Systems automation consumes digital data. Without reliable known data automation systems are inherently unstable. The problem is compounded when users do not own or control the measurements systems, as is required for interoperability. It is therefore important to understand the quality and reliability of the measurement systems being used in drilling automation, and to take steps to ensure that measurement systems are transparent. This webinar will explore significant work in this area, based on published SPE papers.
This first topic is based on SPE 174874 “A Framework for Transparency in Drilling Mechanics and Dynamics Measurements”, which examines the current confusion that exists in drilling mechanics and dynamics measurements. It recommends measurement practices and good processing techniques of these measurements, with examples, and presents a recommended open measurement framework. Adoption of this framework will resolve the current state of confusion and uncertainty, enable all parties to develop monitoring, advising and control applications that use these data, and help lower well costs and improve borehole quality.
The second topic is based on SPE 189626 “ Operator’s Group, Rig Contractors, and OEM/Service Company Work to Solve Rig data Quality Issues”, which looks at quality issues with rig measurement systems, proposing quality practices among data providers and consumers. Widely adopted practices will support and drive requirements for sensor quality, calibration, field verification and maintenance. This standardization enables improved drilling operations, automation, data analysis and big data processing.
The final topic will explore SPE 194082 “Creating Open Source Models, Test Cases, and Data for Oilfield Drilling Challenges” which proposes creating an open source repository of models, source code and data. The intent is to encourage the reuse of continuously improving models and coding efforts, and good data sets for verifying and validating the models. An open source repository for drilling will speed up the rate of learning and automation development.
