Enhancing Channelised Features Interpretability Using Deep Learning Predictive Modelling
Authors: Mad Sahad Salbiah , Tan Nian Wei , Sajid Muhammad , Jones Ernest Austin 
Published: October 2022

Objectives/Scope

The growth of seismic attributes technology in accelerating the advancement of exploration and development started in late 1960s, with identification of bright spots through seismic reflection traits. In between the captured timeline, pattern recognition or neural network-based analysis on seismic attributes since 1990s gradually bring significant improvement in defining structural or depositional environment. Dunham (2020) addressed insufficient labelled geophysical data for supervised classification challenges lead to the possibility of overtraining for small-scale labelled datasets and unstable predictions for unlabeled datasets. Automated 3D interpretation using CNN produced less convincing accuracy on dataset with larger incised channel (Gao, 2021). We explore an approach to improve channelized features prediction accuracy through Deep Learning (DL) algorithms by refining the quality of the input dataset.

Methods, Procedures, Process

We developed supervised learning method using pixel-based segmentation and transformed matrix data into structural interpretation by constraining multi seismic attributes with ground truth dataset. It is divided into two (2) steps: 1a) Exploratory Data Analysis (EDA) where sixteen (16) seismic attributes representing categories defined by Roden (2015) were extracted and normalized for feature's correlation; 1b) We choose fluvial dominated field dataset located in Malay Basin and use Python-based Labelling Tool (PyLT) to annotate 64 time slices penetrating I-27 to I-35 Lower Reservoir of Mid Miocene. Data cube were 2 ms-sampled with size of 1024 × 256 (inlines, crosslines) annotated in z-direction. Associated fluvial facies elements were interpreted into five (5) classes; 2) Application of DL in data augmentation and conducted hyperparameter testing to find the network configuration for optimal performance.

Results, Observations, Conclusions

A range of learning rates have been tested, from 1e-4 to 5e-6 at increments of 5e-6. We have settled on a learning rate of 5e-5, which provided the fastest training time without adversely affecting the training process by introducing unwanted instabilities. Small weight decay multiplier of 1e-3 is used to introduce weight regularization to mitigate overfitting. We tested various batch sizes from 8 to 128, in increments of powers of two. From our observation, increasing batch size led to increases in training speed and improved training stability. We have applied various range of drop out to prevent overfitting by removing unnecessary features during training stage and stabilized predictions while being the regularizer. It was observed dropout slows down the learning rate but yielded performance improvements on test dataset when increased from 0 to 0.3.

Figure 1 (a) portrays two (2) inline sections representing spectral decomposition volumes with Log Pattern Analysis from nearby key wells where we can observe:

Figure 1 Integrated methodology applied in this study consists of four (4) main steps: 1. Exploratory Data Analysis (EDA) for input data; 2. Define the ground truth based on two (2) scenarios; 3. Training the dataset and monitor the model's performance; 4. Geobodies extraction to be integrated with other relevant dataset.

Figure 1 (b – input column), seismic attributes example (inset shown the Sweetness attributes) as part of the input data. We have chosen two (2) different time slices penetrating the studied channelized reservoir interval to identify different fluvial structures. Figure 1b (ground truth column) depicted dataset built using labelling tool. (Figure 1b – Prediction's column) shows It was observed that neural network is capable to capture distributary channels missed by attributes in binary classifications and Mud-Filled Channel (MFC) still can be detected through multi-facies classification even though facing small training dataset in Class III, as observed in deeper slice.

Novel/Additive Information

We have adopted DL method to alleviate detection of fluvial facies despite dealing with limited dataset. Diverse augmentation method was applied to increase the accuracy of model after we have satisfied with the refined annotated ground truth dataset. Random flipping and rotations from 0° to 45° were applied to all tiles and corresponding labels to prevent possibilities of data overfitting and helps to generalize potential aspects of features that DL algorithms have not seen before.