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Record W2322902307 · doi:10.2118/176381-ms

A Mathematical Model for Calculating the Volume of Proppant in Shale Vertical Wells

2015· article· en· W2322902307 on OpenAlexaff
Jie Zeng, Yan Deng, Jianchun Guo, Cong Lu, Bo Gou, Fanhua Zeng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Regina
FundersState Key Laboratory of Oil and Gas Reservoir Geology and ExploitationSouthwest UniversitySouthwest Petroleum University
KeywordsOil shaleGeologyPermeability (electromagnetism)Hydraulic fracturingPetroleum engineeringFracture (geology)PorosityShale gasGeotechnical engineeringMineralogy

Abstract

fetched live from OpenAlex

Abstract Hydraulic fracturing, creating stimulated reservoir volumes, has been widely applied to obtain economic flow in shale gas/oil formations today. However, for a fractured shale vertical well, determining the volume of proppant placed into the pay zone still remains difficult. Previous methods are based mainly upon empirical approaches which, to a large extend, are uncertain and imperfect. This paper established a novel but simple mathematical model to calculate the volume of proppant, merely using optimized parameters of fracture network obtained from numerical simulation. Since the permeability of fracture networks is several orders of magnitude larger than that of the reservoir and the geometry of them is considerably complex, the fracture networks are simplified as a high permeability zone (HPZ) according to the equivalent principle of seepage. HPZ units are selected to build this model based on the following assumptions: (1) the seepage flow in shale involves the matrix flow and fracture flow from HPZ units to wellbore under steady state, (2) a multilayer seepage model is utilized to describe the fluids flowing in HPZ unit and study the characteristic seepage behavior of dual porosity medium, (3) heterogeneity in fracture propagation direction is neglected, (4) the proppant is packed into the fracture uniformly. The model reported here has been successfully applied to XC32 well in Sichuan Basin and its prediction is 408.8m3(40/70-mesh, ceramic proppant). In reality, 400.4m3 of proppant was used and fracturing monitoring showed that the HPZ parameters in the field (length 500~600m, width 130~200m) match well with previous optimized design (length 550m, width 100m). Besides, the resulting flow rate is 7.04×104m3/d in this case, which is a breakthrough for unconventional reservoirs in Sichuan Basin. Because this model considered the vertical heterogeneity of the reservoir and simply utilized HPZ parameters, it is convenient and meaningful to direct the treatment design in the field.

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)0.001
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: none
Teacher disagreement score0.022
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0020.001
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 head0.034
GPT teacher head0.252
Teacher spread0.218 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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