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 of statistics and soft computing methodologies, we shall evolve even greater application across the upstream. The age of the Digital Oilfield littered with intelligent wells generates a plethora of data that when mined surface hidden patterns to enhance the conventional studies. Marrying first principles with data-driven modeling is becoming more popular among earth scientists and engineers.

This book arrives at a very opportune time for the oil and gas industry as we face a data explosion. We have seen an increase in pre-stack analysis of 3D seismic data coupled with the derivation of multiple seismic attributes for reservoir characterization. With the advent of permanently in-place sensors on the ocean bed and in the multiple wells drilled in unconventional reservoirs across shale plays, coal seam gas, steam-assisted gravity drainage, and deep offshore assets, we are watching a proliferation of data-intensive activity.

Soft computing concepts incorporate heuristic information. What does that mean? We can adopt hybrid analytical workflows to address some of the most challenging upstream problems. Couple expert knowledge that is readily retiring from the petroleum industry with data-driven models that explore and predict events resulting in negative impacts on CAPEX and OPEX. Retain the many years of experience by developing a collaborative analytical center of excellence that incorporates soft skills and expertise with the most important asset in any oil and gas operation: data.

I would like to take this opportunity to thank all the contributors and reviewers of the manuscript, especially Horia Orenstein for his diligent expertise in predictive analytics and Moray Laing for his excellent feedback, expertise in drilling, and contribution with the pictures that illustrate many case studies. Stacey Hamilton of SAS Institute has been an encouraging and patient editor, without whom this book would never have been completed. I would like to acknowledge my colleagues in the industry who have given constructive feedback, especially Mike Pittman of Saudi Aramco, Mohammad Kurdi, David Dozoul and Sebastian Maurice of SAS Institute, ensuring the relevance and applicability of the contents.