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Record W2767649029 · doi:10.2118/188873-ms

An Integrated Numerical Simulation Scheme to Predict Shale Gas Production of a Multi-Fractured Horizontal Well

2017· article· en· W2767649029 on OpenAlexafffund
Jie Zhan, Jing Lu, Allan Fogwill, Ivan Ulovich, Jili Cao, Ruijian He, Zhangxin Chen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsCanadian Energy Research InstituteUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaCMG Reservoir Simulation FoundationUniversity of Calgary
KeywordsKnudsen diffusionOil shalePermeability (electromagnetism)MechanicsDarcy's lawPetroleum engineeringKnudsen numberSlip (aerodynamics)GeologyPoromechanicsGeotechnical engineeringThermodynamicsChemistryPorous mediumPorosityPhysics

Abstract

fetched live from OpenAlex

Abstract The application of horizontal well drilling coupled with the multistage fracturing technology enables commercial development of shale gas formations. To optimize the shale gas development, the transient gas flow in a shale formation is of great research interest. Due to anano-scale pore radius, the gas flow in shale matrix may fall in flow regimes which include viscous flow, slip flow and Knudsen diffusion. On top of that, gas adsorption/desorption and stress-sensitivity are some other important phenomena in shales. In this paper, we introduce a novel numerical simulation scheme to depict the above phenomena and predict the gas production from a multi-stage fractured horizontal well, which is crucial for the shale gas development. Instead of Darcy's equation, we implement the apparent permeability in the continuity equation to depict the gas flow (viscous flow, slip flow and Knudsen diffusion) in shale matrix. An adsorption/desorption term is included in the continuity equation as an accumulation term. A sink which is based on Peaceman's well model is placed at the center of the fracture cell. Uniform fluid flow from matrix to fractures is assumed. Only viscous flow is considered in the fractures and the permeability of the fractures doesnot change with pressure. The model is validated via comparing with an infinity-conductivity fracture model. Moreover, the lab data of Eagle Ford shale which provides the relationship between matrix permeability and the effective stress is integrated into the two-way coupling geomechanical process to simulate a stress-sensitive shale formation. Furthermore, the Langmuir and BET models will be compared to investigate the detailed adsorption/desorption process. This methodology examines the influence of each mechanism for the transient shale gas flow. Instead of conventional pressure-independent Darcy permeability, the apparent permeability increases with the development of a shale gas reservoir, which leads to higher productivity. With the gas adsorption/desorption, the reservoir pressure is maintained via the supply of released gas from nano-scale pore wall surfaces, which also leads to higher gas production. In addition, it yields a 5% difference for the cumulative production for one yearbetween the Langmuir and BET models. With the consideration of geomechanics, the apparent permeability is decreased due to the compaction of nano-scale pores, which leads to a decrease in productivity. Due to the difference of compaction magnitude for each grid block, geomechanics creates additional heterogeneity for anano-pore network in a shale formation, which we should pay more attention to. A novel methodology is introduced to examine the crucial phenomena in a shale formation, which simultaneously takes into account the influence of flow regimes, gas adsorption/desorption and stresssensitivity. On top of that, the productivity of a multi-stage fractured horizontal well is quantified. We provide an effective way to quantify the above effects for the transient gas flow in shale formations.

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 score0.024
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
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 head0.018
GPT teacher head0.277
Teacher spread0.259 · 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
Published2017
Admission routes2
Has abstractyes

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