# A Novel Efficient Borehole Cleaning Model for Optimizing Drilling Performance in Real Time
## 5 The Importance of Using the Novel \(\text{TI}_{\text{m}}\) Model in Real-Time  
Unlike existing models that depend on laboratory data and lack real-time forecasting capabilities, the proposed model (\(\text{TI}_{\text{m}}\)) uses a combination of real-time, surface, and operational data to provide instant predictions and documentation with good depth. This enables early identification and mitigation of abnormalities, resulting in reduced drilling costs and operational time. Figure 14 shows the automated process of utilizing field data to evaluate borehole cleaning using the \(\text{TI}_{\text{m}}\) model for optimizing the drilling performance efficiency. As seen in Figure 14, the novel real-time evaluated model (\(\text{TI}_{\text{m}}\)) addresses the limitations of existing drilling operation models by providing instant predictions based on a combination of real-time, surface, and operational data. The automated flowchart demonstrates the efficacy of this model in enhancing borehole cleaning performance and overall drilling efficiency.  
Figure 13: Application of \(\text{TI}_{\text{m}}\) in Well C in the case of a stuck pipe: (**a**) Well-C with poor borehole cleaning, (**b**) ROP, and (**c**) TRQ.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**N**} & \multicolumn{4}{c}{**Effect of Using the Novel \(\text{TI}_{\text{m}}\) Model on the Performance of Well-C**} \\ \cline{2-6}  &
\begin{tabular}{c} **Items** \\ **(Output)** \\ \end{tabular} & **Minimum** & **Maximum** & **Average** & **Statement** \\ \hline
1 & \(\text{TI}_{\text{m}}\) & 0.25 & 1.8 & 0.56 &
\begin{tabular}{c} Insufficient borehole cleaning \\ Lower ROP due to insufficient \\ borehole cleaning \\ Higher TRQ due to the cutting accumulation resulted in stuck pipe incident \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 8: Effect of utilizing the novel \(\text{TI}_{\text{m}}\) model on well performance.  
This innovative approach has the potential to significantly improve drilling operations, leading to reduced costs and increased resource extraction. The use of the TI\({}_{\text{m}}\) model allows the identification and mitigation of drilling abnormalities at an early stage, thereby reducing drilling costs and minimizing operational time. As a result, the TI\({}_{\text{m}}\) model significantly enhances drilling performance efficiency. The TI\({}_{\text{m}}\) model utilizes advanced algorithms and mathematical models to analyse the data and identify the most effective operational strategies for optimizing borehole cleaning. By considering various factors such as drilling fluid properties, wellbore geometry, and drilling parameters, the TI\({}_{\text{m}}\) model can determine the optimum values of key parameters required for efficient drilling operations. More importantly, it is crucial to ensure the accuracy and quality of sensor data acquisition to prevent errors or inaccuracies that may lead to incorrect conclusions and decisions. Additionally, the TI\({}_{\text{m}}\) model is based on certain assumptions, such as the absence of total lost circulation incidents and well control incidents. These assumptions are critical to the effective application of the TI\({}_{\text{m}}\) model and must be considered when utilizing it in operational decision-making.