Gas-hydrates saturation estimation in Krishna-Godavari basin, India

Gas hydrates are an unconventional energy resource. They may become an important source of energy for India in the future. They occur offshore along the continental margin. They are currently in exploratory and evaluation stages and their quantification is an important task. The goal of this thesis is to demonstrate a new technique for the estimation of gas hydrates volumes. The region of study is the Krishna-Godavari basin. It is located on the eastern offshore areas of India. The presence of gas hydrates has been proven by drilling into marine sediments as a part of the Indian National Gas Hydrates Program. Borehole subsurface and surface seismic data were collected during this expedition. I use a 2D seismic reflection line and borehole log data for my study. The method I use for estimation of gas hydrates saturation uses a combination of inversion of seismic reflection data and development of seismic attributes. My approach can be broadly described by following steps:

1. Process the seismic data to remove noise. Use stacked and migrated data along with well logs to perform poststack seismic inversion to obtain impedance information in volumetric portions of the subsurface.
2. Use NMO corrected CDP gather records of the seismic reflection data along with subsurface well logs to perform prestack seismic inversion to obtain impedance volumes.
3. Compare the results from step1 and step 2 and use the best results to perform multi-attribute analysis using a neural network method to predict resistivity and porosity logs at the well location. Use the transform equations obtained at the well location to predict the well logs throughout the seismic section in the desired zone of interest.
4. Use an anisotropic equivalent of Archie’s law that relates resistivity and porosity to saturation to predict saturation throughout the seismic reflection section. The majority of the previous work done in the region is limited to gas hydrates quantification only at the well location. By using neural networks for multi-attribute analysis, I have demonstrated a statistical based method for the prediction of log properties away from well location. My results suggest gas hydrates saturation in the range of 50-80% in the zone of interest. The estimated saturation of gas hydrates matches up very closely with the saturation estimates obtained from the cores recovered during coring of the boreholes. Hence my method provides a reliable method of quantification of gas hydrates by making best possible use of seismic and well log data. The unique combination of impedance derived attributes and neural-network includes the non-linear behavior in the predictive transform relationships. The use of an anisotropic formulation of Archie’s law to estimate saturation also produces accurate results confirmed with the observed gas-hydrates saturation.