Deep Learning Based Seismic Elastic Properties Inversion Guided by Rock Physics

Abstract

Submitted Abstract The aim in characterizing reservoir on seismic is to be able to precisely assess rock and fluid properties from seismic data. Despite several approaches available, geoscientists still facing issue to accurately characterize the elastic & reservoir properties from seismic. This mainly because of the implementation of simplified linearized algorithms and assumptions which unable to fully address problem with non-linearity and non-uniqueness solution. Besides, the conventional approaches also typically take a very long turnaround time due to extensive computational works. Hence, implementation of deep learning-based seismic inversion with incorporation of a generalized rock physics knowledge for elastic properties prediction perhaps might be able to address the issues. The proposed approach consists of 4 main phases. The first phase is to develop rock physics understanding based on in-situ available reference well data. In the next phase, realistic synthetic dataset library at various pseudo well location is generated that mimic the actual subsurface reservoir condition and the corresponding seismic data from those pseudos well library is subsequently simulated. The third phase is to implement U-net deep learning network architecture with residual blocks of ResNet-18 for training, validation, and testing, utilizing synthetic data library generated in the previous phase. The fourth phase is to apply the pre-trained predictive network model on the actual seismic dataset as input for rock properties estimation. The robustness of the proposed approach is first evaluated based on synthetic dataset. Exceptionally good coefficient cross-correlation is obtained after evaluation on the predicted and true elastic properties; while results on the blind test dataset are slightly less compared to the training performance. This shows that the implemented model architecture with specially designed residual blocks can generalize quite well. The network model able to qualitatively distinguish the properties variation and capture the background trend of each elastic property. Besides, the proof-of-concept exercise also is successfully verified on a fluvial dominated field in Malay basin. Qualitatively, the predictive network model able to capture the background trend with some minor overfitting and identify the relative deviation of the elastic properties. The promising results are also comparable with results produced from commercial software, yet with faster turn-around time and more efficient workflow. In this work, a new approach of deep learning-based seismic inversion is introduced for elastic properties prediction which incorporates a generalized rock physics knowledge and a complex deep learning networks architecture of U-Net with residual block ResNet-18.

Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network

“…Applying deep learning to field data is a very challenging task due to two main reasons, which are (1) the residual noise presence in the seismic data and (2) the unavailability of the true label data. To test the robustness of our approach to field data, we used seismic data from a Malaysian field with the labels generated by A. Fuad, M.I., et al [42] via rock-physics-guided, deep-learning-based properties inversion (Figure 17). The same workflow was applied to the field data, such as normalization and transformation to 256 × 256 patches.…”