Abstarct: In this thesis, a new multiple-point simulation (MPS) algorithm is proposed baxsed on a hybrid of the discrete wavelet transform (DWT) and data fusion called CCWSIM, which is an extended version of the CCSIM algorithm. Indeed, what distinguishes the proposed algorithm from the CCSIM algorithm is to compute the cross-correlation function (CCF) in the wavelet feature space. In other words, in the proposed algorithm the CCF is computed using the approximation coefficients of the discrete wavelet transform. The approximation coefficients of the discrete wavelet transform not only extract the most important variability of a pattern but also at each decomposition level have a dimensional reduction equal to 75 percent of the original pattern, which can significantly increase computational efficiency. Also, in order to enhance hard data conditioning in this thesis, an approach baxsed on the data fusion called ordered weighted averaging (OWA) is proposed. In this method, the accuracy of conditioning is improved using a weighted combination of the best-matched patterns in data event in the simulation grid. The proposed algorithm's performance in this thesis is evaluated using various training image in the conditional and non-conditional simulation with well-known MPS algorithms such as SNESIM, FILTERSIM, and MS-CCSIM. Comparative results show that the CCWSIM seems more accurate to reproduce the multiple-point statistics of training image than the other algorithms. Most specifically, time benchmarking shows the proposed algorithm is at least 10 times faster than other algorithms.