Improve the drilling operations efficiency by the big data mining of real-time logging

Improve the drilling operations efficiency by the big data mining of real-time logging

Efficiency

Big data

Gas industry

Bayesian networks

Infill drilling

Data mining

Normal distribution

Data visualization

Today's data is tomorrow's oil and gas. Only the data can tell us right or wrong, but not the experience or feel. The real-time logging data conforms to the 6V features of the Big Data (Velocity, Variety, Volume, Veracity, Visualization and Validity). As a result, the drilling operations efficiency is significantly improved by the Big Data mining of real-time logging. The Big Data mining helps recognize the drilling operations automatically and identify the invisible nonproduction time (INPT). Firstly, the real-time logging data is acquired by the comprehensive logging unit. Secondly, the drilling operations are recognized by applying restrictions to drilling parameters. Thirdly, the daily time and the total time is breakdown based on the above logging data and operations categories. Finally, the INPT is identified by setting the target value based on the Big Data mining and learning curve. The savings potential, which is determined by the average and target, is critical to improve the operations efficiency. On one hand, the Normal Distribution is established by setting the specific operation time (such as slips connection time, etc.) to the X-axis and the operation count to the Y-axis. The average and covariance of the Normal Distribution is calculated. On the other hand, the target value is based on the Big Data mining by the Bayesian network model and the total-time learning curve of batch wells. As a result, the real-time drilling efficiency can identify the best-performing operations, crews and rigs. The crew-based operational performance comparisons are effective to identify the best-performing crews and to indicate where best to focus training and crew supervision efforts in the future. And the real-time drilling efficiency can measure the rig performance in order to make INPT visible. At last, the real-time drilling efficiency yields cost and time savings on both deep-water and complex wells. The method is successfully applied to BD gas field in Indonesia which is HTHP and LW oil field in South China Sea which is in deep-water. Application shows the INPT represents about 32%. The Big Data mining of real-time logging significantly improve the drilling operations efficiency, detect and minimize the INPT. As a result, the Big Data mining yields cost and time savings for tomorrow's market. Copyright 2018, SPE/IADC Middle East Drilling Technology Conference and Exhibition.

2018-January

Qishuai, Yin

Jin, Yang

Bo, Zhou

Menglei, Jiang

Xiaoliang, Chen

Chao, Fu

Li, Yan

Lei, Li

Yatao, Li

Zhengli, Liu

SPE/IADC Middle East Drilling Technology Conference and Exhibition 2018, January 29, 2018 - January 31, 2018

2018

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