Survey of Big Data New SQL Database Systems for Real-Time Data Transfer in the Upstream Oil and Gas Industry

Author(s):  
Basirudin Djamaluddin ◽  
Salahadin Mohammed
2020 ◽  
Vol 8 (5) ◽  
pp. 2582-2586

Automation and control systems are necessary throughout oil & gas industries, to production and processing plants, and distribution and retailing of petroleum products. Pipelines are the efficient mode of transportations of fuels for processing plants over long distances. At present Automation is achieved by using PLC’s that are communicated through SCADA. But it is complex and remote operation is not possible. With the introduction of IoT, the pipeline leak detection system is improved through real-time monitoring of the pipelines. Our Proposed system is designed to detect even small leakage that occurs within the pipeline. The implementation of IoT in oil and gas industries prevents accidents and to make quick decisions based on real-time data


2021 ◽  
Vol 73 (05) ◽  
pp. 56-57
Author(s):  
Judy Feder

This article, written by JPT Technology Editor Judy Feder, contains highlights of paper SPE 203461, “Digitalization in the Oil and Gas Industry—A Case Study of a Fully Smart Field in the United Arab Emirates,” by Muhammad Arif and Abdulla Mohammed Al Senani, ADNOC, prepared for the 2020 Abu Dhabi International Petroleum Exhibition and Conference, Abu Dhabi, held virtually from 9–12 November. The paper has not been peer reviewed. One of the first oil fields in the UAE to be fully operated remotely is in the southeast region, 250 km from Abu Dhabi. The complete paper discusses the development and commissioning of the field, which is the first smart field for ADNOC Onshore. The designed and applied technology facilitated unmanned operation of the field from downhole to export. Introduction Oilfield digitalization encompasses gathering real-time and non-real-time data from wells, flow lines, manifolds, stations, and water injection facilities; analysis of the data using algorithms, flowcharts, plots, and reports; and user access to this data on user-friendly screens. This allows engineers to focus on interpretation of data vs. searching, organizing, and formatting the data. In the bigger picture, the data collected will lead to conclusions and set bases for important decisions for similar projects in the future, enabling a lesson-learning approach to design new oil fields. The accumulated theoretical and practical research results of smart-field implementation require analysis and synthesis to maintain perspective of the entire project. Both were applied in the Mender field, which is the subject of the complete paper. Problem Statement The Mender parent field has been producing since 2013 with minimal digitalization for wellheads. Wells are not fit-ted with remote sensors, and operators have been visiting the wells to collect data using analog gauges. Collected data are stored in computers or as hard copies. Some critical data is lost, which affects decision-making. The new Mender field is 50 km from the parent field and is in a sensitive area close to international borders. The field area is a wildlife reserve for various endangered animals. The nature of operations is highly critical because of concentrations of hydrogen sulfide (H2S) that could jeopardize employees’ health and safety.


Author(s):  
E. B. Priyanka ◽  
S. Thangavel ◽  
D. Venkatesa Prabu

Big data and analytics may be new to some industries, but the oil and gas industry has long dealt with large quantities of data to make technical decisions. Oil producers can capture more detailed data in real-time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. Stream computing is a new way of analyzing high-frequency data for real-time complex-event-processing and scoring data against a physics-based or empirical model for predictive analytics, without having to store the data. Hadoop Map/Reduce and other NoSQL approaches are a new way of analyzing massive volumes of data used to support the reservoir, production, and facilities engineering. Hence, this chapter enumerates the routing organization of IoT with smart applications aggregating real-time oil pipeline sensor data as big data subjected to machine learning algorithms using the Hadoop platform.


2021 ◽  
Author(s):  
Jesus Manuel Felix Servin ◽  
Hala A. Al-Sadeg ◽  
Amr Abdel-Fattah

Abstract Tracers are practical tools to gather information about the subsurface fluid flow in hydrocarbon reservoirs. Typical interwell tracer tests involve injecting and producing tracers from multiple wells to evaluate important parameters such as connectivity, flow paths, fluid-fluid and fluid-rock interactions, and reservoir heterogeneity, among others. The upcoming of nanotechnology enables the development of novel nanoparticle-based tracers to overcome many of the challenges faced by conventional tracers. Among the advantages of nanoparticle-based tracers is the capability to functionalize their surface to yield stability and transportability through the subsurface. In addition, nanoparticles can be engineered to respond to a wide variety of stimuli, including light. The photoacoustic effect is the formation of sound waves following light absorption in a material sample. The medical community has successfully employed photoacoustic nanotracers as contrast agents for photoacoustic tomography imaging. We propose that properly engineered photoacoustic nanoparticles can be used as tracers in oil reservoirs. Our analysis begins by investigating the parameters controlling the conversion of light to acoustic waves, and strategies to optimize such parameters. Next, we analyze different kind of nanoparticles that we deem potential candidates for our subsurface operations. Then, we briefly discuss the excitation sources and make a comparison between continuous wave and pulsed sources. We finish by discussing the research gaps and challenges that must be addressed to incorporate these agents into our operations. At the time of this writing, no other study investigating the feasibility of using photoacoustic nanoparticles for tracer applications was found. Our work paves the way for a new class of passive tracers for oil reservoirs. Photoacoustic nanotracers are easy to detect and quantify and are therefore suitable for continuous in-line monitoring, contributing to the ongoing real-time data efforts in the oil and gas industry.


Author(s):  
Joseph Hlady ◽  
Somen Mondal

The use of Radio Frequency Identification (RFID) has grown substantially in the past few years. Driven mostly by the retail supply chain management industry and by inventory control (loss prevention), RFID technology is finding more acceptance in the security and personal tracking sectors beyond simple pass cards. This growth has of course resulted in greater acceptance of RFID technology and more standardization of process and systems as well as decreased per unit costs. The oil and gas industry is being exposed to the potential use of RFID technology, mostly through the safety and equipment inspection portion of construction management. However, the application of RFID technology is expected to expand to the material tracking and asset management realms in the near future. Integrating the information provided by RFIDs with EPCM project and owner/operator Geographic Information Systems (GIS) is a logical next step towards maximizing the value of RFID technology. By linking assets tracked in the field during movement, lay-down and construction to a GIS, projects will have accurate, real-time data on the location of materials as well as be able to query about those assets after commissioning. This same capability is being modified for post-commission use of RFID with facility GISs. This paper outlines how existing GISs used during the EPCM phases and those employed after commissioning can display, utilize and analyze information provided by RFID technology.


2021 ◽  
Author(s):  
Henry Ijomanta ◽  
Lukman Lawal ◽  
Onyekachi Ike ◽  
Raymond Olugbade ◽  
Fanen Gbuku ◽  
...  

Abstract This paper presents an overview of the implementation of a Digital Oilfield (DOF) system for the real-time management of the Oredo field in OML 111. The Oredo field is predominantly a retrograde condensate field with a few relatively small oil reservoirs. The field operating philosophy involves the dual objective of maximizing condensate production and meeting the daily contractual gas quantities which requires wells to be controlled and routed such that the dual objectives are met. An Integrated Asset Model (IAM) (or an Integrated Production System Model) was built with the objective of providing a mathematical basis for meeting the field's objective. The IAM, combined with a Model Management and version control tool, a workflow orchestration and automation engine, A robust data-management module, an advanced visualization and collaboration environment and an analytics library and engine created the Oredo Digital Oil Field (DOF). The Digital Oilfield is a real-time digital representation of a field on a computer which replicates the behavior of the field. This virtual field gives the engineer all the information required to make quick, sound and rational field management decisions with models, workflows, and intelligently filtered data within a multi-disciplinary organization of diverse capabilities and engineering skill sets. The creation of the DOF involved 4 major steps; DATA GATHERING considered as the most critical in such engineering projects as it helps to set the limits of what the model can achieve and cut expectations. ENGINEERING MODEL REVIEW, UPDATE AND BENCHMARKING; Majorly involved engineering models review and update, real-time data historian deployment etc. SYSTEM PRECONFIGURATION AND DEPLOYMENT; Developed the DOF system architecture and the engineering workflow setup. POST DEPLOYMENT REVIEW AND UPDATE; Currently ongoing till date, this involves after action reviews, updates and resolution of challenges of the DOF, capability development by the operator and optimizing the system for improved performance. The DOF system in the Oredo field has made it possible to integrate, automate and streamline the execution of field management tasks and has significantly reduced the decision-making turnaround time. Operational and field management decisions can now be made within minutes rather than weeks or months. The gains and benefits cuts across the entire production value chain from improved operational safety to operational efficiency and cost savings, real-time production surveillance, optimized production, early problem detection, improved Safety, Organizational/Cross-discipline collaboration, data Centralization and Efficiency. The DOF system did not come without its peculiar challenges observed both at the planning, execution and post evaluation stages which includes selection of an appropriate Data Gathering & acquisition system, Parts interchangeability and device integration with existing field devices, high data latency due to bandwidth, signal strength etc., damage of sensors and transmitters on wellheads during operations such as slickline & WHM activities, short battery life, maintenance, and replacement frequency etc. The challenges impacted on the project schedule and cost but created great lessons learnt and improved the DOF learning curve for the company. The Oredo Digital Oil Field represents a future of the oil and gas industry in tandem with the industry 4.0 attributes of using digital technology to drive efficiency, reduce operating expenses and apply surveillance best practices which is required for the survival of the Oil and Gas industry. The advent of the 5G technology with its attendant influence on data transmission, latency and bandwidth has the potential to drive down the cost of automated data transmission and improve the performance of data gathering further increasing the efficiency of the DOF system. Improvements in digital integration technologies, computing power, cloud computing and sensing technologies will further strengthen the future of the DOF. There is need for synergy between the engineering team, IT, and instrumentation engineers to fully manage the system to avoid failures that may arise from interface management issues. Battery life status should always be monitored to ensure continuous streaming of real field data. New set of competencies which revolves around a marriage of traditional Petro-technical skills with data analytic skills is required to further maximize benefit from the DOF system. NPDC needs to groom and encourage staff to venture into these data analytic skill pools to develop knowledge-intelligence required to maximize benefit for the Oredo Digital Oil Field and transfer this knowledge to other NPDC Asset.


Author(s):  
M. Asif Naeem ◽  
Gillian Dobbie ◽  
Gerald Weber

In order to make timely and effective decisions, businesses need the latest information from big data warehouse repositories. To keep these repositories up to date, real-time data integration is required. An important phase in real-time data integration is data transformation where a stream of updates, which is huge in volume and infinite, is joined with large disk-based master data. Stream processing is an important concept in Big Data, since large volumes of data are often best processed immediately. A well-known algorithm called Mesh Join (MESHJOIN) was proposed to process stream data with disk-based master data, which uses limited memory. MESHJOIN is a candidate for a resource-aware system setup. The problem that the authors consider in this chapter is that MESHJOIN is not very selective. In particular, the performance of the algorithm is always inversely proportional to the size of the master data table. As a consequence, the resource consumption is in some scenarios suboptimal. They present an algorithm called Cache Join (CACHEJOIN), which performs asymptotically at least as well as MESHJOIN but performs better in realistic scenarios, particularly if parts of the master data are used with different frequencies. In order to quantify the performance differences, the authors compare both algorithms with a synthetic dataset of a known skewed distribution as well as TPC-H and real-life datasets.


Author(s):  
Rizwan Patan ◽  
Rajasekhara Babu M ◽  
Suresh Kallam

A Big Data Stream Computing (BDSC) Platform handles real-time data from various applications such as risk management, marketing management and business intelligence. Now a days Internet of Things (IoT) deployment is increasing massively in all the areas. These IoTs engender real-time data for analysis. Existing BDSC is inefficient to handle Real-data stream from IoTs because the data stream from IoTs is unstructured and has inconstant velocity. So, it is challenging to handle such real-time data stream. This work proposes a framework that handles real-time data stream through device control techniques to improve the performance. The frame work includes three layers. First layer deals with Big Data platforms that handles real data streams based on area of importance. Second layer is performance layer which deals with performance issues such as low response time, and energy efficiency. The third layer is meant for Applying developed method on existing BDSC platform. The experimental results have been shown a performance improvement 20%-30% for real time data stream from IoT application.


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