Core/Edge, AI/ML, Cloud…these words can be heard and seen several times a day across a variety of sources but what do they really mean and how does it impact an industry? 

Let me begin by stating that marketing organizations need to take an oath that they will stop using the above words and acronyms unless there is a direct correlation to the products they are positioning, and it can be explained in layman terms. The use of these words has become so widespread that they are used simply because they are in vogue without any contextual correlation.

I am going to take the acronyms mentioned above and relate them to the oil and gas industry so we can understand each of these words/acronyms in business context/value rather than techno-jargon.

Core/Edge: A simple concept of associating a central hub with widespread spokes and how communication transpires between them. ‘Core’ in an oil/gas operation could be a central data center where data captured from the edge (say an oil rig) is analyzed and converted into vital information for decision making. An ‘Edge’ would be an oil rig within which there are many sensors and data points that are captured including oil flow across pipes to the status of drilling bits. A simple example would be taking a drilling bit and knowing ahead of time when it is about to reach its end of life as the change process of replacing the bit is complicated and can take a significant amount of time. Rather than it being reactive, what if you could monitor the wear and tear of the bit via data captured about it on a daily basis and send it to the Core for processing. The Core would now have knowledge of not just the wear and tear on that bit but could compare it to other sites, bit types, correlate it to manufacturers, etc to determine what is the best bit to buy/use for a particular drill site/ use case. In addition, The Core could feed information back to the Edge, i.e. the rig on managing the use of the existing bit in the best manner to garner the greatest ROI. BTW did you know there are different types of drill bits such as roller, diamond and directional and each provides different value and functionality? Worth reading about, but in the interest of not boring you, I will skip the details. 

Artificial Intelligence (AI) and Machine Learning (ML) is the use of machine-based intelligence rather than human intelligence to drive decision making and or the execution of a decision. AI/ML is still in its early stages but evolving at a very rapid rate. The reason AI can evolve rapidly is the innovation of processors i.e. computer chips that can process at a tremendous speed (quantum computing) combined with the vast amount of data being captured, providing for millions/billions of data points to create pattern recognition, process understanding, and anomaly detection. The oil and gas industry goes through massive swings of boom and bust and as such there are large amounts of hiring during boom periods and significant layoffs during downturns. Many of the actions done today across a variety of roles from geophysicists to chemical and mechanical engineers entail understanding and analysis of data points. The use of AI/ML provides these oil and gas companies to process data points across a spectrum of sources multifold faster and more accurately than any human individually. In addition, the use of AI/ML avoids the need to hire/fire based on economic cycles but rather leverage automation to drive more cost-effective and consistent management of the size and skill of the workforce. In addition, AI/ML can play a large role in predictive analysis, take for example the bit discussion we were having earlier. Having a machine understand the data points to determine bit maintenance requirement, put an automated request to purchase a new bit and schedule a change window to swap out the bit creates tremendous efficiencies in the upstream process and has a direct impact on cost containment. According to a 2018 report by Price Waterhouse Coopers, AI/ML deployment in upstream processes could yield between $100 billion and $1 trillion dollars in capital and operating expenditure savings by 2025 for the industry. The impact of AI/ML and the ROI it provides is self-evident and is a major area of focus and investment within the oil and gas industry.

‘Cloud’ computing in the simplest of explanations is the ability to leverage the use of technology resources, (application, platform, infrastructure), on an ‘as need’ basis and to pay for such resources tied to a utility model. Cloud to most people resonates with a ‘public’ option such as Amazon Web Services (AWS) or Microsoft Azure or Google Cloud Platform (GCP). Public cloud providers facilitate a ‘service-based’ delivery model that is tied to using their resources on an as-needed basis and to be charged based on usage. But Cloud does not always need to be ‘public’, it can be ‘private’ or ‘hybrid’. Private clouds are set up by enterprises to drive an automated and transparent means of the use of technology resources and avail the ability to pay for it in a utility format. Private clouds are managed by an organization’s CIO and Central IT. A hybrid cloud is one that avails the services of a public and private cloud, leveraging the best of both worlds. If an application owner decides to create a new application, they may avail the services of a public cloud provider to quickly create and test basic functionality but then move it to a private cloud once the application needs to go into production. Another example is the use of applications that require large compute resources on a ‘one-off’ basis and as such, although in a private cloud, leverage burst compute capacity from the public cloud when needed. As mentioned earlier, the oil and gas industry generates a ton of data across a variety of sources. A single oil rig can generate between 1-2 terabytes of data per day. This data when extrapolated across the number of rigs globally amounts to a ton of data and that is just one segment of the data captured. As such, oil and gas companies end up with petabytes of data generated as a result of its upstream operations, which historically were stored in capital intensive storage arrays within company-owned data centers. Per Gartner, less than 1% of data generated is actually utilized in decision making. Thus, the storage of the data becomes a cost, not a business advantage if left unused. Many oil and gas providers are moving this data to the public cloud and closing up regional and global data centers. The reason for this move is the ability to drive cost efficiencies by tiering the data to the appropriate locations within the public cloud to meet business requirements as well as leverage the computing and analytics capabilities that public cloud providers offer to extract value from the data for informed decisions. 

According to a 2017 report by the World Economic Forum, Digital Transformation will generate USD $1.7 Trillion in value between 2016 and 2025 with a reduction in carbon emissions by 1.2 million tonnes. Technology is always viewed in terms of economic value and although valid, the oil and gas industry shows us that it can have an even more important and lasting impact, helping protect our planet if leveraged correctly. The digital transformation journey that the oil and gas industry has embarked upon is an amalgamation of Core/Edge, AI/ML, and Cloud to create a confluence of technologies that will have a positive ecological impact on our planet. 

Piyush Mehta

Piyush Mehta

Piyush has over 25 years of entrepreneurial experience, with the past 20 focused in software and technology services. Currently Piyush serves as CEO and a Board Member of Data Dynamics, Inc., a company he founded in 2012.

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