Perkiraan Laju Penetrasi Pengeboran Sumur Panas Bumi Menggunakan Adaptive NeuroFuzzy Inference System - Muhammad Taufiq Fathaddin; Rini Setiati; Havidh Pramadika; Agus Guntoro; Fidya Varayesi; Alvita Kumala Sari (Jurnal Offshore: Oil, Production Facilities and Renewable Energy, 2024)
Plan an optimal geothermal well drilling scheme, the identification of suitable drilling parameters must be well known.Several important parameters in a drilling operation include rotary speed (N), weight on bit (WOB), true vertical depth (TVD), foamflowrate (FF), and rate of penetration (ROP). Information regarding these parameters can be obtained from drilling geothermal wells.Drilling parameter correlations are then obtained based on this information. The application of an adaptive neuro-fuzzy inferencesystem (ANFIS) is necessary considering that the relationship between parameters is very complicated and non-linear. On the other hand, the relationship between parameters is not easy to know. In this study, the ANFIS model is developed to propose ROP. Data wasobtained from four wells in a geothermal field in South Sumatra. Three ANFIS models were generated. Each model includes different input parameters. rotational speed (N) and weight on drill bit (WOB) and true vertical depth (TVD) are recommended for estimation of rate of penetration (ROP). Adding the foam flow (FF) input rate parameter can improve the accuracy in three out of four cases. Based on the calculation results of the ANFIS-1, ANFIS-2, and ANFIS-3 models, the average relative absolute deviation (MARE) values were 16.42%, 6.99%, 4.14%, respectively, while the correlation coefficient (R) was respectively - respectively 0.716, 0.909, and 0.937