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6 Aspects of Data Quality that Align with Problems

When looking at controlling costs as an oil and gas operators, it is important to look at the data quality for what you are using to solve problems and to measure your ROI. Do you have complete enough data at a sufficient rate for analytical analysis? What makes quality data? Here are six aspects data quality issues that we have seen:

Coverage – Represents all available data streams for each well / lift-type across a diverse population of assets. Good data goes beyond sensors. Data includes sensors from multiple devices, well design, maintenance history, production, etc.

Connected – Is naming consistent across devices? Various naming conventions causes confusion as teams look in their respective systems for the “other” names of the wells. How easily can data  be extracted from core systems and/or repositories for evaluation, profiling, transformations and modeling.

History – Does historic data show changes in well behavior that correlate to problems and covers a population of events with enough variation / examples.

Continuity – How much source data is available without gaps or lapses? Faulty sensors and communication failures lead to an incomplete picture.

Granularity – Low frequency / granular data can mask problems. With higher frequency data, we can diagnose earlier and take corrective action.

Latency – Is data recent and relevant for modeling so we can diagnose a problematic event earlier to take corrective action sooner.

We are here to help you navigate the challenges faced today, and to work with you and your organization towards improved data quality. In our next post, we’ll look more closely at three of these aspects of data quality – Continuity, Granularity, and Latency.

We offer Solutions to help overcome these challenges! Take a look at our Unified Monitoring Solutions and request access to an online demo to view at your convenience.  If you would like to launch your digital oilfield today, contact us now!