Analyzing effectiveness of the terrigenous reservoirs hydrofracturing at South-Romashkinskaya area of Romashkinskoe oil field at the late stage of development

Галимов, И Ф; Gubaidullin, F.A.; Vakhin, A. V.; Isaev, P. V.

doi:10.24887/0028-2448-2018-1-52-54

Cited by 6 publications

(5 citation statements)

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“…Hydraulic fracturing is one of the most efficient and common methods of increasing well productivity. The use of HF improves the hydrodynamic connection of wells with the productive formation and contributes to the fuller development of oil reserves [1]. The selection of wells for the HF requires the analysis of information on the history of the well operation, past activities, the well technical state, and its bottom-hole zone, etc.…”

Section: Introductionmentioning

confidence: 99%

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Well production after hydraulic fracturing in sandstone rocks in the north of the Perm region

Poplygin¹

2022

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Section: Introductionmentioning

confidence: 99%

“…EMUDC 622.276 V. V. POPLYGIN1 , Associate Professor, Candidate of Engineering Sciences, poplygin@bk.ru1 Perm National Research Polytechnic University, Perm , Russia…”

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confidence: 99%

Well production after hydraulic fracturing in sandstone rocks in the north of the Perm region

Poplygin¹

2022

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“…Currently, most oilfields have entered the middle and late stages of development; hence, the production benefits are increasingly low [1][2][3][4]. The sucker-rod pump is the main pumping system that provides mechanical energy for oil production [5][6][7][8], but due to abnormal working conditions and inefficient management, the energy consumption of production is large [9][10].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

Zhang¹,

Wang²,

Chen³

2021

Energy Engineering

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In recent years, deep learning models represented by convolutional neural networks have shown incomparable advantages in image recognition and have been widely used in various fields. In the diagnosis of sucker-rod pump working conditions, due to the lack of a large-scale dynamometer card data set, the advantages of a deep convolutional neural network are not well reflected, and its application is limited. Therefore, this paper proposes an intelligent diagnosis method of the working conditions in sucker-rod pump wells based on transfer learning, which is used to solve the problem of too few samples in a dynamometer card data set. Based on the dynamometer cards measured in oilfields, image classification and preprocessing are conducted, and a dynamometer card data set including 10 typical working conditions is created. On this basis, using a trained deep convolutional neural network learning model, model training and parameter optimization are conducted, and the learned deep dynamometer card features are transferred and applied so as to realize the intelligent diagnosis of dynamometer cards. The experimental results show that transfer learning is feasible, and the performance of the deep convolutional neural network is better than that of the shallow convolutional neural network and general fully connected neural network. The deep convolutional neural network can effectively and accurately diagnose the working conditions of sucker-rod pump wells and provide an effective method to solve the problem of few samples in dynamometer card data sets.

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“…At present, the problem of oil field development efficiency increase [1][2][3], especially those at the late stage of their development [4], is becoming increasingly important. This stage is characterized by the presence of a large share of hard-to-recover oil reserves, and its extraction rate decrease [5,6].…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Multi-Criterial Choice of Geological and Technical Measures

Panischev¹,

Davydov²,

Anikin³

et al. 2020

International Journal of Engineering Research and Technology

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Analyzing effectiveness of the terrigenous reservoirs hydrofracturing at South-Romashkinskaya area of Romashkinskoe oil field at the late stage of development

Cited by 6 publications

References 0 publications

Well production after hydraulic fracturing in sandstone rocks in the north of the Perm region

Well production after hydraulic fracturing in sandstone rocks in the north of the Perm region

An Intelligent Diagnosis Method of the Working Conditions in Sucker-Rod Pump Wells Based on Convolutional Neural Networks and Transfer Learning

Fuzzy Multi-Criterial Choice of Geological and Technical Measures

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