## 4 Experiments and Results
### Experiments with a Neural Network Classifier  
In addition to the Random Forest Classifiers, Neural Networks were applied to classify the operational states shown in Table 6. In combination with the well-known Sequential Forward Selection (SFS) [6], an estimate of the channels and features relevant for the classification task was made in order to compare it to the features recommended by the SA method. In addition, the performance of the classifier is of interest for comparison to the Random Forest Classifiers. Neural Networks in general generate a substantive computational load in computers. In addition, feature selection increases this. To constrain the computation time to some acceptable time, a subset of the data was extracted from each of the four wells. A drilling process is usually separated into so-called runs. For each such run, a rough description of what happened in the well or at the rig is provided. A drilling run includes all the operations applied during the actual drilling of a well and typically consists of trip-in the drill string, drill the well, and concludes with a drilling trip-out of the drill string (see Figure 8). For the experiments with the Neural Networks, the data were extracted as shown in Table 9. From each well, one single drilling run was selected, and that data was separated into 3 subsets for learning (60%), validation (20%), and testing (20%). For the classification, a special network architecture, the improved completely connected perceptron (iCCP) shown in Figure 12a, was used. The design of the network layer, number of hidden layers, and number of neurons in each hidden layer, is one of the  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multicolumn{6}{c}{**Features (Intuitive Selection)**} & \multicolumn{6}{c}{**Features (SA Recommendation)**} \\ \hline
**Base** & **1 st** & **2 nd** & **3 rd** & **1 st** & **MI** & **2 nd** & **MI** \\ \hline C0108 & :H01 & :H02 & :H12 & :H19 & 1.000 & :H59 & 1.000 \\ C0110 & :H01 & :H02 & :H12 & :H30 & 1.000 & :H19  
major challenges in designing a multi-layer perceptron network. The advantage of the iCCP architecture compared to the multi-layer perceptron is that all neurons in the hidden block are completely connected, and thus the search for the optimal network complexity is straightforward. In oil and gas exploration tasks, this architecture has been applied successfully to simulate drilling hydraulics [44]. For all our experiments identical configurations were used. A total of 10 networks were trained in parallel to prevent them from being trapped in local error minima. Network growth was started from scratch, with no neurons in the hidden block, equivalent to multi-linear regression. Then, the hidden neurons were increased one by one until a maximal number of five hidden neurons were obtained. The number of inputs to all networks was at the utmost of the channels shown in Table 9, but via application of SFS, the input to the networks was managed with forward selection. In the first experiment, the extracted data from all four wells were used as input to the model. Using all of the 57 channels as inputs to the model, a correct classification rate of about 93% was obtained, but the outcomes were improved by reducing the number of inputs according to the results of the feature selection. The results of the feature selection are shown in Figure 13, with 10 out of the 57 channels/features selected (4 of those inputs are recommendations from the ETSA method). The most important input to the model was the difference between bit depth and borehole depth (D0101). The second most important input is the hook load (C0114), followed by rotary speed of the bit (C0120). The CCRs (learning, validation, and test subsets), using  
Figure 12: Neural Network Architectures: (**a**) Completely Connected Perceptron and (**b**) Multi-Layer Perceptron. Figure 13: Feature Selection Results—All Wells. only those three channels as input, were all within a range of about 87%.