05 اردیبهشت 1403
علي رنجبر

علی رنجبر

مرتبه علمی: استادیار
نشانی: دانشکده مهندسی نفت، گاز و پتروشیمی - گروه مهندسی نفت
تحصیلات: دکترای تخصصی / مهندسی نفت
تلفن: 077
دانشکده: دانشکده مهندسی نفت، گاز و پتروشیمی

مشخصات پژوهش

عنوان
In-situ stress estimation using machine learning algorithms and drilling data
نوع پژوهش مقالات در همایش ها
کلیدواژه‌ها
Stress, daily drilling data, geomechanical parameters, machine learning
پژوهشگران پریرخ ابراهیمی (نفر اول) ، امیرجواد برهانی (نفر دوم) ، علی رنجبر (نفر سوم) ، فاطمه محمدی نیا (نفر چهارم) ، آرش ابراهیمی (نفر پنجم)

چکیده

The instability of wells is one of the most important challenges in the drilling industry. Determining the stability of the well is highly dependent on the evaluation and calculation of the in_situ stress. For this purpose, in this study, after calculating the stress with the help of petrophysical data, stress estimation is done through two machine learning techniques. Because obtaining the amount of stress at different depths requires a lot of logs and also high costs, we can obtain the stress values at the desired depths from the drilling data always available in the fields and without the need for additional costs. Therefore, in this study, stress estimation is introduced with the help of machine learning and using daily drilling data as input parameters. The machine learning algorithms used in this article include random tree (RF) and support vector machine (SVM) and the drilling data includes mud input rate (Mwin), mud output rate (Mwout), flow rate (Q ), torque (T) and round per minute (RPM). According to the algorithms used, RF is more accurate than the SVM method for modeling.