Companies can enter the proposed program through two forms of membership: Gold sponsors are companies that support focused research projects of defined scope and provide funding in significant excess of the sponsor fee mentioned below.
Benefits of gold membership include:
- Active participation in focused collaborative research projects.
- Timely deliverables in the form of progress reports, software code, and reproducible computational experiments.
- All benefits of the regular sponsorship described below.
Regular sponsors are companies that support the program by a sponsor fee of $50,000 per year.
Benefits of regular membership include:
- Invitation to sponsor meetings, where research results are delivered in the form of oral presentations and where company representatives get the chance to interact with students and research personnel.
- Access to publication preprints, complete with open-source software code and reproducible computational experiments.
Prospective sponsors should contact firstname.lastname@example.org
Current Funded Projects
Using deep learning to accelerate time-lapse seismic data inversion workflow for reservoir parameter estimation in carbon dioxide and sequestration studies
Machine Learning for passive signal de-noising and arrival picking
Pore pressure monitoring of a hydraulic fracture using reflection seismic data
Seismic characterization and imaging of mobile shales
Deep Learning based workflow for simultaneously interpreting 3-D seismic horizons and faults
Deep learning for velocity model building with common image gather volumes
Elastic Multi-Parameter Waveform Inversion for Subsalt Imaging
Path-Integral Seismic Diffraction Imaging of Fractured Shale Reservoirs
Characterization of Fractured Shale Reservoirs Using Anelliptic Parameters
Lowrank Reverse Time Migration for Subsalt Imaging
Phase Correction of Prestack Seismic Data Using Local Attributes
We develop a novel method for correcting prestack migration gathers for variable phase rotations. Our approach uses specially constructed local attributes, such as local kurtosis and local skewness.
High-Resolution Imaging of the Barrolka Dataset Using Diffraction Attributes
We develop a novel method for high-resolution seismic reservoir characterization and to apply it to a dataset acquired by Santos in Barrolka gas field in Australia. Our approach uses diffraction imaging attributes.
Seismic Wave Focusing for Subsurface Imaging and Enhanced Oil Recovery
Our research focuses on the concept of inversion for seismic sources. Two problems sharing similar characteristics are targeted, one with direct application to imaging and a second one with direct application to enhanced oil recovery (EOR). In the imaging application, the sources could be the very scattering caused by the changes in the velocity with position, which, in seismic exploration terms, is represented by a seismic image. The seismic sources we want to invert for could also very well be the actual sources that are used to generate the initial wavefield. In both cases, wave propagation is guided by the wave equation, and in both cases the optimization procedure is guided by a fitting or shaping procedure. To implement an accurate inversion for the actual wave generating sources, we must first solve the problem for the secondary (reflection) type of sources. Thus, a reliable seismic wave focusing approach is needed for subsurface imaging. In the EOR application, wave generating sources are sought that would maximize a prescribed outcome at the reservoir. The prescribed outcome, which is user-defined and reservoir-specific, aims at increasing the mobility of otherwise entrapped oil, and thus increase production rates or revitalize thought-as-depleted reservoirs. Guided by the maximization goal (e.g. kinetic energy or fluid accelerations), an optimization problem must be solved to arrive at the optimal wave source signal and location to attain the sought outcome. At the heart of these problems is the physics of wave propagation through highly heterogeneous elastic or poro-elastic media, which through various formulations can be geared to serve the prescribed objectives in an optimal way.
Extracting Seismic Events by Predictive Painting and Time-Warping
We are developing a new technology for extracting the geometry of seismic events in migrated images and gathers. The extracted information is useful both for structural seismic interpretation and for seismic velocity estimation.
Our method is based on the technique of predictive painting (Fomel, 2008; 2010) combined with time-warping (Burnett and Fomel, 2009).
Waveform Tomography with Cost-function in the Image Domain
High-resolution Seismic Attributes for Fracture Characterization in Grosmont Formation
We are developing novel technology for high-resolution seismic reservoir characterization with application to high-resolution seismic data acquired by Shell in the heavy-oil Grosmont formation. Our approach uses novel scattering attributes defined by diffraction imaging in the dip-angle domain. Our research team includes geophysicists, structural geologists, and applied mathematicians, with a strong history of interdisciplinary research collaboration.
Multiazimuth Seismic Diffraction Imaging for Fracture Characterization in Low-Permeability Gas Formations
The goal of this project is to create a new technology for fracture detection using seismic diffraction imaging and to test it against realistic fracture patterns. We also aim to perfect sidewall-core and wireline-based methods and to use them for verifying the new seismic method.
Rapid Travel Time Solutions for Near Surface Velocity Estimation
Fast beam migration
We are developing a new technology for wide-azimuth Fast Beam Migration (FBM), a super-efficient seismic imaging algorithm. The faster imaging step allows for more iterations of velocity model building, which enable the processing team to enhance the seismic resolution and imaging of complex geologic structures, and allows for deeper data penetration, steeper dip and sub-salt structure imaging. This advanced imaging methodology will improve success rate and cost effectiveness for new deep-field discoveries, greatly reduce the turnaround time for large surveys, and also have applications in increasing recovery efficiency for the development of existing fields.
Enhanced Seismic Imaging of Land Data
We are developing a new technology for seismic imaging of land data. Two major improvements come from enhancing residual static correction through the use of the predictive painting algorithm and enhancing time-to-depth conversion and interval velocity estimation through the use of the time-to-depth conversion algorithm that takes into account lateral velocity variations.
Subsalt Seismic Imaging Using Levelset Methods
We develop new numerical algorithms applicable for seismic imaging in complex subsalt environments, such as the Gulf of Mexico. The first algorithm is fast multiple-arrival traveltime computation in the phase-space domain extended to wave propagation and imaging with the oriented wave equation. The second algorithm is a semi-automatic extraction of salt bodies from seismic images using levelset surface representations.