{"id":32777,"date":"2018-11-13T15:56:58","date_gmt":"2018-11-13T20:56:58","guid":{"rendered":"https:\/\/digital.hbs.edu\/platform-rctom\/submission\/digital-oilfield-paving-the-way-to-solving-the-imminent-global-energy-crisis-is-it-ready\/"},"modified":"2018-11-13T15:58:21","modified_gmt":"2018-11-13T20:58:21","slug":"digital-oilfield-paving-the-way-to-solving-the-imminent-global-energy-crisis-is-it-ready","status":"publish","type":"hck-submission","link":"https:\/\/d3.harvard.edu\/platform-rctom\/submission\/digital-oilfield-paving-the-way-to-solving-the-imminent-global-energy-crisis-is-it-ready\/","title":{"rendered":"Digital Oilfield: Paving the way to solving the imminent global energy crisis? Is it ready?"},"content":{"rendered":"

With global energy consumption on the rise, the energy industry is faced with pressures now more than ever to meet the global demand. The 2017 global energy consumption was 13.511 billion tonnes oil equivalent, an annual increase of 2.2%, the largest increase in global consumption since 2013, up from 1.2% last year. Most of the global energy consumption, 57.6%, was supplied by oil & gas. [1] Clearly, oil & gas is the backbone of the energy industry and hence the backbone of our world today, fueling all technological advancements and innovations in all fields.<\/p>\n

The question is, however, how long can we keep relying on oil & gas to keep this world going? With a world total proved reserves of 239.3 billion tonnes and an annual consumption of 7.78 billion tonnes (without considering the annual increase in consumption), our hydrocarbon resources can last for nearly just 30 more years. [1] How will we fill this gap in the future? Is there a way to optimize our oil & gas resources and prolong their lifetime to try and bridge this gap? One very promising answer is the Digital Oilfield.<\/p>\n

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[1]<\/p>\n

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Digital Oilfield<\/strong><\/p>\n

Machine learning at its core, the Digital Oilfield is a powerful combination of business process management, information technology and engineering expertise that utilizes advanced big data analytics, artificial intelligence, and the Internet of Things. Its main goal is to increase hydrocarbon exploration efficiencies and optimize oil & gas production. It can \u201clearn\u201d from what works in the best producing wells and apply those efficiencies to entire fields. It will predict equipment breakdown before it happens and bring about \u201ccondition-based\u201d maintenance rather than \u201cschedule-based\u201d methods. Ultimately, it will produce more oil & gas for less cost. [2] The Digital Oilfield market is expected to exceed $28.53 Billion by 2023. [3]<\/p>\n

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BHGE<\/strong><\/p>\n

In July 2017, the giant General Electric completed a merger between its oil & gas business, GE Oil & Gas, and Baker Hughes, the world\u2019s third largest oilfield service company to form Baker Hughes General Electric (BHGE), the world\u2019s only full-stream service company. BHGE\u2019s vision is to bring together digital solutions and technology from GE with Baker Hughes’\u00a0domain knowledge in the oilfield services sector. [4] Using GE\u2019s Predix big data technology and Baker Hughes\u2019 operational subject matter expertise, BHGE is at the forefront of the Digital Oilfield transformation. [5]<\/p>\n

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[5]<\/p>\n

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BHGE relies heavily on machine learning for both its process improvement and product development goals. It creates products and software to harness data from IoT connected devices and deliver machine learning predictive analytics with the goal of maximizing recovery, optimizing production, reducing nonproductive time, conducting predictive maintenance, improving safety, and enabling enterprise-wide digital transformation. Some of its major Digital Oilfield products are: Field Planning Optimization \u201cJewelSuite\u201d, Production & Recovery Optimization \u201cIntelliStream\u201d, Asset Performance Management \u201cAPM Software\u201d and Fit for Purpose Solutions \u201cAI Factory\u201d.[6] In the medium\/long term the company\u2019s strategic vision is to further develop its process improvement and product development by feeding its machine learning and big data models with massive amounts of data from the digitized oilfields and to harness that pool of data to expand its footprint in the digital transformation.<\/p>\n

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Challenges & Recommendations<\/strong><\/p>\n

For BHGE to succeed in both its short term and long-term goals, it must overcome two major challenges. First, to be able to effectively link machine learning analytics with actionable and tailored solutions to clients, BHGE must focus its resources on building cross-trained teams with the operational domain subject matter expertise, the digital expertise and business knowledge. This agile, cross-functional and client centric culture must be developed for the company to achieve its goals. Second, for the big data predictive models to be effective, it must be fed with massive amounts of data which poses a big challenge in the oil & gas industry given the high level of data confidentiality between major oil & gas players. Therefore, to tackle this issue, BHGE must be focused on forming strategic partnerships with major oil & gas companies to form a hub of shared data which can be fed into the big data models.<\/p>\n

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What\u2019s next?<\/strong><\/p>\n