Gaining insights from the dense network of interrelated documents involved in E&P projects requires experience, knowledge, and awareness about the existence of the required data. This framework aims to facilitate the decision-making process while…
The concept of digital transformation is based on two principles: data drivenexploiting every bit of data sourceand user focused. The objective is not only to consolidate data from multiple systems, but to apply an analytics approach to extract…
New technologies like big data, cloud computing, machine learning could play a vital role in providing healthcare services to patients. The healthcare industry is producing a large volume of data which is increasing exponentially. Healthcare industry…
The oil & gas industry has been value added from our digital assets since this new century, which helped our industry dig out more advanced algorithm, more robust logic to address the challenge from HPHT wells and deep-water wells. Nowadays the…
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 rapid improvement of exploration and monitoring technologies, the oil and gas industry has accumulated a large amount of data in the fields of seismic exploration, logging, production, and development. How to transform the huge "data…
Digital twin is the innovation backbone of the smart manufacturing by delivering virtual representation of the real world. Aiming at constructing virtual representations of visual scenes, scene graph generation is a digital twin task that not only…
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…
today, multimedia database management is the most important for the new generation and specifically in big data issues for instant saving, calling and modifying operations for many types of documents as text, video and audio. A huge number of…
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…
This paper reviews how upstream oil and gas organizations can rapidly dissect an expansive volume of data by taking steps to amalgamate all the data by combining the operational data and drilling rig sensor data to real-time windows using advanced…
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…
This paper reports the development and tests of an advance methodologies to predict Upstream plant risky events, such as flaring, applying an integrated framework. The core idea is to exploit Machine Learning and big data analytics techniques to…
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…
With Managed Pressure Drilling (MPD) getting more common, its rubber seals increasingly become a source of potential downtime during drilling operations. The common approach is to provide monitoring towards relevant stakeholders via expert…
Gaining insights from the dense network of interrelated documents involved in E&P projects requires experience, knowledge, and awareness about the existence of the required data. This framework aims to facilitate the decision-making process while…
The concept of digital transformation is based on two principles: data drivenexploiting every bit of data sourceand user focused. The objective is not only to consolidate data from multiple systems, but to apply an analytics approach to extract…
New technologies like big data, cloud computing, machine learning could play a vital role in providing healthcare services to patients. The healthcare industry is producing a large volume of data which is increasing exponentially. Healthcare industry…
The oil & gas industry has been value added from our digital assets since this new century, which helped our industry dig out more advanced algorithm, more robust logic to address the challenge from HPHT wells and deep-water wells. Nowadays the…
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 rapid improvement of exploration and monitoring technologies, the oil and gas industry has accumulated a large amount of data in the fields of seismic exploration, logging, production, and development. How to transform the huge "data…
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…
Smart devices in an Internet of Things (IoT) generate a massive amount of big data through sensors. The data is used to build intelligent applications through machine learning (ML). To build these applications, the data is collected from devices into…
Synthetic data generation is generally used in performance evaluation and function tests in data-intensive applications, as well as in various areas of data analytics, such as privacy-preserving data publishing (PPDP) and statistical disclosure…
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…
This paper reviews the utilization of Big Data analytics, as an emerging trend, in the upstream and downstream oil and gas industry. Big Data or Big Data analytics refers to a new technology which can be employed to handle large datasets which…
This paper presents the analytics of physics-driven big data in reservoir hydrodynamic simulation and parameter optimization for EOR projects in Daqing oilfield. An application model proposed in this study enables reservoir engineers to dynamically…
Digital twin is the innovation backbone of the smart manufacturing by delivering virtual representation of the real world. Aiming at constructing virtual representations of visual scenes, scene graph generation is a digital twin task that not only…
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…
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…
Diagnostic fracture injection tests (DFIT's), or "mini-fracs" are often used to gauge many reservoir and fracture design parameters. However, DFITs are not always conducted in conjunction with the main completions work. This paper proposes a novel…
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…
today, multimedia database management is the most important for the new generation and specifically in big data issues for instant saving, calling and modifying operations for many types of documents as text, video and audio. A huge number of…
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…
This paper reviews how upstream oil and gas organizations can rapidly dissect an expansive volume of data by taking steps to amalgamate all the data by combining the operational data and drilling rig sensor data to real-time windows using advanced…
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…
In recent years, there has been a proliferation of massive subsurface data sets from sources such as instrumented wells. This places significant challenges on traditional production-data-analysis methods for extracting useful information in support…
This paper reports the development and tests of an advance methodologies to predict Upstream plant risky events, such as flaring, applying an integrated framework. The core idea is to exploit Machine Learning and big data analytics techniques to…
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…
With Managed Pressure Drilling (MPD) getting more common, its rubber seals increasingly become a source of potential downtime during drilling operations. The common approach is to provide monitoring towards relevant stakeholders via expert…
Petrophysics is a pivotal discipline that bridges engineering and geosciences for reservoir characterization and development. New sensor technologies have enabled real-time streaming of large-volume, multi-scale, and high-dimensional petrophysical…
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 objective of this paper is to share the results and benefits from a new artificial intelligence and predictive data analytics process. This new process integrates both geoscience and engineering requirements to enhance non-metallic pipe…
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 the informatization and intellectualization era of oil and gas, automation is essential to measure and track detailed performance for routine drilling operations by automatically measuring these individual operations consistently. Presently, the…
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…
Big data has become a major topic in many industries. Most recently, the oil and gas industry adopted a special interest in data science as a result of the increasing availability of public domains and commercial databases. Utilizing and processing…