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…
Integrated asset modeling, application of big data, and automation are among the top emerging trends in the oil and gas industry. The value associated with such implementation projects is very closely linked with the efficient use of the project…
Application of big data is analysed via the present information management of petroleum enterprises in this paper. The logical structure and application architecture are proposed based on hodiernal production, administration and management of oil…
Oil and Gas operations are now being "datafied." Datafication in the oil industry refers to systematically extracting data from the various oilfield activities that are naturally occurring. Successful digital transformation hinges critically on an…
"Big Data is the oil of the new economy" is the most famous citation during the three last years. It has even been adopted by the World Economic Forum in 2011. In fact, Big Data is like crude! Its valuable, but if unrefined it cannot be used. It must…
The application of big data and artificial intelligence (AI) technology has caused subversive changes to society and industry. As the life-blood of our industry, petroleum is an important material basis for promoting national economy and industrial…
Background: Since the introduction of the first electrical resistivity well log by Marcel and Conrad Schlumberger in 1927, the field of petrophysical well logging experienced significant technological advancements [3]. One of the new technologies was…
Big data-driven ensemble learning is explored in this paper for quantitative geological lithofacies modeling, which is an integral and challenging part of petroleum reservoir development and characterization. Quantitative lithofacies modeling…
This paper presents a workflow based on big data analytics to model the reliability of downhole Inflow Control Valves (ICVs) and predict their failures. The paper also offers economic analysis of optimum ICV stroking frequency to maintain valves…
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…
HSE (Health, Safety, and Environment) management is one of the most concerned matters of every business, especially in petroleum Industry. Currently, analyzing the origin of accident and tracing the responsibility of accident commonly happened after…
Big data refers to store, manage, analyze, and process efficiently a huge amount of datasets and to distribute it. Recent advancements in big data technologies include data recording, storage, and processing, and now big data is used in the refinery…
The transformation of big data has quietly infiltrated people's lives. Some advanced companies in the petroleum industry have tried to use big data analysis to more accurately determine well locations, reduce drilling accident rates, and reduce…
Due to the potential severity of oil and gas pipeline accidents, accurate assessments on the reliability and viability of pipelines in the petroleum industry is of paramount importance. Nevertheless, the safety factor (SF) parameter in some…
In this paper, the distribution of methyl dibenzofuran isomers (MDBFs) in petroleum hydrocarbon samples of different ages and sedimentary environments were analyzed by the GC-MS method and relevant software. Relevant parameters of MDBFs were put into…
Fugitive volatile organic compound (VOC) emissions from the petroleum-refining industry constitute a complex issue in China. This problem has a wide range of implications that require comprehensive measures, including a series of critical policing…
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…
Well metering is an important part of daily oilfield management. For wells in a block, production metering can help reservoir managers fully understand the changes in the reservoir and provide a basis for reservoir dynamics analysis and scientific…
Integrated asset modeling, application of big data, and automation are among the top emerging trends in the oil and gas industry. The value associated with such implementation projects is very closely linked with the efficient use of the project…
Application of big data is analysed via the present information management of petroleum enterprises in this paper. The logical structure and application architecture are proposed based on hodiernal production, administration and management of oil…
Oil and Gas operations are now being "datafied." Datafication in the oil industry refers to systematically extracting data from the various oilfield activities that are naturally occurring. Successful digital transformation hinges critically on an…
With the advent of the digital era, oil companies have invested more in obtaining and integrating basic data, and constantly improved the utilization of big data analytics, as an emerging trend, in oil and gas industries, with a view to discovering…
With the in-depth application of big data technology in the field of oil exploration and production. However, the data quality issues have seriously hampered the use of big data. This paper analyzes the evaluation methods of data quality at home and…
"Big Data is the oil of the new economy" is the most famous citation during the three last years. It has even been adopted by the World Economic Forum in 2011. In fact, Big Data is like crude! Its valuable, but if unrefined it cannot be used. It must…
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…
Complexity of the construction projects vary by the domain and type of the project. Due to the interaction between different disciplines and parties, Energy and Petroleum Projects (EPP) are considered among the most complex. This complexity produces…
In the past decade, the amount of data in the petroleum industry has increased rapidly, and the demand for data mining has continued to increase. The method of big data analysis can more scientifically guide the exploration and development, oil…
The application of big data and artificial intelligence (AI) technology has caused subversive changes to society and industry. As the life-blood of our industry, petroleum is an important material basis for promoting national economy and industrial…
The traditional IE system takes a long time to query petroleum information and has poor information management effect. Based on this, a petroleum engineering information system based on big data technology is designed. In the hardware design, the…
Developments of technology along with human work is growing, making the role of technology as an optimization effort in helping human work is needed. One of the technologies that is developing is Internet of Things or commonly abbreviated as IoT.…
Background: Since the introduction of the first electrical resistivity well log by Marcel and Conrad Schlumberger in 1927, the field of petrophysical well logging experienced significant technological advancements [3]. One of the new technologies was…
Big data-driven ensemble learning is explored in this paper for quantitative geological lithofacies modeling, which is an integral and challenging part of petroleum reservoir development and characterization. Quantitative lithofacies modeling…
Oil and gas production operations are key sources of environmental pollution which exposing the people and effect the human activity in the world. Petroleum Development Oman (PDO) is the leading exploration and production oil and gas companies in the…
Big Data technologies are now being actively integrated into the oil and gas sector owing to the need to improve operational efficiency and to optimize a variety of processes. Successful projects in data processing automation have already been…
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 paper presents a workflow based on big data analytics to model the reliability of downhole Inflow Control Valves (ICVs) and predict their failures. The paper also offers economic analysis of optimum ICV stroking frequency to maintain valves…
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…
HSE (Health, Safety, and Environment) management is one of the most concerned matters of every business, especially in petroleum Industry. Currently, analyzing the origin of accident and tracing the responsibility of accident commonly happened after…
With the continuously progressing of building the intelligent oilfield and the rapid development of the Internet of Things and big data technology, the emerging technologies such as the information acquisition, distributed computing and data mining…
Big data refers to store, manage, analyze, and process efficiently a huge amount of datasets and to distribute it. Recent advancements in big data technologies include data recording, storage, and processing, and now big data is used in the refinery…
The transformation of big data has quietly infiltrated people's lives. Some advanced companies in the petroleum industry have tried to use big data analysis to more accurately determine well locations, reduce drilling accident rates, and reduce…
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…
In this paper, the current status and research prospect of big data and intelligent optimization methods in oilfield development were reviewed and discussed, including the basic concepts and characteristics of the techniques, the production problems…
Due to the potential severity of oil and gas pipeline accidents, accurate assessments on the reliability and viability of pipelines in the petroleum industry is of paramount importance. Nevertheless, the safety factor (SF) parameter in some…
In this paper, the distribution of methyl dibenzofuran isomers (MDBFs) in petroleum hydrocarbon samples of different ages and sedimentary environments were analyzed by the GC-MS method and relevant software. Relevant parameters of MDBFs were put into…
Under the guidance of the 13th Five-Year Plan for Petroleum Industry Development, Petroleum enterprises are confronted with the problem of capacity clearing and improving industry concentration and mechanization. In this paper, the SEM model is used…
Fugitive volatile organic compound (VOC) emissions from the petroleum-refining industry constitute a complex issue in China. This problem has a wide range of implications that require comprehensive measures, including a series of critical policing…
Well metering is an important part of daily oilfield management. For wells in a block, production metering can help reservoir managers fully understand the changes in the reservoir and provide a basis for reservoir dynamics analysis and scientific…
Non-metallic materials such as plastics and elastomers are widely used for sealing in downhole operations of the oil and gas industry. These materials are used because of their inherent elasticity and resilience over a wide range of deformation, and…
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…