SPE Online Education
Rate of Penetration Prediction for Multiple Well Profiles Using Artificial Intelligence Techniques
Recorded On: 10/01/2020
Rate of Penetration (ROP) is defined as the volume of rock removed per unit area (ft) per unit time (hr). This important measurement factor is often captured by many drilling companies to gauge the speed and the efficiency of the time spent to drill a section. It has been reported in the industry that high percentage of the well budget is spent on the drilling phase, thus many drilling operators pay close attention to this factor and try to optimize it as much as possible. However, it is very challenging to capture the effect of each individual parameter since most of them are interconnected, and changing one parameter affects the other. As a result, many companies maintain data for the drilling performance per field and set certain benchmarks to gauge the speed of any newly drilled well. To date, no solid or reliable model exists because of the complexity of the drilling process, therefore, the utilization of artificial intelligence (AI) in drilling applications is a game changer since most of the unknown parameters are accounted for during the modeling or training process. The results presented in this webinar demonstrates the use of artificial intelligence techniques to develop rate of penetration models for multiple well profiles from actual field data. This includes model development, training and testing, and validating. It also lists some key challenges during AI modeling such as data quality, data analytics and leaving the model as a blackbox.
This webinar is categorized under the Drilling technical discipline.
All content contained within this webinar is copyrighted by Dr. Ahmad Al-AbdulJabbar and its use and/or reproduction outside the portal requires express permission from Dr. Ahmad Al-AbdulJabbar.
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Dr. Ahmad Al-AbdulJabbar
Drilling Engineering Supervisor, Saudi Aramco
Dr. Al-AbdulJabbar is a drilling engineering supervisor working with Saudi Aramco for more than 10 years. His work experience includes offshore and onshore oil & gas, workover and unconventional resources. He earned his Ph.D. and MS in Petroleum Engineering from King Fahd University of Petroleum & Minerals. His area of research and publication domain includes drilling optimization and artificial intelligence applications for drilling engineering. Ahmad has more than 10 publications including conference papers and journals, and holds 5 patents. He is an SPE Certified Petroleum Engineer and SPE Century Club member.
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