The days of easy oil are over. No longer can a wildcatter don a hardhat, set up a derrick, and strike it rich in Oklahoma. Operators must now use 3D printing to unlock hard-to-reach resources.
By collecting data and utilizing machine learning, the midstream oil and gas industry is improving operational efficiency.
Companies like DrillingInfo are helping develop new machine-learning applications for the oil and gas industry
“The energy patch is ripe for opportunities related to things like predictive analytics, machine learning, artificial intelligence projects” – Tim Dove, Pioneer Natural Resources CEO (June 2017) 
In 2015 and 2016 the UK Government took a new approach to attract interest to its oil &amp; gas resources, adopting an open innovation model as a short and long term value driver in the country’s energy sector. First steps included large investments in acquisition of seismic data to be later published openly to academia and companies. Further measures included the creation of an oil &amp; gas national data repository to provide enhanced and trusted data on the widest possible terms. In this essay, I describe the UK Government’s incentives to adopt this megatrend; additionally, some further questions are outlined in relation to the sector’s motivations to further commit to this development process.
Exploring machine learning developments in the oil and gas industry
Exploring how Shell has leveraged machine learning to adapt the era of low oil prices through predictive maintenance, optimization and safety applications
Canada’s oil sands mining industry faces the entry of self-driving haul trucks as a means to lower operating costs. In an economic environment of low crude oil prices and an unclear future for oil sands mining, is this an investment Suncor Energy should commit to?
“Data is just like crude. It’s valuable, but if unrefined it cannot really be used.” 
British Petroleum (BP) is currently developing blockchain technology that will enable efficiencies in the oil and gas supply chain.