How big data analytics are changing the oil and gas industry

By Amit Mehta on 12/27/2017

HOUSTON -- Today, “big data” has become a ubiquitous phrase at any enterprise, primarily as data in volume, velocity and depth grows due to consumerization of the world. To understand its potential to change the oil and gas industry, one must first understand innovation dimensions and how they can be applied at any enterprise.

Enterprise Innovations usually can happen around three possible dimensions: Business, Operations and Technology.

Business innovation – changing the way people do business via a completely new and disruptive business model. For example, IBM transformed its business completely to go from selling hardware to selling services. Amazon launched cloud based-business via AWS and opened a completely new market category with new business model vs. simply being an online retailer.

Operational Innovation- optimizing a business or certain aspect of operations, by focusing on agility and efficiency. For example, Dell removed the middleman and connected consumers to order an entire PC online, Toyota via JIT, and Kanban philosophies removed all possible inefficiencies from its supply chain to manufacture cars.

Technology Innovation - a singular approach towards creating a product via R&D which can open new revenue streams e.g., Apple flooding the market with smart phone invention.

Defensive strategy

A range of big data initiatives are currently underway today in the entire oil and gas industry, all with different motivations and goals to enable innovations along primarily two dimensions.

For example, hardware service providers are experimenting with data analytics around R&D i.e. Technology Innovation to make their downhole tools much smarter and hence create technology edge via technology innovation. Some examples include potentially using robotics on rigs, predicting MSE at the bit, offering new advanced completion tools and intelligent plunger lift systems.

E&P companies are experimenting with data analytics to cut costs and attain operational efficiencies i.e. operational innovation. Examples are optimizing drilling and completions field operations by treating them as one vs. separately, optimizing enterprise workflows end-end to ensure knowledge workers can spend more time engineering vs. doing project management and admin-related tasks and enabling Integrated Asset Management model capability.

Providers of both hardware and software solutions are experimenting with data analytics to enable either Technology or Operational innovation or both. Examples include predicting tool failures, optimizing production operations, predicting look ahead, advice on sweet spots to maximize EOR and offering end-end integrated packages for Drilling, Completion and Production to serve as a “single source of truth.”

Offensive strategy

The industry is yet to see anyone in oil and gas eco system experimenting with analytics to enable true business innovation. A potential example of business innovation for hardware providers in drilling could be using analytics to disrupt the traditional day rate model and offer guaranteed rig uptime via a completely new business model. E&P companies could follow the lead of Amazon creating cloud-based business and create a new business category opening new revenue streams beyond just oil and gas.

Data analytics value chain

While everyone is investing in analytics, very few have been able to generate the kind of business impact they had hoped for yet. Irrespective of what innovation the players in eco system target, first they must understand the full data analytics value chain. The data analytics value chain encompasses the following: Problem definition, solution design, data preparation/ integration, analysis execution, interpretation, presentation and finally change management of stakeholders. Effective companywide analytics require looking at the full chain and any weak link could hurt the ability to deliver game changing impact irrespective on the innovation dimension it is targeting.

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