In the current economic climate Operators must reduce drilling costs, so they are turning to well data analytics, real-time advisory, and automation systems to make sustainable improvements (Behounek et al. 2017). Rig surface sensor data is critical to improvement; however, documented issues with consistent, reliable, quality data complicates and delays the value from these systems. The Operators Group for Data Quality (OGDQ) seeks to accelerate the adoption of standardized key measurement specifications, data storage, transmission, transformation, and integration by working with Rig Contractors, Original Equipment Manufacturers (OEMs), and Service Companies. The OGDQ effort focuses on key measurements used for important drilling process decision making.

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Declining marginal profits demand that operating companies and drilling contractors review their practices and examine ways to optimize processes and reduce unplanned expenditures. One area that has been largely overlooked is iron roughneck make-up torque. Until recently, most iron roughnecks had no manufacturer's recommended method to field-verify their accuracy and no way to digitally record or transmit the torque applied to one or more connections. This has undoubtedly resulted in unnecessary expense from lost and damage drillpipe and other downhole tools. The economic impact of the problem is significant and warrants structured, collaborative approach for a solution.

In mid-2014, a collaborative group, including an operator, a drilling contractor and multiple vendors and original equipment manufacturers (OEMs), began the first parts of a six-sigma DMAIC (Define-Measure-Analyze-Improve-Control) cycle to improve makeup torque process capability. The effort involved the manufacture of a fit-for-purpose tool to measure applied torque and an exhaustive study of multiple makes, models, contractors, operators and operating conditions. The results of the study demonstrate that the process of making up drillpipe with iron roughnecks is uncontrolled (from a statistical perspective). It is also shown that the tools, as they currently exist, deviate significantly from customer-defined process capability.

Further study is required to address all the common and special cause variations in the process. Moreover, OEMs, contractors, and vendors must work together to implement process controls.

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Language submitted to IADC Contracts Committee encompasses QAQC, instrumentation, data storage and transmission, interoperability, data integration

By Dung Nguyen, ConocoPhillips, and Tim Wiemers, Shell Global Solutions (US)

Using Big Data visualization and analysis tools for drilling and completions operations can help personnel to more effectively and consistently unlock the learnings hidden in this overwhelming data set to improve operations (e.g., efficiency, trouble avoidance/mitigation, cost savings) in a more timely manner.

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The new normal of lower oil prices has led to a simple conclusion—drilling costs must be lowered in order for many projects to be economic. It is not enough to slash big-ticket expenses like cement, mud, and casing, which often lead to potential problems, now and in the future. Operational expenses must be systematically lowered and become much more predictable and repeatable. This requires an approach not dissimilar from LEAN methodologies which are widely used in other industries. 

by Nathan Zenero, Michael Behounek

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Basic data quality issues such as this can be addressed by deploying quality management process at the source of data collection

By Matt Isbell, Hess Corp

Oil and gas drillers have long recognized the importance of measuring, observing and recording critical loads and pressures while drilling wells. Records of drilling measurements and other information collected at the rig preserve decisions and events impacting the safety, quality, delivery and cost of each well. Today, these measurements usually come from a digital data system. Unfortunately, the accuracy and trustworthiness of the data aren’t easy to verify promptly when a safety-critical decision must be made quickly. The Operators’ Group for Data Quality (OGDQ) is addressing this issue by providing data quality guidance, such as a contract addendum for rig contractors and other vendors.

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