*3.1. Artificial Neural Network*

Figure 3 illustrates a schematic of the ANN model developed for estimating the CO2-MMP. The model inputs are reservoir temperature (Temp.), the molecular weight of the heptane plus (MWC7+) and the mole fraction of ethane to hexane (C2–C6%). The ANN model was trained using the seen data (training dataset), then the model becomes ready to determine the MMP for the testing data (unseen data). The model parameters were fine-tuned to minimize the AAPE and maximize the correlation coefficient. The ANN parameters were fine-tuned by changing the number of hidden layers and the number of neurons per each layer, and the best predictive models listed in Table 3. Three cases were reported, the number of hidden layers and neuron per each layer were varied to find the best ANN model. A minimum error of 7.22% and a relatively high correlation coefficient of 0.974 were obtained by using one hidden layer with 20 neurons. Figure 4 shows the predicted results against the actual values for training and testing data for visual validation.

**Figure 3.** Artificial neural network (ANN) model architecture with input, hidden and output layers.

**Table 3.** Artificial neural network (ANN) for testing results.


**Figure 4.** Cross plot of actual against predicted CO2-MMP using the ANN model for (**A**) the training data set and (**B**) the testing data.
