Neural network‐based CO 2 interpretation from 4D Sleipner seismic images

Mapping fault planes using seismic images is a crucial and time-consuming step in hydrocarbon prospecting. Conventionally, this requires significant manual efforts that normally go through several iterations to optimize how the different fault planes connect with each other. Many techniques have been developed to automate this process, such as seismic coherence estimation, edge detection, and ant-tracking, to name a few. However, these techniques do not take advantage of the valuable experience accumulated by the interpreters. We have developed a method that uses the convolutional neural network (CNN) to automatically detect and map fault zones using 3D seismic images in a similar fashion to the way done by interpreters. This new technique is implemented in two steps: training and prediction. In the training step, a CNN model is trained with annotated seismic image cubes of field data, where every point in the seismic image is labeled as fault or nonfault. In the prediction step, the trained model is applied to compute fault probabilities at every location in other seismic image cubes. Unlike reported methods in the literature, our technique does not require precomputed attributes to predict the faults. We verified our approach on the synthetic and field data sets. We clearly determined that the CNN-computed fault probability outperformed that obtained using the coherence technique in terms of exhibiting clearer discontinuities. With the capability of emulating human experience and evolving through training using new field data sets, deep-learning tools manifest huge potential in automating and advancing seismic fault mapping.

Download Full-text

Automatic fault detection on seismic images using a multiscale attention convolutional neural network

Geophysics ◽

10.1190/geo2020-0945.1 ◽

2021 ◽

pp. 1-99

Author(s):

Kai Gao ◽

Lianjie Huang ◽

Yingcai Zheng ◽

Rongrong Lin ◽

Hao Hu ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Fault Detection ◽

Convolutional Neural Network ◽

Contextual Information ◽

Synthetic Data ◽

Feature Maps ◽

Automatic Methods ◽

Fully Automatic ◽

Seismic Images

High-fidelity fault detection on seismic images is one of the most important and challenging topics in the field of automatic seismic interpretation. Conventional hand-picking-based and semi-human-intervened fault detection approaches are being replaced by fully automatic methods thanks to the development of machine learning. We develop a novel multiscale attention convolutional neural network (MACNN for short) to improve machine-learning-based automatic end-to-end fault detection on seismic images. The most important characteristics of our MACNN fault detection method is that it employs a multiscale spatial-channel attention mechanism to merge and refine encoder feature maps of different spatial resolutions. The new architecture enables our MACNN to more effectively learn and exploit contextual information embedded in the encoder feature maps. We demonstrate through several synthetic data and field data examples that our MACNN tends to produce higher-resolution, higher-fidelity fault maps from complex seismic images compared with the conventional fault-detection convolutional neural network, thus leading to improved geological fidelity and interpretability of detected faults.

Download Full-text

Multitask learning for local seismic image processing: fault detection, structure-oriented smoothing with edge-preserving, and seismic normal estimation by using a single convolutional neural network

Geophysical Journal International ◽

10.1093/gji/ggz418 ◽

2019 ◽

Vol 219 (3) ◽

pp. 2097-2109 ◽

Cited By ~ 3

Author(s):

Xinming Wu ◽

Luming Liang ◽

Yunzhi Shi ◽

Zhicheng Geng ◽

Sergey Fomel

Keyword(s):

Neural Network ◽

Image Processing ◽

Fault Detection ◽

Convolutional Neural Network ◽

Basic Step ◽

Edge Preserving ◽

Structural Interpretation ◽

Seismic Image ◽

Normal Vectors ◽

Seismic Images

Summary Fault detection in a seismic image is a key step of structural interpretation. Structure-oriented smoothing with edge-preserving removes noise while enhancing seismic structures and sharpening structural edges in a seismic image, which, therefore, facilitates and accelerates the seismic structural interpretation. Estimating seismic normal vectors or reflection slopes is a basic step for many other seismic data processing tasks. All the three seismic image processing tasks are related to each other as they all involve the analysis of seismic structural features. In conventional seismic image processing schemes, however, these three tasks are often independently performed by different algorithms and challenges remain in each of them. We propose to simultaneously perform all the three tasks by using a single convolutional neural network (CNN). To train the network, we automatically create thousands of 3-D noisy synthetic seismic images and corresponding ground truth of fault images, clean seismic images and seismic normal vectors. Although trained with only the synthetic data sets, the network automatically learns to accurately perform all the three image processing tasks in a general seismic image. Multiple field examples show that the network is significantly superior to the conventional methods in all the three tasks of computing a more accurate and sharper fault detection, a smoothed seismic volume with better enhanced structures and structural edges, and more accurate seismic normal vectors or reflection slopes. Using a Titan Xp GPU, the training processing takes about 8 hr and the trained model takes only half a second to process a seismic volume with $128\, \times \, 128\, \times \, 128$ image samples.

Download Full-text

A neural network approach to seismic event identification using reference seismic images

Proceedings of IEEE International Conference on Systems, Man and Cybernetics ◽

10.1109/icsmc.1994.400175 ◽

2002 ◽

Author(s):

R.C. Hsu ◽

S.S. Alexander

Keyword(s):

Neural Network ◽

Seismic Event ◽

Network Approach ◽

Neural Network Approach ◽

Event Identification ◽

Seismic Images

Download Full-text

FaultSeg3D: Using synthetic data sets to train an end-to-end convolutional neural network for 3D seismic fault segmentation

Geophysics ◽

10.1190/geo2018-0646.1 ◽

2019 ◽

Vol 84 (3) ◽

pp. IM35-IM45 ◽

Cited By ~ 73

Author(s):

Xinming Wu ◽

Luming Liang ◽

Yunzhi Shi ◽

Sergey Fomel

Keyword(s):

Neural Network ◽

Fault Detection ◽

Convolutional Neural Network ◽

Synthetic Data ◽

Data Sets ◽

3D Seismic ◽

Fault Segmentation ◽

Seismic Image ◽

Conventional Methods ◽

Seismic Images

Delineating faults from seismic images is a key step for seismic structural interpretation, reservoir characterization, and well placement. In conventional methods, faults are considered as seismic reflection discontinuities and are detected by calculating attributes that estimate reflection continuities or discontinuities. We consider fault detection as a binary image segmentation problem of labeling a 3D seismic image with ones on faults and zeros elsewhere. We have performed an efficient image-to-image fault segmentation using a supervised fully convolutional neural network. To train the network, we automatically create 200 3D synthetic seismic images and corresponding binary fault labeling images, which are shown to be sufficient to train a good fault segmentation network. Because a binary fault image is highly imbalanced between zeros (nonfault) and ones (fault), we use a class-balanced binary cross-entropy loss function to adjust the imbalance so that the network is not trained or converged to predict only zeros. After training with only the synthetic data sets, the network automatically learns to calculate rich and proper features that are important for fault detection. Multiple field examples indicate that the neural network (trained by only synthetic data sets) can predict faults from 3D seismic images much more accurately and efficiently than conventional methods. With a TITAN Xp GPU, the training processing takes approximately 2 h and predicting faults in a [Formula: see text] seismic volume takes only milliseconds.

Download Full-text