated to facilitate the updating of Kapp after a certain specified time interval, capturing the pressure dependency of the Kapp. The sensitivity of the updating frequency is analyzed. The results reveal that incorporating these additional flow mechanisms by means of the apparent-permeability formulation could potentially increase the overall gas-production prediction by up to 11%, depending on the average pore radius, reservoir pressure, and several other matrix or fluid properties. The implications of Kapp modeling in water-loss mechanisms are further examined through a set of sensitivity analyses, where the effects of multiphase-flow functions and DFN distributions are systematically investigated. The following interesting findings are observed: Ignoring Kapp modeling could overestimate water recovery. Fracturing-fluid propagation and long-term water recovery are strongly affected by the secondary-fracture intensity; increase in secondary-fracture intensity would enhance water loss during flowback. Gas production is highly affected by the amount of water in the near-well region. In a gas/water system, compressibility of the in-situ fluids renders the effects of countercurrent imbibition and water retention to be more complex from those observed in similar water/oil systems. This work offers a novel, yet practical, scheme for representing the pressure-dependent matrix apparent permeability in the flow simulation of shale reservoirs. The proposed method captures the non-Darcy flow behavior caused by the complex transport mechanisms occurring in nanosized pores. Most importantly, this coupling procedure can be implemented in existing commercial reservoir-simulation packages. The results have revealed a few interesting insights regarding the potential implications in fracturing design and estimation of 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 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)0.001
metaresearch head score (Gemma)0.001
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 score0.408
Threshold uncertainty score0.820

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.004
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 head0.017
GPT teacher head0.254
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

Citations3
Published2020
Admission routes1
Has abstractyes

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