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Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)

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Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)

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Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)

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Kongzhi yu Juece/Control and Decision Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980) Tobacco Science and Technology Teikyo Medical Journal Agricultural Mechanization in Asia, Africa and Latin America International Medical Journal Technology Reports of Kansai University Asia Life Sciences Open Access Journals Tagliche Praxis Tobacco Science and Technology Bulletin of National Institute of Health Sciences Journal of the Austrian Society of Agricultural Economics Changjiang Liuyu Ziyuan Yu Huanjing/Resources and Environment in the Yangtze Valley

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)

Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science) (ISSN: 1673-5005) is a monthly peer-reviewed scopus indexed journal from 2006 to up to now. The publisher of the journal is Petroleum university periodical society . JCUP welcomes all original papers of engineering, earth science around the world from any sorts of professionals

01ISSN: 1673-5005

02UGC approved

Aim And Scope Kongzhi yu Juece/Control and Decision Azerbaijan Medical Journal Gongcheng Kexue Yu Jishu/Advanced Engineering Science Zhonghua er bi yan hou tou jing wai ke za zhi = Chinese journal of otorhinolaryngology head and neck surgery Zhenkong Kexue yu Jishu Xuebao/Journal of Vacuum Science and Technology Wuhan Ligong Daxue Xuebao (Jiaotong Kexue Yu Gongcheng Ban)/Journal of Wuhan University of Technology (Transportation Science and Engineering) Zhonghua yi shi za zhi (Beijing, China : 1980) Tobacco Science and Technology Teikyo Medical Journal Agricultural Mechanization in Asia, Africa and Latin America International Medical Journal Technology Reports of Kansai University Asia Life Sciences Open Access Journals Tagliche Praxis Tobacco Science and Technology Bulletin of National Institute of Health Sciences Journal of the Austrian Society of Agricultural Economics Azerbaijan Medical Journal Changjiang Liuyu Ziyuan Yu Huanjing/Resources and Environment in the Yangtze Valley

Chemical Engineering Mechanical engineering, Energy science Geotechnical Engineering and Engineering Geology Fuel Technology Electrical and electronic engineering Earth and planetary sciences Mechanical engineering Computer science and engineering Energy Engineering Power Technology Energy Engineering Electrical and telecommunication engineering Energy science Control and Systems Engineering

Paper DateLine

Submission Deadline
( Vol 47 , Issue 01 ) 15 May 2023

Publish On
( Vol 47 , Issue 01 ) 31 May 2023

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Zhongguo Shiyou Daxue Xuebao (Ziran Kexue Ban)/Journal of China University of Petroleum (Edition of Natural Science)
Dynamic behavior and mitigation methods for drilling fluid loss in fractured formations

In view of the lost circulation in fractured formations, a two-dimensional transient model for describing a power-law drilling fluid loss in an arbitrarily-oriented, compressible, permeable, rough-walled fracture was introduced. In this model, the mechanical fracture aperture and fracture tortuosity were considered to investigate the effect of fracture roughness on fluid loss dynamics. The governing equation of power-law fluid loss model was given and solved to analyze the fluid loss dynamics in fractured formations. The results show that the shear thinning behavior of power-law drilling fl

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An automatic history matching method based on ensemble and neural architecture search

Reformulating the history matching problem from a least-square mathematical optimization problem into a Markov Decision Process introduces a method in which reinforcement learning can be utilized to solve the problem. This method provides a mechanism where an artificial deep neural network agent can interact with the reservoir simulator and find multiple different solutions to the problem. Such a formulation allows for solving the problem in parallel by launching multiple concurrent environments enabling the agent to learn simultaneously from all the environments at once, achieving signific

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Prediction of hook load and rotary drive torque during well-drilling using a BP-LSTM network

In this paper, BP neural network and long & short-term memory neural network were selected along with a double-input network architecture to establish an intelligent prediction model of the hook load and rotary drive torque. In the modeling, a variety of complex parameter Affecting the torque and load, and their dynamic variations with time were considered simultaneously, with the torque and drag being predicted using both sequential data and non-sequential data. A case study using a real oilfield drilling site data indicates that, the hook load and Rotary torque can be accurately predi

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Calculation method of hydraulic parameters in whole cementing process considering coupling effect of temperature and pressure

Considering the complicated interactions between temperature, pressure and hydration reaction of cement, a coupled model of temperature and pressure based on hydration kinetics during deep-water well cementing was established. The differential method was used to do the coupled numerical calculation, and the calculation results were compared with experimental and field data to verify the accuracy of the model. When the interactions between temperature, pressure and hydration reaction are considered, the calculation accuracy of the model proposed is within 5.6%, which can meet the engineering

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Review on application of machine learning in hydraulic fracturing

Growing amount of fracturing stimulation jobs in the recent two decades resulted in a significant amount of measured data available for construction of predictive models via machine learning (ML). Simulataneous evolution of machine learning has made it possible to apply algorithms on the hydraulic fracture database. A typical multistage fracturing job on a near-horizontal well today involves a significant number of stages. The post-fracturing production analysis (e.g., from production logging tools) reveals evidence that different stages produce very non-uniformly, and up to 30% may not be

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