Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing…
Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing…
Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing…
In recent years, there has been a significant increase in the quantity of data generated from monitoring technologies for subsurface operations such as permanent downhole sensors, as well as cross-hole and seismic surveys. Traditional models and…
In recent years, there has been a significant increase in the quantity of data generated from monitoring technologies for subsurface operations such as permanent downhole sensors, as well as cross-hole and seismic surveys. Traditional models and…
In recent years, there has been a significant increase in the quantity of data generated from monitoring technologies for subsurface operations such as permanent downhole sensors, as well as cross-hole and seismic surveys. Traditional models and…
Time series patterns analysis had recently attracted the attention of the research community for real-world applications. Petroleum industry is one of the application contexts where these problems are present, for instance for anomaly detection.…
Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing…
Agricultural Internet of things (AIoT) promotes the modernization of traditional agricultural production and marketing model. However, the existing time series prediction methods for agricultural production and agricultural product (AP) marketing…
At present, there is an increasing demand for big data analysis in various industries. Big data is characterized by large data scale, long calculation cycle, and high resource performance requirements for distributed computing systems. For the…
The climate change and various pollutions have been influencing our societies and economies. The environmental assessment, to be discussed in this study, is increasingly important because it serves as an initial step toward pollution prevention.…
This chapter introduces typical cases of artificial intelligence and big data application in oil and gas industry. In the upstream field, it introduces how to combine digital rock physics with big data and AI to optimize recovery efficiency. Digital…
Underground water-sealed oil-storage caverns still face many challenges after completion, such as oil leakage and excessive water inflow into caverns. However, there is a lack of high quality and efficient operation precedent for large-scale…
Energy resources have acquired a strategic significance for economic growth and social welfare of any country throughout the history. Therefore, the prediction of crude oil price fluctuation is a significant issue. In recent years, with the…
Price fluctuations in the crude oil market is important to both financial practitioners and market participants, since it not only affects investors’ investment, portfolio allocation and risk evaluation, but also influences strategic planning and…
Petroleum exploration and production is an industry that provides researchers with multi-variant challenging "real world" properties. Recently, some petroleum soft computing techniques have gained a greater interest in prediction within the oil…
The determination of flow state remains an important challenge in non-perennial stream catchments. To identify periods of flow and no-flow, previous studies deployed temperature sensors on streambed surfaces and interpreted the resulting time series…
The Gravity Recovery and Climate Experiment (GRACE) mission has been providing abundant information regarding the mass changes of the Earth in terms of time-series of temporal gravity field models since 2002. To derive temporal gravity field models…
Fuel represents one of the main transport costs and, consequently, of a logistical operation. So, a computational tool that allows reliable forecasts on the fuel prices becomes a competitive differential for the logistics operator, especially in a…
This paper proposes alternative methodologies for oil price forecasting using mixed-frequency data and a textual sentiment indicator. The latter variable was extracted from oil market reports issued by the Energy Information Administration. We used…
This chapter introduces typical cases of artificial intelligence and big data application in oil and gas industry. In the upstream field, it introduces how to combine digital rock physics with big data and AI to optimize recovery efficiency. Digital…