How to Use Big Data Technologies to Optimize Operations in Upstream Petroleum Industry

Baaziz, Abdelkader; Quoniam, Luc

doi:10.2139/ssrn.3429410

Cited by 39 publications

(31 citation statements)

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“…This fact is noted again in (Soo and Perez, ) where the authors note that Big Data in combination with advanced analytics can help perform identity traces. As (Baaziz and Quoniam, ) asserts, the analysis of 3D seismic data in terms of its geometry and comparing with neighbouring wells requires the processing of large quantities of data which enable a more accurate outlook for detecting hydrocarbon deposits. Magnetic resonance imaging (MRI) technology is used to identify rock and fluid properties and produces Big Data that can be stored and analysed (Blair, ).…”

Section: Big Data In Upstreammentioning

confidence: 99%

“…Furthermore, this work seeks to address the lack of relevant studies in the literature on the uptake and impact of Big Data in the P&P industry, as was also noted in (Vega‐Gorgojo et al ., ). After all, Big Data is the oil of the new economy (Baaziz and Quoniam, ) and the P&P industry is one where small improvements in efficiency and productivity can result in significant economic gains (Cowles, ).…”

Section: Introductionmentioning

confidence: 99%

“…Historically, within the P&P industry, Big Data was most commonly exploited for seismic interpretation software, to discover O&G reserves which would otherwise have been difficult to locate (Dyson, ). However, with the ever‐increasing volume of data, traditional analysis tools and storage technologies would now fail in capturing, managing and uncovering hidden information (Baaziz and Quoniam, ; Soo and Perez, ), and therefore P&P companies should give more attention and focus to Big Data and related technologies. Currently, most P&P companies fail to exploit the true potential of Big Data owing to a lack of collaboration, limited access to industry leading Big Data‐related computer programs and software processing tools, lack of data integration and lack of human capital (Lloyd's Register, ) with the knowledge and skills to apply Big Data techniques (Dyson, ).…”

Section: Introductionmentioning

confidence: 99%

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Big Data: a big opportunity for the petroleum and petrochemical industry

Hassani

Silva

2018

OPEC Energy Review

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The Petroleum and Petrochemical (P&P) industry is home to the most traded commodity in the world, i.e. oil. Recently, this industry has been struggling to make ends meet with top lines being affected by falling oil prices and bottom lines being squeezed further via increasing operational costs. It is against this backdrop that this paper seeks to identify and summarise the positive influence that the adoption of Big Data can have on the P&P industry. Exhaustive research is carried out on the industry's engagement and adoption of Big Data in upstream, midstream and downstream operations to concisely summarise the varied applications and the potential benefits. Our research indicates that the upstream sector is actively engaging with Big Data to achieve efficiency gains while the midstream and downstream sectors are lagging behind. Overall, it is evident that the P&P industry can find solutions to its aching financial and productivity issues by embracing of Big Data.

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Section: Big Data In Upstreammentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Big Data: a big opportunity for the petroleum and petrochemical industry

Hassani

Silva

2018

OPEC Energy Review

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“…While many companies in the oil and gas sector such as Chevron and Shell, have started looking into leveraging big data analytics, much more effort is needed to benefit from these opportunities in various areas. Baaziz and Quoniam (2013) identify areas that stand to benefit specifically in the upstream oil and gas industry (see (Feblowitz 2012, Nicholson 2012, Hems et al 2013, Seshadri 2013) for further details):…”

Section: Resource Explorationmentioning

confidence: 99%

Big Data and the Internet of Things

Shah¹

2015

Studies in Big Data

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Abstract. Advances in sensing and computing capabilities are making it possible to embed increasing computing power in small devices. This has enabled the sensing devices not just to passively capture data at very high resolution but also to take sophisticated actions in response. Combined with advances in communication, this is resulting in an ecosystem of highly interconnected devices referred to as the Internet of Things -IoT. In conjunction, the advances in machine learning have allowed building models on this ever increasing amounts of data. Consequently, devices all the way from heavy assets such as aircraft engines to wearables such as health monitors can all now not only generate massive amounts of data but can draw back on aggregate analytics to "improve" their performance over time. Big data analytics has been identified as a key enabler for the IoT. In this chapter, we discuss various avenues of the IoT where big data analytics either is already making a significant impact or is on the cusp of doing so. We also discuss social implications and areas of concern.

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“…First, consider an application called customer-360-view [1], which often requires to aggregate multiple data sources, including graph data from social networks, document data from product orders and customer information in a relation database. Second, in Oil & Gas industry [2], a single oil company can produce more than 1.5TB of diverse data per day [3]. Such data may be structured or semi-structured and come from heterogeneous sources, such as sensors, GPS devices, and other measuring instruments.…”

Section: Introductionmentioning

confidence: 99%

UDBMS: Road to Unification for Multi-model Data Management

Liu

et al. 2018

Lecture Notes in Computer Science

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A traditional database systems is organized around a single data model that determines how data can be organized, stored and manipulated. But the vision of this paper is to develop new principles and techniques to manage multiple data models against a single, integrated backend. For example, semi-structured, graph and relational models are examples of data models that may be supported by a new system. Having a single data platform for managing both well-structured data and NoSQL data is beneficial to users; this approach significantly reduces integration, migration, development, maintenance and operational issues. The problem is challenging: the existing database principles mainly work for a single model and the research on multi-model data management is still at an early stage. In this paper, we envision a UDBMS (Unified Database Management System) for multi-model data management in one platform. UDBMS will provide several new features such as unified data model and flexible schema, unified query processing, unified index structure and cross-model transaction guarantees. We discuss our vision as well as present multiple research challenges that we need to address.

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How to Use Big Data Technologies to Optimize Operations in Upstream Petroleum Industry

Abstract: Big Data is the oil of the new economy" is the most famous citation during the three last years.

Cited by 39 publications

References 1 publication

Big Data: a big opportunity for the petroleum and petrochemical industry

Big Data: a big opportunity for the petroleum and petrochemical industry

Big Data and the Internet of Things

UDBMS: Road to Unification for Multi-model Data Management

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