The Implementation of Machine Learning in Lithofacies Classification using Multi Well Logs Data

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns

Analisis Faktor Kapasitas Pembangkit Listrik Hibrida PLTB dengan PLTD di Pulau Terpencil: Studi Kasus Elat Pulau Serau Maluku

ABSTRAKRasio elektrifikasi di Indonesia belum mencapai 100%, penyebabnya antara lain masalah lokasi di daerah terpencil atau kepulauan dan mahalnya biaya operasi PLTD. Salah satu solusi adalah membangkitkan listrik berbasis energi terbarukan setempat. Tahap awal pemanfaatan energi terbarukan perlu dihitung faktor kapasitas (CF). Tujuan penelitian ini menganalisis CF untuk PLTB dengan metode perhitungan analitik berbasis potensi energi angin, spesifikasi teknologi PLTB dan PLTD, profil beban dan energi listrik yang dapat diproduksi untuk pengembangan sistem hibrida dengan mengambil kasus di Elat Pulau Serau Maluku. Hasil perhitungan CF untuk 5 teknologi PLTB yang berbeda dengan variasi ketinggian di Elat telah diverifikasi dengan simulasi menggunakan perangkat lunak HOMER dengan nilai rerata galat -0,030. Semakin tinggi PLTB, nilai CF semakin besar dengan konstanta 0,0030.Kata kunci: elektrifikasi, faktor kapasitas, PLTB, PLTD, sistem hibrida ABSTRACTThe electrification ratio in Indonesia has not reached 100%, the causes include problems with the location in remote areas or islands and the high operating costs of diesel power plant (DPP). One solution is to generate electricity based on local renewable energy. The initial stage of utilizing renewable energy needs to calculate the capacity factor (CF). The purpose of this research is to analyze CF for wind turbine generator (WTG) with analytical calculation methods based on wind energy potential, technology specifications of WTG and DPP, load profiles and electrical energy that can be produced for hybrid system development by taking the case in Elat Serau Island, Maluku. The results of CF calculations for 5 different WTG technologies with altitude variations in Elat have been verified by simulation using HOMER software with a mean error value of -0.030. The higher the WTG, the greater the CF value with a constant of 0.0030.Keywords: electrification, capacity factor, diesel power plant, wind turbine generator, hybrid syste

The Implementation of Machine Learning in Lithofacies Classification Using Multi Well Logs Data

Lithofacies classification is a process to identify rock lithology by indirect measurements. Usually, the classification is processed manually by an experienced geoscientist. This research presents an automated lithofacies classification using a machine learning method to increase computational power in shortening the lithofacies classification process's time consumption. The support vector machine (SVM) algorithm has been applied successfully to the Damar field, Indonesia. The machine learning input is various well-log data sets, e.g., gamma-ray, density, resistivity, neutron porosity, and effective porosity. Machine learning can classify seven lithofacies and depositional environments, including channel, bar sand, beach sand, carbonate, volcanic, and shale. The classification accuracy in the verification phase with trained lithofacies class data reached more than 90%, while the accuracy in the validation phase with beyond trained data reached 65%. The classified lithofacies then can be used as the input for describing lateral and vertical rock distribution patterns