scholarly journals Monitoring and analysing long-term vertical time-series deformation due to oil and gas extraction using multi-track SAR dataset: A study on lost hills oilfield

2022 ◽  
Vol 107 ◽  
pp. 102679
Author(s):  
Jiancun Shi ◽  
Bing Xu ◽  
Qi Chen ◽  
Miaowen Hu ◽  
Yirui Zeng
Keyword(s):  
Time Series ◽  
Oil And Gas ◽  
Gas Extraction

Long-term effects of income specialization in oil and gas extraction: The U.S. West, 1980–2011

2014 ◽  
Vol 45 ◽  
pp. 186-195 ◽  
Author(s):  
Julia Haggerty ◽  
Patricia H. Gude ◽  
Mark Delorey ◽  
Ray Rasker
Keyword(s):  
Oil And Gas ◽  
Gas Extraction ◽  
Long Term Effects ◽  
The U.S

scholarly journals Environmental Vulnerability in Pre-Salt Oil and Gas Operations

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 732
Author(s):  
Tatiana Farane Mein ◽  
André Luiz Veiga Gimenes ◽  
Eduardo Mario Dias ◽  
Maria Lídia Rebello Pinho Dias Scoton ◽  
Miguel Edgar Morales Udaeta
Keyword(s):  
Environmental Impact ◽  
Oil And Gas ◽  
Vulnerability Index ◽  
Gas Production ◽  
Gas Extraction ◽  
Oil And Gas Production ◽  
Environmental Vulnerability ◽  
Production Stages ◽  
Oil And Gas Operations

The objective of this work is to analyze disturbances in the environment caused by anthropic activities in the oil and gas extraction sector. Methodologically, focusing on environmental vulnerability (EV), hydrocarbons (oil and gas) are considered through a qualitative and quantitative analysis of environmental impacts, including the research of Environmental Impact Studies and procedures like EIA/RIMA (institutional Environmental Impact Reports in Brazil). This study focuses on the operation and demobilization of the offshore drilling activity and the installation and operation of the Santos Basin pre-salt oil and gas production (Stages 1, 2, and 3). The criteria addressed in the EIA/RIMAs are used, focusing on those that correlate with EV and oil and gas extraction. Impacts for long-term, permanent, partially reversible, or irreversible disturbances are filtered, totaling 53 impacts (31 effective/21 potential). We concluded that the criteria and methodologies of EIAs vary between stages. At times, the variation is so drastic that the same impact can have a completely different rating from one stage to another, despite referring to the same area. This condition makes it impossible to define a single vulnerability index for the pre-salt venture. This work does not offer a concrete resolution, but exposes the EV issue and its inconsistencies.

The Belt and Road Initiative: Goals and Challenges

2020 ◽  
pp. 167
Author(s):  
Adnan Khalaf i Hammed Al-Badrani ◽  
Hind Ziyad Nafeih
Keyword(s):  
Development Strategy ◽  
Soft Power ◽  
Oil And Gas ◽  
Power Lines ◽  
Belt And Road Initiative ◽  
Belt And Road ◽  
Ancient Silk Road ◽  
Main Engine ◽  
The Belt And Road

The Belt and Road Initiative is an initiative to revive the ancient Silk Road, through networks of land and sea roads, oil and gas pipelines, electric power lines, the Internet and airports, to create a model of regional and international cooperation.       It is essentially a long-term development strategy, launched by the Chinese president in 2013 to become the main engine of Chinese domestic policy and foreign diplomacy and within the framework of the soft power strategy, to enhance its position and influence in the world as a peaceful and responsible country.   The study includes identifying the initiative and setting goals for China, as well as the challenges and difficulties that hinder the initiative.

Trends and Prospects of Development of the Oil and Gas Sector in the Context of Digitalization

2020 ◽  
Vol 26 (1) ◽  
pp. 35-45 ◽  
Author(s):  
A. G. Kazanin
Keyword(s):  
Oil And Gas ◽  
Digital Technologies ◽  
Oil And Gas Industry ◽  
Global Market ◽  
The Arctic ◽  
Business And Society ◽  
Gas Industry ◽  
Exploration And Production ◽  
Oil And Gas Sector

The modern oil and gas industry is heavily dependent on the processes and trends driven by the accelerating digitalization of the economy. Thus, the digitalization of the oil and gas sector has become Russia’s top priority, which involves a technological and structural transformation of all production processes and stages.Aim. The presented study aims to identify the major trends and prospects of development of the Russian oil and gas sector in the context of its digitalization and formation of the digital economy.Tasks. The authors analyze the major trends in the development of the oil and gas industry at a global scale and in Russia with allowance for the prospects of accelerated exploration of the Arctic; determine the best practices of implementation of digital technologies by oil and gas companies as well as the prospects and obstacles for the subsequent transfer of digital technologies to the Russian oil and gas industry.Methods. This study uses general scientific methods, such as analysis, synthesis, and scientific generalization.Results. Arctic hydrocarbons will become increasingly important to Russia in the long term, and their exploration and production will require the implementation of innovative technologies. Priority directions for the development of many oil and gas producers will include active application of digital technologies as a whole (different types of robots that could replace people in performing complex procedures), processing and analysis of big data using artificial intelligence to optimize processes, particularly in the field of exploration and production, processing and transportation. Digitalization of the oil and gas sector is a powerful factor in the improvement of the efficiency of the Russian economy. However, Russian companies are notably lagging behind in this field of innovative development and there are problems and high risks that need to be overcome to realize its potential for business and society.Conclusions. Given the strategic importance of the oil and gas industry for Russia, its sustainable development and national security, it is recommendable to focus on the development and implementation of digital technologies. This is crucial for the digitalization of long-term projection and strategic planning, assessment of the role and place of Russia and its largest energy companies in the global market with allowance for a maximum number of different internal and external factors.

How Much Do U.S. State Governments Really Tax Oil and Gas Extraction?

2015 ◽  
Author(s):  
Jeremy Weber ◽  
Yongsheng Wang ◽  
Maxwell Chomas
Keyword(s):  
Oil And Gas ◽  
Gas Extraction ◽  
State Governments

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

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