Part 1. The Generation Y Opportunity
Part 2. Data Driven Models for the Real World
Part 3. What Next for Advanced Analytics?
Leveraging your data to take advantage of the big crew change
One of the greatest challenges facing our industry today is managing the ‘Big Crew Change’. Anticipating the impending shortage of technically proficient personnel as they exit stage right, we are faced with not only an experience gap, but also a fundamental difference in the way that the next generation thinks. What cannot be quantified is the extent of the impact of this demographic shift. What type of changes in efficiency and performance might we anticipate if about 20 percent of the business has fewer than 5 years of pertinent experience?
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 by coupling expert knowledge that is readily retiring from the petroleum industry with data-driven models to explore and predict events resulting in negative impacts on CAPEX and OPEX.
The question we should be considering is: “How can Information Technology not only bridge the experience gap, but also enable the next generation to carry the torch of oilfield innovation to new and greater heights?”
Generation Y vs. Generation X and Baby Boomers
Before we begin to consider what the answer may be, let us first understand the underlying principal differences between the various generations within our workforce, Figure 1.
The key to any IT strategy allowing the new generation is to comprehend and quantify some significant and fundamental tenets. The Baby Boomers were great innovators of electronic technologies, and Generation X that followed had to understand the fundamentals inherent in these technologies so that they could develop and evolve the plethora of software solutions that have dynamically changed the world we live in today. This resulted in both generations being labeled as Technology Savvy. However, Generation Y has generally not been exposed to the underlying technology solutions, but rather have been technology enabled. In short, their focus is not on how things work, but on what they can achieve with technology. Their focus is on the enablement rather than the comprehension. Just ask your children to plug in that new games console and you’ll see what we mean. Yet once it is up and running there is only one winner.
Enabling Generation Y to Utilize Data
So what does this mean as regards managing the crew change in Oil and Gas? The answer is relatively simple. If we consider the innovation of electronic technologies in terms of an oil and gas reservoir, Generation X and the baby boomers have achieved some pretty amazing numerical solutions such as reservoir simulators. However, these solutions have been created and evolved around first principles and empirically acceptable mathematics and physics. What is unknown and uncertain are managed by assumptions and estimates. As Generation Y enters the work force they are less likely to discuss the underlying principles and more likely to consider these assumptions and estimates in greater detail. Generation Y embark upon a new way of thinking and a new paradigm for problem solving. So Generation X’s prevailing intuition on how things work, for example in reservoir simulation and modeling, may not necessarily apply when Generation Y adopt an enablement strategy to resolve the complexity inherent in reservoir modeling. They are wondering how they can enable themselves to better understand the life-cycle of a reservoir based on technology enablement. Put simply, can we use the volumes of information that have been collected to quantify these uncertainties without getting into the weeds of traditional empiricism that furnish the physics based numerical models?
Figure 2 illustrates how Generation Y places itself at the center of the relationship between information and ordinary activities such that they have a more strategic approach to problem solving that leverages data-driven models bounded by engineering constraints inherent in physics based models.
Let us illustrate the Generation Y stance with an example in E&P, focusing on Artificial Intelligence (AI) in reservoir simulation and modeling. It is generally accepted that AI represents the capability of machines to imitate or even eclipse human intelligence in routine engineering and scientific tasks that are centered on perception, reason and action. AI like human intelligence is by nature multifaceted, attaining goals that range from knowledge representation and reasoning, to comprehension as well as visual perception. Generation X has a history of misusing and ultimately squandering such soft computing technology, consequently adopting a stance to misjudge and prematurely dismiss the perceived benefits of AI when applied to reservoir simulation and modeling, preferring to fall back on conventional and well established workflows and technology that invariably succeeds in the hands of an experienced team of engineers and geoscientists.
Generation Y’s timely position in the technology evolution married with a strategic perspective enables the adoption of the data-driven technologies and machine learning such that reservoir models are built on the observed phenomena. These observations are represented in the form of data. Let the data do the talking. This refreshing approach has witnessed the birth over the past decade of top-down intelligent reservoir modeling and simulation methodologies based on surrogate reservoir models where the manacles of empirical algorithms are loosened if not entirely discarded.
The current status of the Oil and Gas industry’s demographics already paints a doomsday scenario necessitating a large influx of new hires over a condensed timespan. One factor that positions this crew change in a different light is the drive for new hires is mandated by demographics, and thus the industry will have serious staffing issues in spite of the level of hiring activity. There is nothing to hinder an influx of inexperienced new hires. The difficulty will be to train them to be fully able and competent very quickly without the traditional indulgence of having them learn the hard way through on the job training, exposing them to a period of trial and error. The most critical question is: Are we really exposed to have a substantial loss in performance?
In the next article we shall discuss in more detail how data-driven models are changing the world. We shall enumerate what the Oil and Gas industry can garner from other businesses as regards improving not only workplace efficiency and collaboration threaded by IT and data-driven methodologies, but also enabling Generation Y to take Innovation in Oil and Gas to another level.