| Abstract |
Seismic facies interpretation plays a vital role in oil and gas exploration and production. However, traditional methods, such as trace inversion and manual interpretation, are often time-consuming and labor-intensive. In recent years, deep learning algorithms have emerged as promising and efficient tools for facies identification with 3D seismic data. As a rapidly developing field, deep learning models with various network structures rise up all the time. Some of them are employed by researchers in the case studies of facies interpretation and are claimed as the better methods. However, the influence of the input features especially their inherent data structure, has attracted few discussions so far. Furthermore, most current studies using artificial intelligence for seismic interpretation primarily rely on two major branches of deep learning algorithms: convolutional neural networks (CNNs) which are skilled in capturing spatial patterns, and recurrent neural networks (RNNs) which are effective at modeling temporal dependencies. As a result, these networks and their variants fail to simultaneously leverage both spatial and temporal coupling of the multidimensional data. In this paper, we replace the matrix multiplications inside the memory cell of the general long short-term memory unit with a convolution operation, which is a basic module of the deep learning framework, to attach the capability of capturing the spatial dependencies with temporal dynamic behavior to the recurrent architecture. A patched deep learning model based on this theoretically rational and programming feasible RNN variant is implemented in three experiments of the 3D seismic facies interpretation. Our study firstly highlights the importance of the input seismic attributes in providing valuable information for making accurate predictions. The results from the first experiment demonstrate that the selection of seismic attributes based on their correlation with the interpretation target greatly enhances the model performance. Furthermore, by comparing the predictions from the proposed model with the ones from the model that just utilizes the spatial dependencies, our study emphasizes the significance of incorporating spatio-temporal dependencies within the chosen seismic attributes during the interpretation, as it leads to improved predictions, especially in boundary detection. Last but not least, our experiments demonstrate that the contribution of spatial dependencies to 3D seismic interpretation diminishes as the spatial distance increases. Therefore, selectively augmenting the training data with samples that have weaker spatial correlations can significantly enhance the model’s performance. Based on our findings, we prefer to conclude that interpreters that consider spatio-temporal dependencies inside the full covering optimized attributes can improve the quality of 3D seismic facies interpretation. This conclusion can serve as an outline for the workflow of Deep Learning-assisted 3D seismic interpretation. |
, Sumit Verma 
