In the next ten years, instantaneous value from digital oilfield systems will dramatically alter the oil and gas landscape as cost and operational efficiencies are attained through the reliance on artificial intelligence. The digital oilfield will radically change how oilfield workers, machines, and the holistic enterprise operate to achieve results and compete in the new digital world. The new digital oilfield will be a disruptive technology that creates new value streams for exploration and production ranging from automated decisions and reactions in real time to massively improved operational efficiencies, connected infrastructure platforms, and much better interaction between machines and humans. The days of collecting and storing large volumes of data for later analysis will become a distant memory. The digital oilfield will change expectations for all aspects of our industry ranging from how fast decisions are made to detecting patterns the human eye cannot see in order to take advantage of the insights quicker. Data silos will be reduced and information shared across all areas of the oil company. At a simple level, artificial intelligence will be used to increase the accuracy of predictions to near-cognitive robotic comprehension in machine learning.
To quote Erik Brynjolfsson, director of the MIT Initiative on the Digital Economy, " Humans must adapt to collaborate with machines, and when that collaboration happens, the end result is stronger." This session will outline three ways advanced analytics and artificial intelligence can help bridge the gap between the digital oilfield and analytics:
predictive analytics from a case study in predictive asset failures,
machine learning in completions, and
text analytics in drilling. The session will outline the people, process and technologies needed to enable this infrastructure. Additionally, key pitfalls to avoid regarding systems, silos, and the human barriers to understanding artificial intelligence and getting past the hype associated with new technologies will be discussed.
Historically, exploration and production companies have benefited from high oil prices. The industry has been able to hide operational inefficiencies due to large budgets permitted by high oil prices. Financial risk was often ignored because the expectation of the next return on investment was enough to justify the risks. Recent changes in the economics of the oil and gas industry have caused exploration and production companies to rethink their budgets and to institute cost-cutting measures to mitigate the loss of revenue. Outdated technologies and practices are under continual scrutiny to ensure that budgets are met as margins tighten. This has resulted in oil and gas companies instituting data analytics as a means of improving cost savings. (Mitchell, 2016)