Like oil, data is a commodity with tangible value. The new black gold costs organizations millions of dollars to produce (think well logs, SCADA, leases, etc.). Importantly, energy companies make billion dollar decisions on their data, however, like gold, getting your hands on the right data to make crucial drilling or M&A decisions is ironically difficult as energy professionals are often knee deep in it. E&Ps and midstream companies need to get as good at gathering, processing, and distributing data as we have become with the physical movement of hydrocarbons.
In a sense, the digital oilfield is like the wild west where energy professionals spend an inordinate portion of their day on data wrangling because of a chaotic mix of structured and unstructured data, multiple versions of the truth, and lack of data governance. Every department in the energy enterprise is contending with increasing volumes, variety, and velocity of data, leading to analysis paralysis, delayed decision-making, and unnecessary rework (e.g., prior period adjustments). This “big data” dilemma often leaves important questions difficult to answer quickly, including:
- Do geoscience, drilling, and completion teams have the most accurate datasets to optimize well placement and performance?
- Are mineral and royalty owners getting paid based on accurate volumes and interest decimals?
- Do field and production teams have clear situational awareness to effectively manage assets following a merger or acquisition?
- Is compliance providing the most accurate production and environmental reporting to state and federal agencies?
- Are land, production operations, accounting, and regulatory making decisions with the current/correct well status?
Easy access to high quality oilfield data is crucial to your cash flow, such as the allocations that drive production accounting. Effectively managing data is also about mitigating risks.
For example, inconsistent use of data standards introduces the possibility of missing an obligation to interest owners if the land department uses API-12 while the drilling department uses API-14,preventing accurate tracking of a wellbore trajectory or new side track on a lease. Or consider a midstream example where fixed asset accounting depends on high quality location data for an interstate pipeline. Lat/long for preliminary right of way, permitted, and as-built designs can vary and with hundreds of miles to account for across dozens of city, county, and state lines, teams struggle to stay compliant. These examples share a similar risk where even a small discrepancy to asset location can have massive financial implications, underpayment to interest owners in the first and underpayment to state and municipal taxing authorities in the latter, all hinging on effective data management.
Over the course of the next few blogs, I’m going to delve deeper into what effective data management looks like for the energy industry. This starts with building a digital gathering system to bring disconnected data sources together where it can be aggregated, standardized, normalized, and distributed to systems and people. For organizations who do it well, data management provides a competitive edge in an increasingly digital oilfield that increases business performance and sets every department up for success.