Construction of fuzzy systems based on fuzzy c-means clustering and singular value decomposition for predicting rate of penetration in geothermal drilling

Abstract

The potential for geothermal energy is very abundant, but its utilization is still minimal. Therefore, the utilization of geothermal energy facility that has been installed must be optimized. This study aims to predict drilling rate of penetration using the first-order Sugeno’s fuzzy system. Fuzzy c-mean and singular value decomposition were used to form the rules and determined the parameters respectively. This study used in total of 6738 data of geothermal wells drilling in Indonesia. The results show that the rate of penetration prediction has accuracy 85.76% for data training and 87.72% for data testing, and it is better than the radial basis function neural networks (RBFNN) and RBFNN-singular value decomposition (SVD) methods.