چکیده مقاله
Automatic fault detection in seismic data is pivotal for advancing hydrocarbon exploration and geological risk assessment This study introduces an artificial intelligence AI based framework leveraging the Random Forest RF algorithm to achieve accurate fault detection in synthetic seismic data A dataset of 2000 samples, featuring coherence, dip angle, and curvature, was generated using a normal distribution and preprocessed through standardization, Z score based outlier removal, and SMOTE class balancing Optimized via GridSearchCV, the RF model attained an accuracy of 0 67, an area under the receiver operating characteristic ROC curve AUC of 0 74, and a recall of 0 73 for the fault class, demonstrating robust detection capability Visual analyses confirmed the effective separability of the selected features This framework outperforms traditional manual interpretation, offering transformative applications in hydrocarbon exploration, geological structure analysis, and seismic risk assessment Future work should validate this approach with real seismic data and incorporate advanced geophysical attributes to enhance generalizability Overall, this study highlights a pivotal step toward automating fault detection, substantially improving the efficiency and accuracy of geophysical exploration
کلیدواژهها
نویسندگان
Mahdi Chegini
Department of Petroleum Engineering, Ahvaz Faculty of Petroleum, Petroleum University of Technology PUT , Ahvaz, Iran
Jamshid Moghadasi
Department of Petroleum Engineering, Ahvaz Faculty of Petroleum, Petroleum University of Technology PUT , Ahvaz, Iran
Mohammad Jamialahmadi
Department of Petroleum Engineering, Ahvaz Faculty of Petroleum, Petroleum University of Technology PUT , Ahvaz, Iran
شیوه ارجاع
Chegini, Mahdi and Moghadasi, Jamshid and Jamialahmadi, Mohammad,1404,AI-based Fault Detection in Synthetic Seismic Data for Hydrocarbon Exploration,The 7th Applied Geophysics Conference in Oil Exploration,Tehran
ارائهشده در
مجموعه مقالات هفتمین کنفرانس ژئوفیزیک کاربردی در اکتشاف نفت23 مهر 1404 · تهران