Journal of China University of Petroleum (Edition of Natural Science)

Scoupus Indexed(2024)

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UGC-CARE APPROVED(2024)

About

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 Ed. Off. Journal of the Univ. Petroleum, China . JCUP welcomes all original papers of engineering, earth science around the world from any sorts of professionals.

01ISSN: 1673-5005

Paper Deadline

Submission Deadline
( Vol 48 , Issue 07 )
19 Jul 2024


Publish On
( Vol 48 , Issue 07 )
31 Jul 2024


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All Published Journal
Journal of China University of Petroleum (Edition of Natural Science)
Wellbore temperature profiling for deepwater multi-gradient drilling

In order to study the distribution law of temperature in deep water CML dual-gradient drilling wellbore, the coupling relationship between wellbore temperature, pressure, drilling fluid density and rheological properties was comprehensively considered, and the transient heat transfer model of deep water CML dual-gradient drilling wellbore was established in combination with the characteristics of deep-water CML dual-gradient drilling process and the law of energy conservation. Based on an example well, the effects of cycle time and drilling fluid physical parameters on annulus temperature w

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Seismic horizon picking technology based on improved DNA algorithms

Picking the reflection horizon is an important step in velocity inversion and seismic interpretation. Manual picking is time-consuming and no longer suitable for current large-scale seismic data processing. Automatic algorithms using different seismic attributes such as instantaneous phase or dip attributes have been proposed. However, the computed attributes are usually inaccurate near discontinuities. The waveforms in the horizontal direction often change dramatically, which makes it difficult to track a horizon using the similarity of attributes. In this paper, we propose a novel method

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Study on fracture toughness of X80 pipeline steel based on small punch test

Evaluating the strength properties of materials of an in-service pipeline without shutting down transportation has been always a challenge. A novel and non-destructive method for determining the true stress-strain curve of pipeline steel based on backpropagation artificial neural network and small punch test is proposed in this study. The elastoplastic mechanical properties of the pipeline steels could be obtained by this method. The load-displacement curves of 2261 groups of different hypothetical materials were obtained by the finite element model of small punch test within Gurson-Tvergaa

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A new drilling fluid technology based on supramolecular chemistry

Based on supramolecular chemistry, a rheology modifier CFZTQ-1 for oil base drilling fluids was developed, and an innovative high-density organoclay-free oil base drilling fluid system centering on CFZTQ-1 was designed, evaluated and applied in the field. CFZTQ-1 can strongly increase the elasticity of invert emulsion due to the supramolecular structure assembled in water phase; CFZTQ-1 has stronger effect in elevating the yield point and suspension ability than several foreign rheology modifiers; the synergistic effect with organoclay also makes CFZTQ-1 available in traditional clay-contai

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A novel deep learning model with physical process information for prediction of flow behaviors in oil and gas reservoirs

Reservoir modeling to predict shale reservoir productivity is considerably uncertain and time consuming. Since we need to simulate the physical phenomenon of multi‐stage hydraulic fracturing. To overcome these limitations, this paper presents an alternative proxy model based on data‐ driven deep learning model. Furthermore, this study not only proposes the development process of a proxy model, but also verifies using field data for 1239 horizontal wells from the Montney shale formation in Alberta, Canada. A deep neural network (DNN) based on multi‐layer perceptron was applied to predi

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