Harness Oil and Gas Big Data with Analytics: Optimize Exploration and Production with Data Driven Models

Preface

My motivation for writing this book comes from the cumulative issues I have witnessed over the past seven years that are now prevalent in the upstream oil and gas industry. The three most prominent issues are data management, quantifying uncertainty in the subsurface, and risk assessment around field engineering strategies. With the advent of the tsunami of data across the disparate engineering silos, 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 nondeterministic perspective a professional hobby 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. The Society of Petroleum Engineers has witnessed an incredible release of papers at conferences globally that provide beneficial evidence of the application of neural networks, fuzzy logic, and genetic algorithms to the disciplines of reservoir modeling and simulation. As the old school retire from the petroleum industry and the new generation of geoscientists graduate with an advanced appreciation ...