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Record W4411383080 · doi:10.3390/pr13061915

Simulation Study of Refracturing of Shale Oil Horizontal Wells Under the Effect of Multi-Field Reconfiguration

2025· article· en· W4411383080 on OpenAlexaff
Hongbo Liang, Penghu Bao, Gang Hui, Zeyuan Ma, Xuemei Yan, Xiaohu Bai, Jiawei Ren, Zhiyang Pi, Ye Li, Chenqi Ge, Yujie Zhang, Xing Yang, Yunhu Lu, Dan Wu, Fei Gu

Bibliographic record

VenueProcesses · 2025
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsUniversity of Calgary
FundersScience Foundation of China University of Petroleum, BeijingPetroChina Company LimitedChina University of Petroleum, Beijing
KeywordsPetroleum engineeringOil shaleGeologyOil fieldControl reconfigurationField (mathematics)Geotechnical engineeringPetrologyEngineeringPaleontologyEmbedded systemMathematics

Abstract

fetched live from OpenAlex

The mechanisms underlying formation energy depletion after initial fracturing and post-refracturing production decline in shale oil horizontal wells remain poorly understood. This study proposes a novel numerical simulation framework for refracturing processes based on a three-dimensional fully coupled hydromechanical model. By dynamically reconfiguring the in situ stress field through integration of production data from initial fracturing stages, our approach enables precise control over fracture propagation trajectories and intensities, thereby enhancing reservoir stimulation volume (RSV) and residual oil recovery. The implementation of fully coupled hydromechanical simulation reveals two critical findings: (1) the 70 m fracture half-length generated during initial fracturing fails to access residual oil-rich zones due to insufficient fracture network complexity; (2) a 3–5° stress reorientation combined with reservoir repressurization before refracturing significantly improves fracture network interconnectivity. Field validation demonstrates that refracturing extends fracture half-lengths to 97–154 m (38–120% increase) and amplifies RSV by 125% compared to initial operations. The developed seepage–stress coupling methodology establishes a theoretical foundation for optimizing repeated fracturing designs in unconventional reservoirs, providing critical insights into residual oil mobilization through engineered stress field manipulation.

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)0.000
metaresearch head score (Gemma)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.773
Threshold uncertainty score0.231

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.008
GPT teacher head0.266
Teacher spread0.258 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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