## 1 Introduction
They obtained the optimum rotation angle by cross correlating the static and dynamic moduli of the reservoir rock with the corresponding calculated EEI from different rotation angles and generated 3D reflectivity EEI for every obtained optimum angle of rotation. The geomechanical parameter cubes are generated by performing model-based inversion applied to the 3D EEI reflectivity. [PERSON] et al. [10] claimed that their proposed inversion result was in comparable accuracy with those of simultaneous inversion, with validation from the blind-well test, and, therefore, it can bring significant progress in the future. To attain more accurate results, [PERSON] et al. [10] recommended conducting a feasibility step to take into account the uncertainty of the empirical angle of rotation and the use of other seismic analysis along with EEI analysis. [PERSON] [11] demonstrated the use of EEI to directly estimate the petrophysical properties (i.e., porosity, water saturation and shaliness) from seismic data of a clastic gas reservoir. They determined the optimum angle of rotation for each petrophysical parameter through a petrophysical analysis. The volume of intercept and gradient impedance are derived from seismic data volume using simultaneous inversion and projected to the optimum angle of rotation corresponding to the petrophysical parameter as predicted petrophysical properties. They showed that the inverted petrophysical values revealed a proper match at the blind well location. In conclusion, EEI inversion is effective for lithology and fluid prediction in clastic reservoirs. The result of inversion can be beneficial for static reservoir model building, volumetric calculation and determination of new potential drilling locations. For the successful application of EEI in petrophysical prediction, [PERSON] [11] reminded us that seismic processing should ensure that the reservoir amplitude versus angle (AVA) responses are preserved in the seismic data. It was also recommended to use a more sophisticated inversion strategy (based on tailored rock-physic models) to use the EEI for predicting petrophysical properties from the different geologic scenarios. This paper proposes an enhancement of EEI inversion in order to make it able to invert petrophysical properties from subtle seismic data. Stochastic and neural network techniques will be used to improve the vertical resolution, unity and absolute value of the EEI inversion result. The stochastic process will build EEI reflectivity models by perturbing the basic inverted EEI reflectivity with constraints from the basic inverted EEI signature and petrophysical log data. The constraints take the role of guiding the perturbation to produce EEI models that obey both the EEI signature and well petrophysical log data. The neural network aims to learn and memorize the relationship between the perturbed EEI models and the well petrophysical logs, which are used for predicting the petrophysical properties away from well locations. The proposed method is implemented to predict porosity, the volume of shale and water saturation from a gas field composed of thin sand-shale sequences in the Sarawak Basin, Malaysia.