oa On Utilizing Functional Networks Computational Intelligence in Forecasting Rock<br>Mechanical Parameters for Hydrocarbon Reservoirs: Methodology and Comparative Studies

Rock mechanical parameters of reservoir rocks play an extremely important role in solving problems<br>related to almost all operations in oil or gas production. A continuous profile of these parameters along<br>with the depth is essential to analyze these problems which include wellbore stability, sand production,<br>fracturing, reservoir compaction, and surface subsidence. The mechanical parameters can be divided<br>into three main groups: viz., elastic parameters, strength parameters, and in-situ stresses. Even the<br>profile of in-situ stresses with depth is estimated using logs with elastic parameters as an essential<br>input. The focus of this article is on the prediction of elastic parameters along with the depth of a given<br>reservoir based on functional networks as a novel computational intelligence and data mining modeling<br>scheme.