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.

Significant improvements in remote drilling operations have been made in recent years. With proper positioning, these advances could help mitigate the challenge of replacing a retiring workforce. Another concern when transitioning from a tenured employee to one with less experience is work efficiency. While data from the USA Federal Bureau of Labor Statistics has shown the trend for injury accidents has been steadily declining, incidents of Non Productive Time (NPT) have gone up significantly. It appears the traditional approach of passing knowledge to the next generation of engineers has been broken negatively impacting the drilling industry. To address the disturbing trend, a Middle East operator has been investigating various technologies to facilitate improvement and reduce NPT. The provider has applied a strategic combination of services and modeling systems to achieve the objective including a real-time ANN drilling parameter optimization system that provides information to ensure maximum run length from all bits and downhole tools at the highest possible ROP. Benefits of the system include extended tool life, fewer trips and the ability to manage the bit's dull condition. The provider's mud logging managed the operation locally at the well site, while their drilling and measurement personnel monitored the processes at the operations service center in the Middle East. In Houston, the bit company offered technical assistance effectively covering the drilling operation continuously. The unified team approach involved getting the operator engaged as a key member in the planning and execution stage. The process yielded higher data visibility, making it easier to identify and reduce NPT. It also aided in improving workflow and furnished optimized drilling parameters at the rig-site to ensure improvement in drilling efficiency and higher ROP. By leveraging real-time data, operators can optimize drilling efficiency to help bridge the experience gap until the next generation of drilling engineers are fully trained.