A Song of Ice and Data


    Jun 21, 2019

    Data science is a key that can unlock the secrets to better business. By better managing and understanding resources, companies can drive down costs, even in areas where they don’t actively seek it. If you know where and how to optimize your business, you can understand what can be automated and, from the digital perspective, move past the opaque way of doing things and optimize processes based on data.

    The problem is that decision-makers often don’t know where the inefficiencies are.

    Saving Costs, Lives & Business with Data & AI

    The oil and gas industry employs multi-modal logistics in that different kinds of transportation must be considered together to improve efficiency. Sites or fields are often located in remote areas several days from a transport hub. The oil fields in Siberia, for example, are located in extreme geographical and weather conditions that, to get there, require a cargo train, a redistribution warehouse, trucks, and helicopters – there are also hundreds of other parameters to consider such as lead and arrival time, repair time, cost, and the visibility of the transportation.

    One of my friends happened to work on a drilling project in Siberia. The company was moving all the components required to the far north, which involved a tremendous amount of facilities and equipment like pumping stations, tanks, oil wells, and rigs. To work well, maintenance, repairs, and the constant checking of operating parameters is an ongoing task. However, precisely delivering spare parts to a remote icy area is no walk in the part when multiple transportation modes are required. 

    It’s also worth a quick reminder of what Siberia is: a vast expanse of permafrost, with slight surface melt and stretches of boggy ground during short summers, dotted with mountains and rivers. 

    The only real way to get there is on a helicopter or a small plane. An oil company may have to transport tens of thousands of workers by air, in addition to cargo flights.

    Oil and gas companies spend a massive amount of money on helicopters. In 2016, five leading companies in the sector – CHC Group, Bristow Group, Petroleum Helicopters, Era Group Inc и HNZ Group Inc – reported how much their customers pay for helicopter services per year:

    • Statoil Norway: US$$284 million 
    • Petrobras: US$235 million
    • Royal Dutch Shell: US$210 million

    Oil and gas companies also own their own choppers, which can double overall costs. For example, Russian Rosneft spends US$175 million dollars on helicopter companies and the same amount on their own – an eye-watering total of US$350 million.

    Data Meets Business

    For executives, it’s a business problem to reduce logistics costs and improve safety. For data scientists, it’s an equation with many variables, with a thorough analysis of operations helping to spot suboptimal or inefficient activities. In the project that this friend of mine was involved in, modeling the traffic flows using optimization software showed that the most efficient way to connect to distant geographies is the use of large helicopters, whereas the company had been assuming that lighter models were better because they’re more economical. In fact, larger helicopters proved to be more economical and faster in the long-term.

    Another way of optimizing operations is by better managing materials inventories and spare parts located in numerous places. Building one system that can calculate and predict inventory costs could bring down costs significantly.

    Some would argue that oil and gas sector will lose its viability as the costs of oil exploration and extraction go up, renewable energy initiatives gather steam around the world, and the public become increasingly aware of protecting the environment. However, oil has a vast number of uses – including in clothes and medicine, meaning that the need for it will never go away.

    Threatened by more requirements and competition, it’s time for oil and gas companies fully apply data management techniques to their businesses.

    The takeaway here is that assumptions we make are not always the best solution for any given scenario – the best solution may even be counterintuitive.

    A common expression in the ICT industry is “data is the new oil.” However, oil is the old oil and it needs data + AI.

    Disclaimer: Any views and/or opinions expressed in this post by individual authors or contributors are their personal views and/or opinions and do not necessarily reflect the views and/or opinions of Huawei Technologies.


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