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Record W4310290469 · doi:10.56952/igs-2022-183

Digital Simulation of Blockage-Removing Mechanism Upon Microfracturing of Unconsolidated Sandstone Formation

2022· article· en· W4310290469 on OpenAlexaboutno aff
Haitao Zhu, Botao Lin

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicHydraulic Fracturing and Reservoir Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsGeologyHydraulic fracturingPermeability (electromagnetism)Geotechnical engineeringPetroleum engineeringWellborePetrology

Abstract

fetched live from OpenAlex

Abstract Fine migration is considered to be the primary factor leading to the production decline of the unconsolidated sandstone formations. Micro-fracturing technology is regarded as a practical approach to solving these problems for improving the permeability near the wellbore. However, no detailed and comprehensive research has investigated the blockage-removing mechanism upon microfracturing. Therefore, a comprehensive simulation method is proposed in this study, capable of simulating the permeability evolution and the mechanical responses of the unconsolidated sandstone during the micro-fracturing. Afterward, the blockage-removing mechanism of micro-fracturing and the associated mechanical deformation are analyzed in a field case. Furthermore, the simulation results were classified using digital method and made into a predictive chart. The results show that the permeability damage caused by a blockage in an unconsolidated sandstone can be treated by micro-fracturing. The underlying mechanisms can be summarized in three aspects. First, the hydraulic effect can mitigate the permeability damage caused by particle deposition. Secondly, the increase in pore throat radius due to pore dilation can disrupt the structure of the particle bridging. Third, the blockage caused by size exclusion is diminished due to an increase in the effective pore radius. These results elucidated the mechanism of blockage removal by microfracturing and provided valuable guidance for field engineers to improve the subsequent stimulation work. Introduction In recent years, more and more oil and gas resources are found in unconsolidated sands or weakly consolidated sandstones, such as the Gulf of Mexico, Athabasca (Canada), Orinoco (Venezuela), and Bohai Bay Basin of China (Xiong et al., 2018; Wang et al., 2021). These resources include conventional oil and gas reservoirs and unconventional reservoirs such as oil sands. The loss of permeability caused by fines migration in the reservoir is always considered a significant factor responsible for productivity decline. Due to unconsolidated cementation, fines migration almost occurs during the whole process of oil and gas reservoir development, such as drilling, fracturing, water injection, or liquid production. Due to the high fluid velocity near-wellbore, it is essential to note that fines migration and blocking always occur near the wellbore. Various approaches have been proposed to alleviate the adverse effects of fines migration on reservoir permeability, including nanofluid clay stabilizers, and polymers. (Huang et al., 2008; Yuan et al., 2016; Zhang et al., 2015). However, these methods are not only costly and effective for a short period, but are mainly preventive in focus.

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)0.000
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 score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.204
Teacher spread0.198 · 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

Citations0
Published2022
Admission routes1
Has abstractyes

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