11 خرداد 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 Comparing the performance of machine learning methods in estimating the shear wave transit time in one of the reservoirs in southwest of Iran
نوع پژوهش مقالات در نشریات
کلیدواژه‌ها
Shear wave transit time; Shear wave Velocity, Geomechanics, Machine Learning
مجله Scientific Reports
شناسه DOI 10.1038/s41598-024-55535-2
پژوهشگران محمدرسول دهقانی فیروزآبادی (نفر اول) ، شهریار جهانی (نفر دوم) ، علی رنجبر (نفر سوم)

چکیده

Shear wave transit time is a crucial parameter in petroleum engineering and geomechanical modeling with significant implications for reservoir performance and rock behavior prediction. Without accurate shear wave velocity information, geomechanical models are unable to fully characterize reservoir rock behavior, impacting operations such as hydraulic fracturing, production planning, and well stimulation. While traditional direct measurement methods are accurate but resource-intensive, indirect methods utilizing seismic and petrophysical data, as well as artificial intelligence algorithms, offer viable alternatives for shear wave velocity estimation. Machine learning algorithms have been proposed to predict shear wave velocity. However, until now, a comprehensive comparison has not been made on the common methods of machine learning that had an acceptable performance in previous researches. This research focuses on the prediction of shear wave transit time using prevalent machine learning techniques, along with a comparative analysis of these methods. To predict this parameter, various input features have been employed: compressional wave transit time, density, porosity, depth, Caliper log, and Gamma-ray log. Among the employed methods, the random forest approach demonstrated the most favorable performance, yielding R-squared and RMSE values of 0.9495 and 9.4567 respectively. Furthermore, the artificial neural network, LSBoost, Bayesian, multivariate regression, and support vector machine techniques achieved R-squared values of 0.878, 0.8583, 0.8471, 0.847 and 0.7975, RMSE values of 22.4068, 27.8158, 28.0138, 28.0240 and 37.5822 respectively. Estimation analysis confirmed the statistical reliability of the Random Forest model. The formulated strategies offer a promising framework applicable to shear wave velocity estimation in carbonate reservoirs.