2D Elastic Frequency-Domain Waveform Inversion, written in the Python computing language
The world’s remaining oil and gas reservoirs demand increasingly specialized methods. My research uses an advanced physical method known as ‘waveform inversion,’ a comprehensive approach that aims to use all of the information from a given seismic dataset to reconstruct a high-resolution subsurface image. I am working with real land seismic data from Ohio, courtesy of Arcis Seismic Solutions. The reservoir in this area is Utica Shale. Although waveform inversion has been applied in the seismic processing industry, it has been limited to acoustic propagation models. It has thus been limited to only producing primary-wave (P-wave) images. In my Master of Science thesis, I am extending waveform inversion to include the elastic case, which has the potential to produce shear-wave (S-wave) images, yielding more information about the reservoir. High-quality S-wave images will be of great interest to companies like Arcis, because these images provide information on reservoir properties controlling production. In order to incorporate the elastic case, I will develop code, 'Elastico.' I propose to apply elastic waveform inversion to synthetic data first, and then the data from Ohio. The final goals include high-resolution near-surface velocity models, useable for exploration and development of oil and gas targets.