Abstract

This research focuses on creating the prediction tools for the three key properties in coalbed methane (CBM) reservoir; the properties are gas content, Langmuir parameters, and permeability. Basically, their roles are to describe the gas in place and also future dynamic behavior of CBM reservoir. These three key properties are tried to be predicted with open-hole data as the inputs.

It uses artificial neural network (ANN) and adaptive neuro fuzzy inference system (ANFIS) to generate the prediction tools. It is started from data preparation and processing, then pattern or function identifications, and finalized by validation and testing. Several training algorithms are applied for ANN such as adaptive gradient descent (ANN_GDX), Levenberg-Marquardt (ANN_LM), resilient backpropagation (ANN_RP), scaled conjugate gradient (ANN_SCG), and Bayesian regularization algorithm (ANN_BR). The first fives employ the early stopping technique for regularization, and the last one does Bayesian regularization. On the other hand, the ANFIS will use only early stopping technique.

Based on this research, it is concluded that both ANN and ANFIS are able to identify the patterns or function between open-hole log data and the three key properties (TKP). Furthermore, it can be concluded that ANN_LM, ANFIS, and ANN_BR are the best selected algorithms which resulted the lowest error of TKP’s predictions.

This work is licensed under a Creative Commons Attribution 4.0 License.