Petroleum geoscientists and engineers will rely heavily on automation and statistical analytics in the near future in most disciplines, particularly for geomodeling. The recent downturn in the industry and the sluggish return to more favorable oil prices, coupled with the impending “big crew change,” will spawn fewer domain experts to produce viable drilling projects, particularly in unconventionals. Industry will look to universities to broaden their petroleum curricula in order to cultivate students with wider disciplinary expertise – expertise that enriches their principal domain. Graduates will not only need to blur the divisions between geoscience and engineering, but they will also need to have substantial expertise in areas like data science and computer engineering. This means that geoscientists and engineers will further develop left-brain and right-brain activities, respectively.
The implications to geomodeling, the building of static models, are significant. In fact, we can foresee the merging of static and dynamic models to produce a single geo-reservoir model. Current barriers to produce such a model like grid size, identification of key variables, flow equations, and time-steps, will be broken down in the presence of cloud computing, seamless workflows, machine learning, and advanced particle physics that overcome barriers between solid and fluid mechanics, which are already working their way into our space from other disciplines.
While this rather diverse set of skills may reduce deep expertise in any one individual sub-discipline, the iconic domain experts of today will very likely be smart, cloud-based machines. These computers will talk to each other and enrich themselves through constant, real-time assessments of massive data stores, and deliver exploration and production nuggets of cognitive learnings to new-age petroleum scientists. In the near future, geoscientists and engineers will behave much more like pilots than carpenters.