Digital oilfield

Intelligent Fields Technology-2018

What I find impressive is the number of people who were hiding in normal discipline jobs who are coming out of the closet with their Python scripts. And, it’s working. In many ways, order is coming to the mess, efficiency is coming to tiresome manual activities, and richness is coming to decisions.

I’m sure you’ve seen it very obviously happening all around us. Yet, looking at the details still surprises. While reviewing the papers published in the intelligent-fields area this year, I was struck by the contrast I saw compared with just 3 years ago. Novel and niche are giving way to systemic and pervasive. What only recently was the domain of academics and research types with larger operators and service companies has broadened to an amazing diversity of practitioners.

The papers mirror what I see and hear in many of the companies I interact with. Yes, our companies have hired more people with formalized training in data science, but what I find impressive is the number of people who were hiding in normal discipline jobs only a few years ago who are coming out of the closet with their Python scripts. And, it’s working. In many ways, order is coming to the mess, efficiency is coming to tiresome manual activities, and richness is coming to our decisions.

So, what changed that we are more rapidly seeing the promised progress?  

I associate much of the acceleration to what I would call an open-source mentality, an approach that prefers to find an appropriate, available solution that is easily accessible, rather than developing or buying something fit-for-purpose. “There’s an app for that” has evolved to marketplace models, not only on your smart phone but also now in the Jupyter notebook on your desktop or in the marketplace of your cloud environment. As a result, or perhaps as a driving part of the changes, tech giants such as Amazon and Microsoft are finding their part in the energy sector by providing convenient and efficient marketplaces supporting integration of open-source and proprietary technologies. Smaller companies and startups can deliver low-cost solutions to such environments, and cooperative developments such as the Open Earth Consortium will bring further efficiencies by delivering standard oil-and-gas-specific frameworks. Instead of armies of developers delivering the next generation over 5 or 10 years, a capable community is emerging that can deliver a multitude of small advances that build on synergies of existing capabilities.

I hope to see you at the SPE workshop on Smart Integration in Production System Modeling on 19–20 June in Galveston, Texas, USA.

This Month's Technical Papers

Intelligent Completion in Laterals Becomes a Reality

A Neural-Network Approach for Modeling a Water-Distribution System

Embedded Discrete Fracture Modeling With Artificial Intelligence in Permian Basin

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John Hudson, SPE, has more than 25 years of experience in subsurface software, flow assurance, production-system design, and technology development. He has held technical and managerial positions in Shell at locations in Europe and North America, providing consultancy to a diverse set of assets globally. Hudson’s activities have included the development of a model-based, cloud-deployed, real-time operational support system for major gas-production systems. He is currently Americas regional support and development manager for subsurface and wells software. Hudson holds a PhD degree in chemical engineering from the University of Illinois. He serves on the JPT Editorial Committee and can be reached at www.linkedin.com/in/hudsonjohnd.