ACE stimulated neural network for shear wave velocity determination from well logs

Shear wave velocity provides invaluable information for geomechanical, geophysical, and reservoir characterization studies. However, measurement of shear wave velocity is time, cost and labor intensive. This study proposes a swift and exact methodology, called ACE stimulated neural network, for prediction of shear wave velocity from available well logs such that it will be able to surpass previous models. The proposed method is composed of two major parts: 1) transforming input/output data space to a higher correlated space using alternative condition expectation (ACE), and 2) making a neural network formulation in transformed data space. Transforming in the first step makes it easier for neural network to find the complicated underlying dependency of input/output data. Therefore, neural network will be able to develop an accurate and strong formulation between conventional well logs and shear wave velocity. The Propounded approach was successfully applied in one of the carbonate gas fields of Iran. A comparison between proposed model and previous models showed superiority of ACE stimulated neural network.