Oilfield Technology
This article originally appeared in Oilfield Technology.
While companies in industries such as logistics, consumer products and utilities are discovering for the first time the power of advanced analytics, executives in the oil and gas industry have been analysing huge volumes of data to make technical and strategic decisions for decades. In their search to find where hydrocarbons lie deep beneath the surface, oil and gas companies have invested in seismic software, visualisation tools and other digital technologies for many years.
Now, the rise of pervasive computing devices—affordable sensors that collect and transmit data—as well as new analytic tools and advanced storage capabilities are opening more possibilities every year. Oil producers can capture more detailed data in real time at lower costs and from previously inaccessible areas, to improve oilfield and plant performance. For example, they can pair real-time downhole drilling data with production data of nearby wells to help adapt their drilling strategy, especially in unconventional fields.
These analytic advantages could help oil and gas companies improve production by 6-8%—percentages that are typical across industries according to Bain & Company’s 2014 survey of more than 400 executives across sectors. Analytical leaders, however, are still the exception. The survey showed that only around 4% of companies across industries have the capabilities to use advanced data analytics to deliver tangible business value.
While some oil and gas companies have invested in their analytics capabilities, many struggle to get their arms around this powerful new opportunity. Senior executives usually understand the concepts around Big Data and advanced analytics, but their teams have difficulty defining the path to value creation and the implications for technology strategy, operating model and organisation. Too often, companies delegate the task of capturing value from better analytics to the IT department, as a technology project.
Instead, the business should lead analytics work because it requires cross-functional ownership and participation. Some companies commit to ambitious technology transformations in search of analytic nirvana, but these transformations often fail to generate enough value along the way.