[show_avatar email=tim.voyt@emc.com align=left avatar_size=30]
One of the best parts of my job is that I get to work with E&P customers and partners in who are working to solve really interesting business challenges. What’s even more interesting to me is that more and more of these business challenges have an information technology component that will positively or negatively impact the overall efficiency and effectiveness of the ultimate solution. The intersection and (more often than not) inter-dependence of technology within business operations provides fertile ground for technology companies such as EMC to engage with our clients in line of business discussions rather than just traditional IT dialogues. As such, our customers and partners are looking to EMC to take an active role in innovative research and development projects that incorporate business workflows, process improvement efforts, application development, underlying infrastructure stacks, and data themes. It is good to see IT professionals having a “seat at the table” and being able to contribute real value when it comes to solving business challenges. The realization that IT can help create a business advantage rather than being viewed as just another expense has broad implications. Not only can the appropriate use of technology provide real, quantifiable benefit for the company that embraces it, it also drives technology innovation across the larger client/partner ecosystem.
Innovation is what sustains companies like EMC and the ability to engage in meaningful projects with our clients, partners, and even sometimes our competitors forces everyone to think along different lines. These multi-company, multi-discipline projects can produce not only cutting edge technology, but can also result in changes to business models. Take the Cloud movement (public, private, hybrid – you choose) as a case in point. Technology advances enabled effective movement of data and workloads into the Cloud and this in turn enabled shifts in business models for a certain segment of applications, data and workflows. The same thing is happening with the management, mobility and handling of Big Data.
While we in the O&G industry are “kicking the tires” on applying cloud technologies to upstream scientific applications (it’s coming, more on that in another blog post), we are most certainly the poster child for Big Data challenges. That is why EMC has invested heavily in O&G professionals, partnerships, engineering efforts, and a brand new O&G Big Data research center in Rio de Janeiro. I see some very exciting possibilities as I work with leading O&G companies and industry partners. Here is a sample of what I’m hearing:
- Reservoir Model Complexity Everyone is striving to build higher precision models that more accurately identify harder to find hydrocarbon, at deeper locations and hidden under more layers of fluid, salt and rocks; Technological advancements are enabling better data inputs and applications are allowing higher resolution models to be built. We need to figure out how to cost effectively build and leverage these models across an appropriate infrastructure stack.
- Speeding Up Analysis Cycles Geoscientists and engineers want to be able to do more iterations within and across disciplines to improve the interpretation and modeling of reservoirs and to cross correlate conclusions based on more intense collaboration. How do you ensure you have the right computing environment to do timely processing, model simulation, and analysis more often and faster, yet do so in a secure way to protect corporate intellectual property?
- Data Stream Proliferation The continued proliferation of sensor technology within drilling and production programs yields an ever growing amount of real time and near real time data. What’s the optimal way to integrate these data streams with historical data sets and extract more meaningful information? How can this data be harvested to extract better trending, correlation, and predictive analytics within the drilling and/or production process?
- O&G Data Life Span What is the best way to handle, protect, and recall data sets that have a life span of 50 years or more? Even if we can recall this data years from now, how do we ensure it’s in context so that the decision chain utilized by previous interpreters can be traced and understood? One step further, even if all the data can be retrieved in proper context and the decision chain reconstructed, will the original versions of applications, operating systems, and hardware components still exist to make any sense of the retrieved data?
- Enabling Global Collaboration Greater collaboration and automation technologies are being required to tap into a geographically diverse supply of industry professionals. Not everyone sits in Houston and better systems that promote cross continent collaboration in a seamless and secure manner need to be developed and deployed. How and when should Cloud paradigms be utilized (or not) within the E&P processes and what is possible today given current global infrastructure limitations?
These are just a sampling of areas where I see potential alignment of business processes and technology innovation. Each one of these represents a worthy challenge to tackle and could spawn multiple research tracks. What do you think? Please post your thoughts as I’d like to hear your views.