Elastic Properties Estimation From Prestack Seismic Data Using GGCNNs and Application on Tight Sandstone Reservoir Characterization

Traditional optimization algorithms are usually applied to estimate the elastic parameters of the subsurface by using field seismic data. However, these optimization algorithms highly depend on prior knowledge (e.g., the initial model setup and sparsity), leading to serious inversion uncertainties. Nowadays, with the rapid development of neural networks, convolutional neural networks (CNNs) have been widely imposed on estimating elastic parameters from field data. However, the deficiency of labeled seismic data impedes the CNNs application in seismic inversion. Moreover, both the size and diversity of labeled datasets are also critical factors influencing the accuracy and resolution of predicted parameters when using the CNNs-based inversion techniques. In this work, taking the unconventional tight sandstone formation as an example, we develop a geological and geophysical model driven CNNs (GGCNNs), named as GGCNNs. The proposed GGCNNs allow us to take advantage of both the prior geological information and basic geophysical model from the generated synthetic labeled prestack seismic datasets, representing essential characteristics of the subsurface. Moreover, under the consideration of data diversity, the GGCNNs model enables us to make a tradeoff between the inversion accuracy and labeled data size. Applications on both synthetic and field data clearly demonstrate the effectiveness of the proposed GGCNNs model for predicting elastic parameters by using prestack seismic data, i.e., its predicted results are with high accuracy in the vertical profile and continuity and smooth in the horizon slice.

Automated summary

Traditional optimization algorithms used for estimating elastic parameters from field seismic data rely heavily on prior knowledge, leading to uncertainties in inversion results. With the development of neural networks, convolutional neural networks (CNNs) have been applied to this task, but the lack of labeled seismic data and diversity in datasets can hinder their effectiveness. The proposed Geological and Geophysical Model Driven CNNs (GGCNNs) model in this study leverages synthetic labeled prestack seismic datasets to incorporate geological information and achieve a balance between inversion accuracy and labeled data size. Applications demonstrate the effectiveness of GGCNNs in predicting elastic parameters with high accuracy and smoothness.