Classification of reservoir rocks using deep learning
سال انتشار: 1401
نوع سند: مقاله کنفرانسی
زبان: انگلیسی
مشاهده: 136
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شناسه ملی سند علمی:
OGPC04_116
تاریخ نمایه سازی: 25 اردیبهشت 1402
چکیده مقاله:
Porosity, permeability, and hydrocarbon in place in reservoirs are among the critical parameters in petroleum engineering. To find them, it is necessary first to determine and diagnose lithology. Lithology can be determined by geological analysis of slabbed whole cores. This process is usually done by hand during macroscopic core studies, which is time-consuming and may be influenced by user bias. Thus, using efficient and automatic methods for lithology detection is of prime interest. This study uses machine learning methods to identify lithology and classify rocks from whole core images. For this purpose, we compared three widely used network architectures (i.e., Resnet-۵۰, Resnext-۵۰, and a convolutional neural network). The architectures are coded in the Pytorch library. ۳۰۰۰ meters of whole core images from ۲۸ wells of sandstone reservoirs are used as a dataset. This approach is automatic and free of user bias. Our result shows that Resnext-۵۰ could predict and classify the lithology of unseen whole core images with ۹۶.۷۸% accuracy.
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نویسندگان
Sayedeh Zahra Ghavami
Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University
Davood Khoozan
Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University
Saeid Sadeghnejad
Department of Petroleum Engineering, Faculty of Chemical Engineering, Tarbiat Modares University