CO2 sequestration and enhanced coal bed methane (ECBM) extraction necessitate CO2 injection into coal reservoirs that affect the coal strength properties and long-term integrity of the seam. Evaluation of CO2-induced coal strength alterations is essential to minimize the reservoir damage. Advanced soft computing models have become prevalent in rock mechanics field, as they are capable of learning trends from complex data sets, preserving the experience and using it for predictions. We present two models viz. artificial neural network (ANN) and adoptive neuro-fuzzy inference system (ANFIS) to predict the strength alterations of coal, under various CO2 saturation conditions. Model performances are compared with linear and non-linear multivariate regression analyses (L-MRA and NL-MRA). We consider three effective input parameters (i.e. coal type, CO2 saturation pressure and CO2 interaction time) and one output parameter (i.e. unconfined compressive strength (UCS)) in the models. ANN consists of a three-layer feed-forward back-propagation network with a 3-5-1 architecture and ANFIS consists of [4 4 4] Gaussian type membership functions. Model results confirm that ANFIS has the highest prediction capacity followed by ANN, with R2 equal to 0.9954 and 0.9933, respectively. Both L-MRA and NL-MRA prediction performances are not satisfactory, as R2 values are only 0.7854 and 0.7821 for two models, respectively. Thus, general statistical models like MRA fail to precisely predict the complex strength alterations. From the verified models, we show that well-trained ANN and ANFIS models can successfully fit and forecast the experimental data, and are able to predict the long-term CO2 saturation effect on coal strength.
- Adaptive neuro-fuzzy inference system (ANFIS)
- Artificial neural network (ANN)
- CO interaction time
- CO saturation pressure
- Coal rank
- Unconfined compressive strength (UCS)