(23b) Harness Big Data with Big Analytics

With the advent of the tsunami of data across the disparate
engineering silos in the oil and gas industry, it is evident that data-driven
models offer incredible insight turning raw Big Data into actionable knowledge.
I see geoscientists piecemeal adopting analytical methodologies that
incorporate soft computing techniques as they come to the inevitable conclusion
that traditional deterministic and interpretive studies are no longer viable as
monolithic approaches to garnering maximum value from Big Data across the
Exploration and Production value chain.

No longer is the stochastic and non-deterministic
perspective a professional hobby in any scientific industry as the array of
soft computing techniques gain credibility with the critical onset of technical
papers detailing the use of data-driven and predictive models.

Soft computing concepts incorporate heuristic information.
What does that mean? We can adopt hybrid analytical workflows to address some
of the most challenging business problems across not only the E&P value
chain but also across a wide array of scientific research.

I shall discuss a few case studies in the upstream oil and
gas industry as well as other scientific and engineering disciplines where big
data and big analytics address the three most prevalent issues: data
management, quantifying the uncertainty in the data and risk assessment.