Big data analytics (BDA) is a maturing technology that is gaining momentum in the upstream oil and gas industry. The practice centers on aggregating all data from different sources into a "data lake" or equivalent storage for analytics and reporting. But if the quality of the data coming into the lake is unknown, uncertain, or poor, the results derived from analytics may not be reliable.

To help determine whether the data is fit for purpose, WITSML v2.0 has some significant new capabilities including a new Data Assurance object and improved metadata on the redesigned Log object.

Trusted data is the foundation for all analytical and reporting initiatives. The Data Assurance object does not determine the data quality; rather, it provides the means to transmit assurance that business policies and supporting rules are met in the data transfer process. This capability means that users can apply their own data quality processes, algorithms, and transformations to ensure the data are fit for purpose, auditable, and traceable to meet their business objectives. For example, data assurance policies and rules supporting sensor precision and calibration can be transferred between applications that validate the data according to a company's business requirements.

The newly designed Log object and addition of key metadata will address some of the historical organizational challenges of previous versions of the WITSML Log object and enable more intelligent data mining and more efficient and automated use of larger datasets. For example, the WITSML v2.0 Log object references classes of the Practical Well Log Standard (PWLS), an industry standard that lists and defines service company mnemonics. This capability supports a use case such as "give me all the sonic logs" regardless of vendor. As a side activity, but driven by the needs of WITSML users, the PWLS is also being updated.

The combination of these capabilities can help increase users' trust in their data, improve analytics, and ultimately help companies to realize more value from big data analytics and its ability to help upstream oil and gas improve safety, reduce operational risk, and improve efficiency.

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