WITSML v2.0: Paving the Way for Big Data Analytics Through Improved Data Assurance and Data Organization

摘要

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.