چکیده مقاله
Accurate prediction of subsurface acoustic impedance is a cornerstone of seismic reservoir characterization Traditional model based inversion techniques often fail under sparse well control and geologically heterogeneous conditions This study introduces a hybrid workflow that integrates physics based rock physics modeling with two machine learning approaches, namely multi layer feedforward networks MLFN and radial basis function networks RBFN , to enhance acoustic impedance inversion in a data constrained clastic reservoir in the Persian Gulf Rock physics modeling was applied to generate synthetic elastic logs, including compressional velocity, shear velocity, density, and pseudo wells to enrich the training dataset and ensure geological plausibility Seismic attributes representing amplitude, phase, and frequency characteristics were extracted and used as inputs for MLFN and RBFN architectures A leave one well out cross validation strategy was employed to validate the models Results show that both methods successfully reproduced acoustic impedance, with the MLFN achieving higher predictive accuracy, reflected in a cross correlation of 87% At the same time, the RBFN offered faster training and robust performance in capturing localized nonlinearities The comparative analysis highlights the respective strengths and weaknesses of both methods and emphasizes their complementary value for practical reservoir characterization This workflow illustrates that physics guided machine learning provides a reliable solution for generating impedance volumes while reducing reliance on dense well control, offering a promising approach for offshore and frontier exploration settings
کلیدواژهها
نویسندگان
Arash Ghiasvand
Department of Petroleum and Geoenergy Engineering, Amirkabir University of Technology, Tehran, Iran
Abdolrahim Javaherian
Department of Petroleum and Geoenergy Engineering, Amirkabir University of Technology, Tehran, Iran
Maryam Amirmazlaghani
Department of Computer Engineering, Amirkabir University of Technology, Tehran, Iran
Mohammad Reza Saberi
GeoSoftware, ۲۵۹۱ XR, The Hague, The Netherlands
Hadi Mahdavi Basir
Department of Petroleum and Geoenergy Engineering, Amirkabir University of Technology, Tehran, Iran
Benyamin Khadem
Department of Energy Resources, University of Stavanger, ۴۰۳۶, Stavanger, Norway
شیوه ارجاع
Ghiasvand, Arash and Javaherian, Abdolrahim and Amirmazlaghani, Maryam and Saberi, Mohammad Reza and Basir, Hadi Mahdavi and Khadem, Benyamin,1404,Physics-guided machine learning for acoustic impedance inversion: a comparative study of MLFN and RBFN in data-constrained in an oilfield using rock physics-based pseudo-wells,The 7th Applied Geophysics Conference in Oil Exploration,Tehran
ارائهشده در
مجموعه مقالات هفتمین کنفرانس ژئوفیزیک کاربردی در اکتشاف نفت23 مهر 1404 · تهران