Hybrid clustering-estimation for characterization of thin bed heterogeneous reservoirs

Abstract

Modeling heterogeneous reservoirs is cumbersome as it requires a great effort to determine the variation of properties with respect to direction, while the lack of adequate data makes this a difficult task. Generating static models for heterogeneous reservoirs remains an important challenge in petroleum engineering applications which requires more investigations. Some heterogeneous reservoirs, such as thin bed reservoirs, may be divided into some homogeneous subzones where characterization of these homogeneous sub-reservoirs and their integration can represent the properties of the heterogeneous reservoir. To investigate this concept, in this paper, three exemplar reservoirs (ER) were generated. The heterogeneity in the data is increased from ER1 to ER2 and ER3. In the first step, each reservoir was studied as one single zone, so the results can be compared with the proposed method in this work. Ordinary kriging (OK) and multilayer perceptron neural network (MLP) were used for modeling of these exemplar reservoirs. This study showed that OK cannot model reservoir characteristics, whereas the MLP yielded reasonably acceptable results. In the next step, a hybrid clustering classification-based method was applied to divide the reservoir to homogeneous subzones. Each reservoir was modeled in terms of its homogeneous subzones. The homogeneous subzones were modeled using OK and MLP. The results showed that the developed model was successful in modeling the heterogeneity at a reasonable CPU processing time. Also, it was seen that in case of using the simple modeling techniques, MLP neural network yields more reasonable results, compared with OK.