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Re-Imagining Data Management in Oil and Gas

Technology advances enable oil and gas companies the ability to unlock reserves at lower costs, at greater depths and in more remote locations, but currently the oil and natural gas industry is in one of the deepest downturns since the 1980's. Prices have dropped over 70% since mid-2014, resulting in the expected decline in investment and drilling activity. The industry has reached the bottom of the cycle with slowly increasing oil prices and a few more rigs going back to work, but this downturn has the characteristics for a "lower for much longer" scenario. With prices this low, the industry is taking a hard look at every aspect of their business including how they can use their collected data to improve operational efficiency and increase profitability.

Every company must get more out of the data they are collecting. Maximizing efficiency is essential to lower production costs in complex oil developments (deeper subsea/subsurface, higher pressures and temperatures, remote operations, etc.), not to mention the increasing volume and velocity of data from the Internet of Things (IoT). The value of information in the oil field has been proven but we seek new ways to use it to increase insight into operations and complex reservoirs to make better decisions which result in increased productivity and profitability.

Driving the new information trends are: digital intensity (increase in number and variety of sensors, field automation, smart equipment, increase in documents, increase in size of seismic surveys and reservoir models) and interconnected devices (remote decision support centers, remote control of processes, decrease in the use of proprietary networks and growth of internet, plus connected supply chains).

Operations Technology (OT) is emerging as a steward for engineering applications, operations and field automation (SCADA) systems. This area is rarely a corporate department. COOs in the oil and gas industry are usually assigned to a business unit or assets in a local geographic area. The growth of OT is happening from the "ground up", so to speak. Some companies have field automation standards but with legacy properties and many mergers and asset acquisitions, there is a complex diversity of solutions found in the field. This community is usually driven by local champions and operational teams.

The connection between OT and corporate IT has traditionally not been very formal or visible, but they do have a number of common issues such as: telecommunications, protocols, data access, architecture, mobility, and cybersecurity. Often these groups are struggling to find common solutions for patch management, upgrade and version changes and ways to bring data to engineering teams.

All these advances are going to make life interesting for the parties involved. Many advances in the Industrial Internet of Things favors OT over traditional IT, but all the data needs to ride on a common ICT backbone. With the increasing number of interconnections, a total security solution is needed. In order for sensor and machine data to match with transactions, documents and structured data, data management solutions must mature. The current tensions and often separation between OT and IT have to evolve into converged approaches. It is time to make friends, not enemies, in order to enable the digital oilfield.

But is our data foundation ready to enable provide accurate guidance?

There is no question that we need to re-think and re-imagine the role of data management in oil and gas to meet these challenges, but we cannot ignore the elephant in the room - data quality and data governance! The subject isn't popular and no one wants to talk about it because it is difficult to accomplish though the principles are well understood. One of the problems is that data management and information strategies are not highly regarded or said more plainly - these projects don't win recognition or promotions so are considered low priority and are last to receive resources.

However, a poor data management foundation, ineffective data governance processes and lack of alignment between engineering, operations and IT present barriers to the adoption of workflow optimization and advanced analytics solutions.

Data is often considered a personal or asset-specific possession and not a corporate asset. There are precious few Chief Data Officers and the lack of a coordinated strategy is reflected in the amount of data kept in informal spreadsheets and on shared drives.  Add to the equation the strong belief that standards hinder innovation, the internal IT department doesn't get what the engineering, earth science and operations groups are trying to do and many technical experts prefer customization to standardization. Finally, there those who think the promise of new emerging technology will eliminate the need for the hard work required to develop a robust data foundation and effective data governance framework.

In some corporate cultures, line management is often supportive of mavericks who operate outside enterprise standards because of a belief that the engineer is more productive doing it their way rather than being restricted to consensus best practices. Application rationalization and agreement on a company standards computing or data platform has proven difficult to achieve despite the obvious cost reduction benefits of supporting only a restricted standardized portfolio of tools and a trusted single source of data. Support for standards, either industry or company, often takes a back seat to customization and personal preferences.

Does this leave a company in an either/or situation? Either you let mavericks have non-standard pigeon holes of data in spreadsheets, or institute a police state of data quality management? Where does trusted data live and who owns it? Or is there another option?

If companies do not want to do the required work of data management but only want end results, there is a way to develop an effective data foundation using emerging digital technologies. This data-as-a-service platform would fill the slot currently role that should be played by internal company data management practices. But if internal efforts prove an insurmountable challenge, then starting again with an external alternative just may get the industry digital oilfield back on track.

We'll explore more about the data-as-a-service options in a later post.

Is it time to re-imagine the way you manage data? 

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