Operators' Group, Rig Contractors, and OEM/Service Company Work to Solve Rig Data Quality Issues
- M. Behounek (Apache Corp) | D. Nguyen (ConocoPhillips) | S. Halloran (Ensign Energy Services) | M. Isbell (Hess Corp) | C. Mandava (Nabors) | N. Vinay (Nabors) | J. McMullen (Noble Corporation) | C. Hoefling (NOV)
- Document ID
- Society of Petroleum Engineers
- IADC/SPE Drilling Conference and Exhibition, 6-8 March, Fort Worth, Texas, USA
- Publication Date
- Document Type
- Conference Paper
- 2018. IADC/SPE Drilling Conference and Exhibition
- 1.10 Drilling Equipment, 1.6 Drilling Operations, 1.12.6 Drilling Data Management and Standards, 1.12 Drilling Measurement, Data Acquisition and Automation, 1.11 Drilling Fluids and Materials
- Data Sampling, Contract, Rig Sensors, Data Transmission, Data Quality
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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.
For this paper, the OGDQ worked with Rig Contractors and an OEM/Service Company to advance recommended data quality components in work processes and commercial agreements. By bringing transparency to the process, the authors hope to contribute to the efforts to address operational data quality issues and to drive alignment and improvements among Operators, Rig Contractors, OEMs, and Service Companies. This paper outlines an approach to putting data quality into practice, including initially identifying the problem, field verification, developing key measurement specifications, constructing framework components, and anticipating management of change issues.
Quality drilling data is essential to both rig and office personnel who are tasked with decision making for fast-paced well programs. Quality drilling data is also essential for the data-driven systems developed to assist in managing well delivery. Rig studies show several cases where Operators independently uncovered systematic errors for 10 key measurements used for drilling process decision making (Zenero 2014; Zenero et al. 2016). The 10 key measurements are listed as follows:
Rotary/Top Drive Torque
Joint Makeup/Breakout Torque
Rotary/Top Drive Rotational Speed
Stand Pipe Pressure
Drilling Fluid Pump Rate
Drilling Fluid Tank/Pit Volume
Drilling Fluid Density
Drilling Fluid Viscosity
Widespread agreement on data quality practices among Operators, Rig Contractors, OEMs, and Service Companies is crucial for their quick adoption, and an industry-wide approach has a profound effect on drilling operations. Widely adopted practices will support and drive requirements for sensor quality, calibration, field verification, and maintenance. This standardization will, in turn, significantly enable improved drilling operations, drilling analysis, and big data processing by correcting many errors resulting from poor data quality. This paper outlines the methodology used to develop a guide for commercial drilling components, and illustrates the application of this guide with selected drilling data use cases.
|File Size||1 MB||Number of Pages||21|
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