Project Title: An Intelligent Systems Approach to Reservoir Characterization.

One of the realistic ways of ensuring an abundant, economical supply of natural gas with minimal environmental impact would be to increase the recovery factor of the gas reservoirs. This objective can be achieved by:
A) increasing the productivity of the existing gas wells (identifying the bypassed gas pockets to produce - completing productive horizons that were previously thought to be non productive) and
B) reducing the cost associated with field development (efficiently and accurately identifying infill drilling prospects).

The objective of this project is to build a scale-down correlation between seismic data and wireline logs. The implementation of intelligent systems to bridge the gap between low-resolution seismic inversions and higher-resolution well log data will be tested using data collected under a previous DOE contract.

We propose a three-step approach to achieve the scale down process:

  1. Use VSP or other higher resolution seismic data (vibroseis and weight-drop) to increase resolution of the conventional seismic data.
  2. Construct, train, and calibrate and verify an intelligent system to simulate the well- log reflectivity response from traces in the vicinity of the well.
  3. Inversion of seismic data to logs using the intelligent system developed in step 2 outside the training area.