Oil and gas producers are embracing big data and turning to advanced analytical tools to help with everything from seismic analyses to predicting production, but one area in particular is getting more attention than the others.
Drilling analytics is one of the hottest areas right now, according to Alyssa Farrell, global manager of energy and sustainability solutions for SAS.
“The goal is to use more real-time data in analytics so they’re getting data from maybe the last foot that they drilled. They want to be able to use that to inform the next foot that they drill versus just going on the drilling program,” Farrell said. “They are not quite there yet. In some cases, if they have downhole data they can get very close near real-time data analytics. That really helps with reducing any nonproductive time, improving the quality of the wellbore and all of that leads to improved production outcomes and faster time to first oil.”
SAS is among the companies researching which types of analytics return value during the drilling process, one of several areas where advanced analytics can benefit producers. Others include forecasting production based on various resource recovery techniques to extend the life of mature fields and better characterizing unconventional reservoirs such as shale gas or tight sands, Farrell said.
The oil and gas industry has begun to utilize analytics as easy oil disappears, sending companies into more geologically challenging and remote areas to find and produce hydrocarbons. The push to exploit big data and apply advanced analytics comes as operators in unconventional plays work to boost recovery rates, while oil and gas discoveries worldwide fall with energy demand forecast to increase.
With the increased interest in analytics has come the expectation for good-quality and consistent E&P data that are available when needed and at the level of granularity desired, Farrell continued. However, “I think one of the challenges moving forward is that this increased exposure to the opportunities that lie ahead with analytics may place some unanticipated pressure on the data stewards within E&P companies,” she said.
Software is available for data cleansing, text analysis and content categorization as well as data quality assessment, reconciling and data integration, among other tasks.
However, solutions to challenges in the oil patch are not just technology solutions.
“I think the broader challenge is getting the data processes in place to share those data as a company and making them available to those who are doing the analysis,” Farrell continued. Based on knowledge gained from focus groups, she said, “On a case-by-case basis [companies] may be successful, but they have difficulty repeating that success time and time again.”
The reasons vary. It could be that each project has a different combination of subcontractors or that a project calls for a different type of drilling technology. Another obstacle lies in the approach companies take when it comes to data governance. Some take a broad approach, while others take a project-by-project approach, Farrell said.
The future of advanced analytics will involve incorporating the first principles approach, which is the engineering- and physics-based approach to understanding a reservoir based on mathematical principles and laws.
“First principles guide most of what is done today in the upstream [sector] whether it is quantifying the asset in a reservoir, determining a drilling program or looking at how to modify resource recovery,” Farrell said. “What advanced analytics is doing is helping to augment first principles by using a data-driven approach. So it’s not replacing them; it’s supplementing them, and in some cases, making them more relevant to near real-time analytics.”
For example, a drilling program may be based on the first principles approach to what is known about a reservoir; however, actions may be modified daily based on analytical data acquired while drilling, she said. “This is making upstream more efficient, more safe and more reliable, allowing [companies] to drill fewer holes and get improved resource recovery from it.”
It’s also putting pressure on today’s workforce, she added, pointing out how increased interest in analytics has heightened the need for data scientists and others capable of applying such analytics to first principles.
Analytics will be a focus of SAS Day, scheduled for 8:30 am. to 1:30 p.m. Oct. 2 at Texas A&M University’s Mays Business School facility in Houston. The agenda includes sessions on case studies and predictive analytics and a keynote by author Keith Holdaway, principal solutions architect for SAS’ global oil and gas business unit. The event will give participants a chance to hear about not only big data and analytics but also their intersection with the Internet of things and about emerging technology like Hortonworks’ Hadoop, a framework for distributed storage and processing of large sets of data on hardware.
To register for SAS Day, visit http://w1.stat.tamu.edu/public-seminars/sasday2014/index.html.
Contact the author, Velda Addison, at email@example.com.