Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response. Here, the challenge lies in the multimodal nature of urban big data. We propose a compact neural ensemble model to alleviate…
The prediction of reservoir fluid production law play a key role in offshore oil field development plan design. It determines the parameter selection of pump displacement, oilfield submarine pipe capacity, platform fluid handling capacity, power…
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…
Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various…
Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response. Here, the challenge lies in the multimodal nature of urban big data. We propose a compact neural ensemble model to alleviate…
The prediction of reservoir fluid production law play a key role in offshore oil field development plan design. It determines the parameter selection of pump displacement, oilfield submarine pipe capacity, platform fluid handling capacity, power…
Route optimization has been a research topic for many years in the maritime industry and it constitutes one of the key components to improving energy efficiency and sustainability in ship operations. This paper deals with the challenge of estimating…
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…
Demand forecasting in the energy sector is essential for both countries and companies to plan their supply and demand. Agents in the highly volatile oil markets have to act fast and data-driven. In the literature, studies on oil or gasoline demand…
This paper highlights the development and results of an innovative tool for prediction of process upsets and hazard events associated with production operations of an oil and gas field. Summarily, this software can give recommendations on actions to…
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the…
Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various…
Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies…
The process of long-distance hot oil pipeline is complicated, and its safety and optimization are contradictory. In actual production and operation, the theoretical calculation model of oil temperature along the pipeline has some problems, such as…
The objective of this paper is to demonstrate the process of unleashing the potential of digital oil fields by combining the power of Big Data platform with the Internet of Things (IoT). This new method enables efficient machine learning training…
The relative permeability test (RPT) plays an important part in production prediction, the law of water cut increasing analysis, the research on recovery factor and the reservoir numerical simulation. The residual oil saturation is one of the most…
Route optimization has been a research topic for many years in the maritime industry and it constitutes one of the key components to improving energy efficiency and sustainability in ship operations. This paper deals with the challenge of estimating…
Demand forecasting in the energy sector is essential for both countries and companies to plan their supply and demand. Agents in the highly volatile oil markets have to act fast and data-driven. In the literature, studies on oil or gasoline demand…
Predicting risk map of traffic accidents is vital for accident prevention and early planning of emergency response. Here, the challenge lies in the multimodal nature of urban big data. We propose a compact neural ensemble model to alleviate…
This paper highlights the development and results of an innovative tool for prediction of process upsets and hazard events associated with production operations of an oil and gas field. Summarily, this software can give recommendations on actions to…
The prediction of reservoir fluid production law play a key role in offshore oil field development plan design. It determines the parameter selection of pump displacement, oilfield submarine pipe capacity, platform fluid handling capacity, power…
Due to the complex structure and massive volume of large equipment in petrochemical enterprises, it was very difficult to carry out inspection and maintenance work. The traditional expert system used a single knowledge base and reasoning machine, so…
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the…
Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is…
Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies…
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…
The process of long-distance hot oil pipeline is complicated, and its safety and optimization are contradictory. In actual production and operation, the theoretical calculation model of oil temperature along the pipeline has some problems, such as…
The objective of this paper is to demonstrate the process of unleashing the potential of digital oil fields by combining the power of Big Data platform with the Internet of Things (IoT). This new method enables efficient machine learning training…
The relative permeability test (RPT) plays an important part in production prediction, the law of water cut increasing analysis, the research on recovery factor and the reservoir numerical simulation. The residual oil saturation is one of the most…
This study aims at revealing how commercial hotness of urban commercial districts (UCDs) is shaped by social contexts of surrounding areas so as to render predictive business planning. We define social contexts for a given region as the number of…
Analyses have been widely applied in production forecasting of oil/gas production in both conventional and unconventional reservoirs. In order to forecast production, traditional regression and machine learning approaches have been applied to various…
Due to the complex structure and massive volume of large equipment in petrochemical enterprises, it was very difficult to carry out inspection and maintenance work. The traditional expert system used a single knowledge base and reasoning machine, so…
Fluid properties are key factors for predicting single well productivity, well test interpretation and oilfield recovery prediction, which directly affect the success of ODP program design. The most accurate and direct method of acquisition is…
It is important for monitoring and predicting equipment failures. The existing fault prediction method has poor efficiency and accuracy on processing imbalanced data. This paper proposes a feature pattern-based LSTM method (called FLSTM, Feature…
Route optimization has been a research topic for many years in the maritime industry and it constitutes one of the key components to improving energy efficiency and sustainability in ship operations. This paper deals with the challenge of estimating…
Demand forecasting in the energy sector is essential for both countries and companies to plan their supply and demand. Agents in the highly volatile oil markets have to act fast and data-driven. In the literature, studies on oil or gasoline demand…
This paper highlights the development and results of an innovative tool for prediction of process upsets and hazard events associated with production operations of an oil and gas field. Summarily, this software can give recommendations on actions to…
Hydrocarbon production from shale formation has become an essential part of the global energy supply in the past decade. The life of a project in an unconventional play significantly depends on the prediction of Estimated Ultimate Recovery (EUR).…
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the…
This study aims at revealing how commercial hotness of urban commercial districts (UCDs) is shaped by social contexts of surrounding areas so as to render predictive business planning. We define social contexts for a given region as the number of…
Performance prediction is one of the important contents of oilfield development. It is also an important content affecting investment decision-making, especially for offshore oilfields with large investment. At present, most oilfields in China have…
Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies…
The objective of this study was to assess the feasibility of application of analytics techniques in a new heavy oil asset in Kuwait in the following areas: data integration and visualization to support Well, Reservoir and Facility Management (WRFM),…
The process of long-distance hot oil pipeline is complicated, and its safety and optimization are contradictory. In actual production and operation, the theoretical calculation model of oil temperature along the pipeline has some problems, such as…
The objective of this paper is to demonstrate the process of unleashing the potential of digital oil fields by combining the power of Big Data platform with the Internet of Things (IoT). This new method enables efficient machine learning training…
It is important for monitoring and predicting equipment failures. The existing fault prediction method has poor efficiency and accuracy on processing imbalanced data. This paper proposes a feature pattern-based LSTM method (called FLSTM, Feature…
The relative permeability test (RPT) plays an important part in production prediction, the law of water cut increasing analysis, the research on recovery factor and the reservoir numerical simulation. The residual oil saturation is one of the most…
In order to develop new materials and meet the requirements of ultra-deep petroleum and gas development, four prediction models for composition-yield strength and composition-hardness of large cross-sectional martensitic steel with the highest…
Hydrocarbon production from shale formation has become an essential part of the global energy supply in the past decade. The life of a project in an unconventional play significantly depends on the prediction of Estimated Ultimate Recovery (EUR).…
Due to the complex structure and massive volume of large equipment in petrochemical enterprises, it was very difficult to carry out inspection and maintenance work. The traditional expert system used a single knowledge base and reasoning machine, so…