Jet characteristics and optimal design of hydraulic perforating nozzles for natural gas hydrate reservoirs-Reference-Cited by-同舟云学术

Jet characteristics and optimal design of hydraulic perforating nozzles for natural gas hydrate reservoirs

Published:2023-09 Issue:9 Volume:15 Page:
ISSN:1687-8132
Container-title:Advances in Mechanical Engineering
language:en
Short-container-title:Advances in Mechanical Engineering

Author:

Gao Yu¹,Xu Zhenqiang²³^ORCID,Shen Kaixiang²³,Wu Shilei⁴

Affiliation:

1. Shenzhen South China Sea Oil Economic Development Co. Ltd, Shenzhen, China

2. Guangzhou Marine Geological Survey, China Geological Survey, Guangzhou, China

3. National Engineering Research Center of Gas Hydrate Exploration and Development, Guangzhou, China

4. School of Mechanical Engineering, Suzhou University of Science and Technology, Suzhou, China

Abstract

Jet nozzle is a core component of the hydraulic perforating fracturing tool, and the design of the nozzle structure directly affects the perforating effectiveness and efficiency. In this paper, the flow field characteristics of hydraulic perforating nozzles are calculated by the SST k- ω turbulence model. The results show that the nozzle structure parameters significantly influence the jet performance. Through orthogonal experiments, the conical-cylindrical nozzle geometric parameters are preliminarily optimized as follows: the cylindrical segment diameter is 6 mm, the contraction angle is 10°, the contraction segment length is 8 mm, and the cylindrical segment length is 12 mm. The results can provide theoretical guidance for later experimental research on jet rock breaking and optimization design of the jet tools.

Funder

Marine Economy Development Foundation of Guangdong Province “Technical Support for Stimulation and Testing of Gas Hydrate Reservoirs”

Publisher

SAGE Publications

Subject

Mechanical Engineering

Link

http://journals.sagepub.com/doi/pdf/10.1177/16878132231197488

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5. Numerical study of fracture dynamics in different shale fabric facies by integrating machine learning and 3-D lattice method: A case from Cangdong Sag, Bohai Bay basin, China