Application of Artificial Neural Networks for Predicting Relative Permeability in Talang Akar Formation in Field X
Muhammad Taufiq Fathaddin1*, Alvita Kumala Sari1, Daddy Sutansyah1, Baiq Maulinda Ulfah2, Wisup Bae3, Pri Agung Rakhmanto1, Sonny Irawan4

1) Universitas Trisakti, Jakarta, Indonesia
2) Sekolah Tinggi Teknologi Minyak dan Gas Balikpapan, Balikpapan, Indonesia
3) Energy and Mineral Resource Engineering Department, Sejong University, Republic of Korea
4) Nazarbayev University, Nur Sultan, Kazakhstan

Abstract

Relative permeability is an important parameter for estimating multi-phase fluid flow in porous rocks. Relative permeability is a complex physical property that is influenced by the behavior and interactions between the fluid and rock phases. Measurement of the relative permeability of cores in the laboratory can be carried out using steady-state or non-steady-state techniques. Permeability measurement is relatively difficult and time consuming. Because of the difficulty in measurement, empirical models are often used to estimate relative permeability or extrapolate to limited laboratory data. Artificial neural network (ANN) is a method that can be used to obtain complex correlations of parameters that influence each other. In this study, ANN is used to predict the relative permeability of water and oil. The proposed model evaluates the relative permeability of a phase as a function of porosity, rock absolute permeability, depth, permeability of other phases and water saturation. A total of 159 relative permeability data from field X were used for the training and testing processes. Based on the comparison between measured and calculated data, the correlation coefficients for relative permeability to oil and water using ANN method are 0.77 and 0.94 respectively. While those using regression analysis are 0.88 and 0.73 respectively.

Keywords: Artificial Neural Network, Relative Permeability, Oil, Water, Regression Analysis

Topic: Engineering