POTENTIAL HYDROCARBON ZONES PREDICTION USING ARTIFICIAL NEURAL NETWORK APPLICATION - CASE STUDY : FIELD X BACHELOR THESIS David Kristianto 12215079 Submitted as partial fulfillment of the requirements for the degree of BACHELOR OF ENGINEERING in Petroleum Engineering study program PETROLEUM ENGINEERING STUDY PROGRAM FACULTY OF MINING AND PETROLEUM ENGINEERING INSTITUT TEKNOLOGI BANDUNG 2019 1 POTENTIAL HYDROCARBON ZONES PREDICTION USING ARTIFICIAL NEURAL NETWORK APPLICATION - CASE STUDY : FIELD X David Kristianto* and Amega Yasutra** Copyright 2019, Institut Teknologi Bandung Abstract One important aspect in petroleum engineering is to define the zone interest, which indicate that there is potential value of hydrocarbon reserve. Up until now, well log analysis is the standard method to determine those potential hydrocarbon zones by estimating water saturation parameter using empirical correlations developed by experts, such as Archie equation for clean sandstone, Simandoux and Waxman-Smits for shaly sandstone. However, pay zones interpretation from well log analysis is heavily depended upon human interpretation and field evaluation. Result of created water saturation log are limited by assumptions of used correlations, therefore it might be possible that human errors are happening when interpreting pay zones. The approach of this study is to eliminate those assumptions created when analysing potential pay zones, using machine learning application to create prediction based on well log input data without further interpretation. An Artificial Neural Network (ANN) model is created using well log data for input and production rate data for output, the neural network model are then tested to give prediction in multiple wells. Based on production rate data, fractional flow data can then be determined and being used as output data. Created ANN model is composed of 2 layer and 5 optimum hidden neurons with feedforward back-propagation learning algorithm. ANN model gives correlation factor result of 0.8990 for training, 0.9233 for validation and 0.9910 for testing. The model is then used for predicting pay zones in which two kinds of prediction test are conducted. The first prediction is applied on 30 samples of production rate data test as comparison, producing a correlation factor result of 0.8432. The second prediction is applied on a well without data test as comparison. Overall, created ANN model is able to give good prediction of pay zones matched with data test result with limitations that the model is dependent on the quality and numbers of data being used. Keywords: Pay Zone, Artificial Neural Network, Feedforward Back-Propagation, Machine Learning, Fractional Flow Sari Salah satu aspek penting dalam teknik perminyakan adalah dalam hal pendefinisian zona interest, yang menunjukkan bahwa terdapat potensi cadangan hidrokarbon di dalamnya. Sampai saat ini, metode evaluasi zona yang berpotensi mengandung hidrokarbon ini adalah melalui analisa log sumur dengan menentukan nilai saturasi air menggunakan korelasi yang dikembangkan para ahli, seperti persamaan Archie untuk clean sandstone, Simandoux dan Waxman-Smits untuk shaly sandstone.