Driving Rig Performance through Real-Time Data Analysis, Benchmarking, Dashboards and Developed Key Performance Indicators

Using the right offset well data to plan the next project is critical, but performance expectations get diluted when metrics are averaged over large depth ranges. Focusing on the rate of penetration (ROP), a benchmarking process was developed to improve drilling efficiencies. The whole process was automated and included a real-time analysis of the well being drilled that was capable of quickly identifying the 1) offset wells based on location, lithology, well type, and performance; 2) sectors within those wells with common formation, wellbore, and tubular characteristics; and 3) the effective technical limit (ETL)—the best drilling performance that realistically can be repeated. The process mentioned in this paper identifies opportunities for operators to reduce inefficiencies while drilling, allowing users to take action while there is still opportunity to mitigate or eliminate the damage. Using the guided workflow and real-time data to identify and select the appropriate offset wells, multiple ETL sectors are created based on the characteristics they share (formation, wellbore, tubular). During real-time operations, when the same set of characteristics for a particular sector is met, ETL data for that sector are displayed, and users can evaluate performance in real time compared to best historical performance. This paper also presents a case study in which this process was applied to improve an operator's drilling efficiency.

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Future Workforce Education through Big Data Analysis for Drilling Optimization

Zhou

Baumgartner

Saini

et al. 2017

SPE/IADC Drilling Conference and Exhibition

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On their continuous quest to improve drilling efficiency, operators are reaching more and more towards the sensor and data-streaming technologies and their powerful data analytics capabilities. For this project, an operator partnered with the drilling automation research group at the University of Texas at Austin to develop a workflow for big data analysis and visualization. The objectives were to maximize the value derived from data, establish an analysis toolkit, and train students on data analytics—a necessary job function of any future drilling engineer. The operator provided data sets, business and technical objectives, and guidance for the project, while a multi-disciplinary group of undergraduate and graduate students piloted an analysis workflow. The students developed methods to: 1) understand and clean the data; 2) structure, combine, and condense information; 3) visualize, benchmark, and interpret the data, as well as derive key performance indicators (KPIs); and 4) automate these processes. The operator provided data collected from drilling 16 wells in an US unconventional play. The large data sets comprised of un-organized time and depth based information from surface and downhole sensors, daily drilling reports, geological information, etc. Students were trained on specialized software and subsequently curated data into smaller sizes and standard formats. Students investigated bottom hole assembly (BHA) and directional drilling performance using a combination of auto-generated conventional visuals (e.g., BHA designs, annotated time vs depth curves) and newly developed tools (e.g., tortuosity, 3D well trajectory plots combined with operational data). Methods for ‘push a button' investigations of mechanic specific energy (MSE), vibration, torque and drag were also developed by calculating specific KPIs from the raw measurement data. The analysis work itself coupled with the attempt to improve the workflow processes served as a meaningful and highly effective way to educate students and prepare them to be the "drilling engineers of the future" with proficiency in data analytics.

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Driving Rig Performance through Real-Time Data Analysis, Benchmarking, Dashboards and Developed Key Performance Indicators

Cited by 6 publications

References 1 publication

Research on Quantitative Method and Main Control Factors of Drilling Crew Operation Effect

Research on Quantitative Method and Main Control Factors of Drilling Crew Operation Effect

Employing Process, Benchmark Automation, and Real-Time Analysis to Improve Drilling Efficiency

Future Workforce Education through Big Data Analysis for Drilling Optimization

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