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Artificial Intelligence Applications in Petroleum Supply and Management
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Artificial Intelligence Applications in Petroleum Supply and Management

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "H1: Petroleum Engineering".

Deadline for manuscript submissions: closed (30 May 2023) | Viewed by 14490

Special Issue Editors

Prof. Dr. Wei Pan

E-Mail Website
Guest Editor
School of Applied Economics, Renmin University of China, Beijing 100872, China
Interests: artificial intelligence; digital economy; input–output theory; environment and health; risk management; energy optimization
Special Issues, Collections and Topics in MDPI journals
Dr. Gang Xie

E-Mail Website
Guest Editor
Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China
Interests: artificial intelligence; economic forecasting; sustainable supply chain management; risk management; energy optimization
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

I would like to invite you to contribute to a Special Issue of Energies on “Artificial Intelligence Applications in Petroleum Supply and Management”.

At present, the application of artificial intelligence in the petroleum industry is far from extensive, as it is still in the primary stage of exploration. However, new technologies are introducing a new era in the field, such as integrated sensors, augmented reality (AR), virtual reality (VR), artificial intelligence (AI), and unmanned driving, which have brought great challenges and possibilities to the “very traditional” oil industry. For example, unmanned aerial vehicles (UAVs) have begun to be used to monitor and manage the safety and efficiency of oil equipment operations, while smart sensors and satellite imaging technology are helping operators to manage and operate remote well sites in real time, reducing the risk of long-term isolation.

Technologies such as COMOS and Walkinside are also helping to create 3D visualizable virtual oil platforms that digitally capture real-time information about each component, enabling operators to manage and monitor efficiently and accurately without ever having to step foot on the platform. These activities require relevant supporting technologies, such as the Internet of Things (IoT), information and communication technology (ICT), and 5G. Additionally, with the continued use of conventional oil for nearly a hundred years and with most of the remaining recoverable oil and gas resources in the deep sea, the polar region where no human life is present, the application of artificial intelligence can greatly help humankind to achieve economic security for such unconventional oil and gas development and reduce casualties and supply costs, while at the same time reducing the pollution of the environment through oil development.

A range of tools based on artificial intelligence, simulation integration, and automated data analysis are being developed and increasingly used. Artificial intelligence is undoubtedly an epoch-making leap in software development. It can constantly store, learn, and analyze data to help to improve the accuracy of our future predictions, and it can help more enterprises to use big data technology and analyze data to gain more a strategic understanding.

As petroleum is the most widely used and highly efficient energy raw material, its supply and management play a huge role in the survival and development of petroleum enterprises. However, with the gradual depletion of petroleum, the remaining recoverable resources are primarily located in areas beyond human reach, such as the deep sea and the polar regions. The development of artificial intelligence (AI) is a rare opportunity for petroleum enterprises. Firstly, with the help of AI technology, petroleum enterprises can effectively control and optimize the production process, improve production efficiency, and reduce energy consumption and material consumption. Secondly, AI technology can help petroleum enterprises to introduce safety management services, allowing machines to replace people in inspecting safety hazards, forecasting safety risks, ensuring installations’ safe operation, and preventing safety accidents.

This Special Issue aims to introduce the application of AI technology, such as machine learning, in petroleum supply and management. The ultimate goal is to identify the most promising approaches for each of the current and future challenges in the petroleum sector and provide readers with a set of concrete applications of AI. These applications are not only conducive to the transformation of the petroleum industry but also of great significance to promoting energy conservation and emission reduction for the whole industry.

Prof. Dr. Wei Pan
Dr. Gang Xie
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • AI in petroleum supply systems
  • AI in petroleum management systems
  • AI in energy storage
  • energy forecast
  • energy system
  • energy consumption
  • energy optimization
  • sustainability
  • artificial intelligence (AI)
  • Internet of Things (IoT)
  • Information and communication technology (ICT)
  • big data
  • 5G
  • intelligent control
  • machine learning
  • deep learning

Published Papers (5 papers)

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Research

16 pages, 289 KiB  
Article
Do Artificial Intelligence Applications Affect Carbon Emission Performance?—Evidence from Panel Data Analysis of Chinese Cities
by Ping Chen, Jiawei Gao, Zheng Ji, Han Liang and Yu Peng
Energies 2022, 15(15), 5730; https://doi.org/10.3390/en15155730 - 07 Aug 2022
Cited by 32 | Viewed by 3994
Abstract
A growing number of countries worldwide have committed to achieving net zero emissions targets by around mid-century since the Paris Agreement. As the world’s greatest carbon emitter and the largest developing economy, China has also set clear targets for carbon peaking by 2030 [...] Read more.
A growing number of countries worldwide have committed to achieving net zero emissions targets by around mid-century since the Paris Agreement. As the world’s greatest carbon emitter and the largest developing economy, China has also set clear targets for carbon peaking by 2030 and carbon neutrality by 2060. Carbon-reduction AI applications promote the green economy. However, there is no comprehensive explanation of how AI affects carbon emissions. Based on panel data for 270 Chinese cities from 2011 to 2017, this study uses the Bartik method to quantify data on manufacturing firms and robots in China and demonstrates the effect of AI on carbon emissions. The results of the study indicate that (1) artificial intelligence has a significant inhibitory effect on carbon emission intensity; (2) the carbon emission reduction effect of AI is more significant in super- and megacities, large cities, and cities with better infrastructure and advanced technology, whereas it is not significant in small and medium cities, and cities with poor infrastructure and low technology level; (3) artificial intelligence reduces carbon emissions through optimizing industrial structure, enhancing information infrastructure, and improving green technology innovation. In order to achieve carbon peaking and carbon neutrality as quickly as possible during economic development, China should make greater efforts to apply AI in production and life, infrastructure construction, energy conservation, and emission reduction, particularly in developed cities. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Supply and Management)
13 pages, 3104 KiB  
Article
A Well-Overflow Prediction Algorithm Based on Semi-Supervised Learning
by Wei Liu, Jiasheng Fu, Yanchun Liang, Mengchen Cao and Xiaosong Han
Energies 2022, 15(12), 4324; https://doi.org/10.3390/en15124324 - 13 Jun 2022
Cited by 3 | Viewed by 1318
Abstract
Oil drilling is the core process of oil and natural gas resources exploitation. Well overflow is one of the biggest threats to safety drilling. Prediction of the overflow in advance can effectively avoid the occurrence of this kind of accident. However, the drilling [...] Read more.
Oil drilling is the core process of oil and natural gas resources exploitation. Well overflow is one of the biggest threats to safety drilling. Prediction of the overflow in advance can effectively avoid the occurrence of this kind of accident. However, the drilling history has unbalanced distribution, and labeling data is a time-consuming and laborious job. To address this issue, an overflow-prediction algorithm based on semi-supervised learning is designed in this paper, which can accurately predict overflow 10 min in advance when the labeled data are limited. Firstly, a three-step feature-selection algorithm is conducted to extract 22 features, and the time series samples are constructed through a 500-width sliding window with step size 1. Then, the Mean Teacher model with Jitter noise is employed to train the labeled and unlabeled data at the same time, in which a fused CNN-LSTM network is built for time-series prediction. Compared with supervised learning and other semi-supervised learning frameworks, the results show that the proposed model based on only 200 labeled samples is able to achieve the same effect as supervised learning method using 1000 labeled samples, and the prediction accuracy can reach 87.43% 10 min in advance. With the increase in the proportion of unlabeled samples, the performance of the model can sustain a rise within a certain range. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Supply and Management)
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14 pages, 5306 KiB  
Article
International Natural Gas Price Trends Prediction with Historical Prices and Related News
by Renchu Guan, Aoqing Wang, Yanchun Liang, Jiasheng Fu and Xiaosong Han
Energies 2022, 15(10), 3573; https://doi.org/10.3390/en15103573 - 13 May 2022
Cited by 9 | Viewed by 2474
Abstract
Under the idea of low carbon economy, natural gas has drawn widely attention all over the world and becomes one of the fastest growing energies because of its clean, high calorific value, and environmental protection properties. However, policy and political factors, supply-demand relationship [...] Read more.
Under the idea of low carbon economy, natural gas has drawn widely attention all over the world and becomes one of the fastest growing energies because of its clean, high calorific value, and environmental protection properties. However, policy and political factors, supply-demand relationship and hurricanes can cause the jump in natural gas prices volatility. To address this issue, a deep learning model based on oil and gas news is proposed to predict natural gas price trends in this paper. In this model, news text embedding is conducted by BERT-Base, Uncased on natural gas-related news. Attention model is adopted to balance the weight of the news vector. Meanwhile, corresponding natural gas price embedding is conducted by a BiLSTM module. The Attention-weighted news vectors and price embedding are the inputs of the fused network with transformer is built. BiLSTM is used to extract used price information related with news features. Transformer is employed to capture time series trend of mixed features. Finally, the network achieves an accuracy as 79%, and the performance is better than most traditional machine learning algorithms. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Supply and Management)
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14 pages, 950 KiB  
Article
The Mediating Role of Public Health between Environmental Policy Tools and Economic Development
by Hui Xu, Wei Pan, Meng Xin, Cheng Hu, Wu-Lin Pan, Wan-Qiang Dai and Ge Huang
Energies 2022, 15(3), 835; https://doi.org/10.3390/en15030835 - 24 Jan 2022
Cited by 5 | Viewed by 2079
Abstract
Environmental pollution damages public health and affects economic development. Environmental regulation is the main way for the government to solve environmental pollution. So what type of environmental regulation works better for public health and economic development? Can environmental regulation have an influence on [...] Read more.
Environmental pollution damages public health and affects economic development. Environmental regulation is the main way for the government to solve environmental pollution. So what type of environmental regulation works better for public health and economic development? Can environmental regulation have an influence on economic development through public health? To solve these problems, this research uses China’s provincial panel data from 2013 to 2017 to divide environmental regulation into command-control policy tools and economic incentive policy tools and uses the mediating effect model to examine the relationship among environmental regulation, public health and economic development. The results show that: (1) There is a positive correlation between economic incentive policy tools and economic development; while no significant relationship between command-control policy tools and economic development is founded; (2) The relationship between command-control policy tools and public health is not significant, while the relationship between economic incentive policy tools and public health is positive; (3) Public health does not play a mediating role between command-control policy tools and economic development but plays a partial mediating role between economic incentive policy tools and economic development. Therefore, the government should strengthen the use of economic incentive policy tools to promote public health and sustainable economic development. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Supply and Management)
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19 pages, 5346 KiB  
Article
Estimation of Real-World Fuel Consumption Rate of Light-Duty Vehicles Based on the Records Reported by Vehicle Owners
by Isabella Yunfei Zeng, Shiqi Tan, Jianliang Xiong, Xuesong Ding, Yawen Li and Tian Wu
Energies 2021, 14(23), 7915; https://doi.org/10.3390/en14237915 - 25 Nov 2021
Cited by 7 | Viewed by 2912
Abstract
Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, [...] Read more.
Private vehicle travel is the most basic mode of transportation, so that an effective way to control the real-world fuel consumption rate of light-duty vehicles plays a vital role in promoting sustainable economic growth as well as achieving a green low-carbon society. Therefore, the factors impacting individual carbon emissions must be elucidated. This study builds five different models to estimate the real-world fuel consumption rate of light-duty vehicles in China. The results reveal that the light gradient boosting machine (LightGBM) model performs better than the linear regression, naïve Bayes regression, neural network regression, and decision tree regression models, with a mean absolute error of 0.911 L/100 km, a mean absolute percentage error of 10.4%, a mean square error of 1.536, and an R-squared (R2) value of 0.642. This study also assesses a large pool of potential factors affecting real-world fuel consumption, from which the three most important factors are extracted, namely, reference fuel-consumption-rate value, engine power, and light-duty vehicle brand. Furthermore, a comparative analysis reveals that the vehicle factors with the greatest impact are the vehicle brand, engine power, and engine displacement. The average air pressure, average temperature, and sunshine time are the three most important climate factors. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications in Petroleum Supply and Management)
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