Method for Estimating Permeability in carbonate reservoirs from Typical Logging Parameters using Functional Network

Obtaining permeability data has proved to be a difficult and expensive process. A permeability profile could determine which sections in a well are most profitable to stimulate and help characterize a reservoir very well. Utilizing well log data to find permeability could potentially reduce the need for laboratory testing which could also minimize the cost and lower the risk of the associated process. Permeability estimation in carbonate reservoirs is a challenging task to be handled accurately. Many researches tried to relate permeability and reservoir properties using complex mathematical equations which resulted in inaccurate estimation of the formation permeability values. The objective of this study is to develop a Functional Networks (FN) model that can be used to predict the permeability of heterogeneous reservoir based on three logs only, namely, resistivity, bulk density, and neutron porosity. In addition to the FN model, in this paper an empirical correlation of the FN model to predict permeability is also presented that can be used for carbonate rocks as far as given inputs lies within the range defined in the paper.