As an industry, oil and gas is well-known for analyzing big data in the pursuit of resources. For instance, companies use seismic and geological surveys paired with advanced analytical techniques to find the best drill sites in three dimensions.
However, big data analytics is as necessary for eliminating inefficiencies in operational processes as it is for finding resources in the first place. Proven resource reserves are sensitive to market pricing, since the total cost of production varies greatly depending on the extraction techniques, difficulty of the working location and the transport costs for bringing products to refining facilities. There are significant differences in production costs between unconventional reserves such as deep-ocean and conventional shallow, land-based fields.
Where is the data?
Data analytics requires information first and foremost, and in the case of managing assets and resources for maximum efficiency, this requires an enterprise resource planning (ERP) tool. The ERP tool can include everything from a baseline inventory of assets to a real-time tracker of mobile assets with live routing assistance. Knowing exactly where exploration rigs, service vehicles and major spares are at all times can help companies avoid costly delays when equipment fails. Downtime directly affects how long it takes for the costs of exploration to become revenue, and poor asset management increases this lag time.
It is important for ERP tools in this environment to be supported through the cloud with flexible user interfaces that work in the field as well as in headquarters. This level of computational speed for real-time decision support is only found through in-memory computing platforms such as SAP HANA. Older database architectures and implementations are not able to deliver these results in a dynamic environment such as oil and gas field operation. Older systems can analyze history, but they do not support the business needs for effective decisions in real time.
It is a powerful advantage when companies can make operational decisions based on real-time asset information that feeds back into financial and human resources systems. It is necessary to mine this data in order to make decisions in the present and create long-term operational plans. This analysis also allows companies to efficiently allocate physical and financial assets when commodity markets fluctuate. Future casting scenarios based on operational costs of existing activities and various market pricing will prepare businesses to quickly take advantage of market changes. When automated workflow triggers are keyed to fieldwork and market price trends, the business can activate plans while the competition is still gathering information.
Big data analytics adds significant value to businesses when they move beyond analyzing historical data to find past trends and start using real-time support and predictive analytics. This is not to say historical data isn't needed — yesteryear's data is an important part of understanding past performance. The analysis of this data can find inefficiencies that may lead to operational changes, but predictive power comes from knowing which past decisions led to improved outcomes. This moves the business from making decisions based on gut feelings to making decisions based on actual data, thereby ensuring repeatable success.
Jason Hannula is a seasoned IT business analyst who has worked with various agencies to deliver transformation projects that improve business processes and data usage. He has also been writing about the impacts of emergent technologies and trends on businesses for since 2013.