Sequence Modeling for Reservoir Characterization   Reservoir characterization involves the estimation petrophysical properties from well logs, core data and seismic data. Estimating such properties is a challenging task due to the non-linearity and heterogeneity of the subsurface. Recent advances in machine learning have shown promising results for recurrent neural networks (RNN) in modeling complex sequential data such as videos and speech signals.  In this research, we model seismic traces as sequential data and applying state-of-the-art sequence modeling techniques such RNNs and LSTMs for reservoir characterization including, but not limited no, property prediction and facies analysis.

Sequence Modeling for Reservoir Characterization

Reservoir characterization involves the estimation petrophysical properties from well logs, core data and seismic data. Estimating such properties is a challenging task due to the non-linearity and heterogeneity of the subsurface. Recent advances in machine learning have shown promising results for recurrent neural networks (RNN) in modeling complex sequential data such as videos and speech signals.

In this research, we model seismic traces as sequential data and applying state-of-the-art sequence modeling techniques such RNNs and LSTMs for reservoir characterization including, but not limited no, property prediction and facies analysis.

Texture Image Processing   Texture representation plays a very important role in many applications of the field of visual understanding. Examples of these applications are segmentation, retrieval, and material recognition.   In our work, develop and design discriminative, robust, and efficient texture features that can be used to build interpretable texture models. In addition, we design similarity measures designed specifically to capture textural content in an image as opposed to generic distance (or similarity) measures. 

Texture Image Processing

Texture representation plays a very important role in many applications of the field of visual understanding. Examples of these applications are segmentation, retrieval, and material recognition. 

In our work, develop and design discriminative, robust, and efficient texture features that can be used to build interpretable texture models. In addition, we design similarity measures designed specifically to capture textural content in an image as opposed to generic distance (or similarity) measures. 

Machine Learning for Seismic Interpretation   Seismic interpretation, the process of identifying the different subsurface geophysical structures. The process is very time consuming and labor intensive.  In our work, we aim to use the fast growing field of machine learning to reduce the time and effort spend on seismic interpretation. We develop automatic seismic interpretation system. In addition, we create large-scale labeled datasets suitable for training machine learning models.  

Machine Learning for Seismic Interpretation

Seismic interpretation, the process of identifying the different subsurface geophysical structures. The process is very time consuming and labor intensive.

In our work, we aim to use the fast growing field of machine learning to reduce the time and effort spend on seismic interpretation. We develop automatic seismic interpretation system. In addition, we create large-scale labeled datasets suitable for training machine learning models.  

Seismic Attribute Enhancement    Seismic attributes play a pivotal role in the field of seismic interpretation. Some of these attributes are derived from the seismic signals (traces) directly which makes them vulnerable to noise.   In our work, we utilize multiscale analysis and other as well as other image processing representations to enhance the quality and resolution of these attributes without compromising small-scale features that are of interest in seismic images. 

Seismic Attribute Enhancement

Seismic attributes play a pivotal role in the field of seismic interpretation. Some of these attributes are derived from the seismic signals (traces) directly which makes them vulnerable to noise. 

In our work, we utilize multiscale analysis and other as well as other image processing representations to enhance the quality and resolution of these attributes without compromising small-scale features that are of interest in seismic images.