A novel method for seismic-attribute optimization driven by forward modeling and machine learning in prediction of fluvial reservoirs

Abstract

The use of seismic attributes for hydrocarbon exploration is well established, and the integration of multiple attributes by supervised machine learning is increasingly being applied as an effective method for attribute optimization. However, this method relies upon a dense array of wells to be employed as a training dataset, which limits its application to fields with sparse boreholes, especially offshore. To address this limitation, this study proposes a novel method of seismic attribute integration driven by forward seismic modeling, enabling the integration of multiple attributes by supervised learning in fields with a limited number of wells. This proposed method consists of two main tasks: (i) establishing a forward geological (lithological) model on a well-correlation section by forward seismic modeling, based on seismic data and wells from the study area; (ii) fusing multiple seismic attributes by supervised machine learning, employing the forward geological model and its synthetic seismic reflection as the training dataset. This method is applied and tested on a real-world case study from the East China Sea region. In this case study, seismic surface attributes of root-mean-square amplitude, max peak amplitude and sweetness are selected and then integrated using the proposed method. The integrated seismic attribute shows significant advantages for the detection of channel belts. Notably, it results in markedly improved correlation between seismic attribute and sand thickness, with the correlation coefficient increasing from 0.586 to 0.849, compared to the original seismic attribute.