Transforming petroleum downstream sector through big data: a holistic review

Journal of Petroleum Exploration and Production Technology https://doi.org/10.1007/s13202-020-00889-2 REVIEW PAPER - PRODUCTION ENGINEERING Transforming petroleum downstream sector through big data: a holistic review Harsh Patel1 · Dhirenkumar Prajapati2 · Dharamrajsinh Mahida1 · Manan Shah3 Received: 27 November 2019 / Accepted: 9 April 2020 © The Author(s) 2020 Abstract 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 sector for the estimation of the energy efficiency and to reduce the downtime, maintenance, and repair cost by using various models and analytics methods. In the liquefied natural gas and city gas distribution industry, also, it is used in maintenance and to predict the failure of process and equipment. In this paper, authors have reviewed that how big data now used in the storage and transportation of oil and gas, health and safety in the downstream industry and to accurately predict the future markets of oil and gas. There are many areas where we can efficiently utilize big data techniques, and there are several challenges faced in applying big data in the petroleum downstream industry. Keywords Big data · Data science · Oil and gas · Production · Refinery Introduction Technological advancements in petroleum industry in recent years cause generation of huge amount of datasets in different sectors of petroleum industry including upstream, midstream, and downstream. Big data is the technology which helps oil and gas companies to handle and process these huge datasets from upstream, midstream, and downstream. International Data Corporation Energy took one survey in 2012, based on that about 70% of US oil companies were unaware from big data and its applications in oil and gas industry. Recently, there was a survey done by General Electric and Accenture and executives in which they found that 81% of them considered big data in the top priorities of oil companies. (Mohammadpoor and Torabi 2018). Even the renewable energy industry also uses the big data analytics to predict the energy generation by various * Manan Shah 1 School of Petroleum Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India 2 Petrowatch, Ahmedabad, Gujarat, India 3 Department of Chemical Engineering, School of Technology, Pandit Deendayal Petroleum University, Gandhinagar, Gujarat, India sources such as solar, wind, hydropower. Recently, Ifaei et al.’s (2018) case study on renewable energy in Iran took place, where they use clustering analysis (K-means) method to analyze the renewable energy sources in Iran. As a result, they found that the share of solar, wind, hydro- and biogas sources had average shares of 55.7%, 25.7%, 12.7%, and 5.9%, respectively. So, Iran can implement the solar and hydropower renewable sources for their green energy generation. There are various definitions of big data by different studies. Some of them are as below. Big data is the amount of data beyond the ability of technology to store, manage, and process efficiently (Manyika et al.2011; Jha et al. 2019; Kakkad et al. 2019; Kundaliya et al., 2020). Big data is a term which defines the hi-tech, high speed, high volume, complex, and multivariate data to capture, store, distribute, manage, and analyze the information (Shah et al. 2019; Patel et al. 2020a, b; Ahir et al. 2020; Parekh et al. 2020). Big data is high volume, high velocity, and/or high variety information assets that require new forms of processing to enable enhanced decision making, insight discovery, and process optimization (Jani et al. 2019; Patel et al. 2020a, b). Big data technologies are new generation technologies and architectures which were designed to extract value from multivariate high volume datasets efficiently by 13 Vol.:(0123456789) Journal of Petroleum Exploration and Production Technology providing high speed capturing, discovering, and analyzing (Gantz and Reinsel 2011; Shah et al. 2020a, b; Pandya et al. 2020; Sukhadiya et al. 2020). Hashem et al. (2015) defined big data by combining various definitions in the literature as follows: In the cluster of methods and technologies, new forms are integrated to unfold hidden values in diverse, complex, and high volume data sets (Gandhi et al. 2020). The use of big data applications is increasing day by day in energy sector and that creates significant opportunity in energy conservation, energy management, environmental protection and energy consumption and generated production data. A huge amount of data generated from wide spectrum of energy sources includes smart meter reading data, weather–climate data, asset management data, energy performance certificates, building stock editing, sustainable energy, socioeconomic data, and comfort levels etc. (Marinakis et al. 2018). In recent years, the global oil and gas industry has had to explore extremely turbulent waters after a long period of investment in super-capital assets and significant funding to help strengthen investments. In light of the recent surge in digital and data in different areas, oil companies need to consider digital technology advancements to take advantage of the additional benefits derived from the current income generation limit within the company. Digitization can be used to leverage additional resources to maximize growth in the oil and gas sector to create value in an interconnected energy system. “Data is the oil of the new economy” has been the most recognized reference in recent years. Global financial meetings have even embraced him. Be that as it may, there is so much to say about the huge data generated by the oil sector and its upstream part in particular. In the upstream part, understanding and using information allow companies to stay focused on themselves by organizing, investigating, representing, and progressing in the field. The 2D, 3D, and 4D geophysical organizations with oil and gas propulsion propelled by seismic sources generate remarkable information in the middle of the investigation phases. They almost control the execution of their operational resources. To do so, they use a large amount of information collection sensors in underground wells to provide consistent and consistent information to understand the benefits and environmental conditions. Unfortunately, these data come in different and progressively complex structures, making it a test for the collection, translation and use of divergent information. For example, Chevron’s internal computer movement alone exceeds 1.5 terabytes every day. Advances in huge information coordinate collections of normal and unique information to deliver the right data at the right time to the right leader. These capabilities enable 13 organizations to track large volumes of information, change core responsive leadersh (...truncated)