Quantitative parameter modeling
The Techlog K.mod module extracts essential information from log data to
- predict nonrecorded parameters (Ф, K)
- reconstruct missing or poor quality measurements to compensate for bad hole conditions, environmental effects, acquisition problems, etc.
- control scale shift management from core to reservoir scale
- compare well log and core data to reduce the need for coring and plug analysis of subsequent appraisal wells.
Supervised neural networks
Parameters can be reconstructed or modeled directly from log data via an interactive learning process. The original variability in the data can be retained using the powerful, nonlinear Multilayer Perceptron modeling tool.
Fully quantified uncertainties
Full control of input parameters is retained, while clear feedback on log and model quality is provided; the K.mod module is not a “black box” tool. Uncertainties can be managed on input (back propagation method to check the contribution of each input) and on output (self-organized map categorizes training and validation data for their effectiveness in modeling the target data). Inputs can also be weighted, which allows the forcing extreme values. If required, output and learning data distributions can be standardized to match dynamic ranges.
Interactive, intuitive, and fast
The K.mod module is based on complex technology, but it remains easy to use. It is a straightforward, efficient tool that provides simple interpretation and accurate reservoir characterization.