# Drilling Fluid and Cement Slurry Design for Naturally Fractured Reservoirs
## 3 Prediction of Lost Circulation  
For the successful drilling of a well through naturally fractured reservoirs to the target depth, predicting the loss zone using artificial intelligence can be of great help [1, 27, 28, 29, 30, 31, 32, 51]. [PERSON] et al. (2010) tried to predict lost circulation in different areas in Maroun oilfield using drilling daily reports from 32 drilled wells. They used artificial neural networks to estimate the quantity and quality of fluid loss for each day using recorded data in that day or previous days. According to their research, the network precision in cases of low mud loss is very good, but in cases of very severe losses, it is significantly worse. The reason for that can be data inconsistency of input parameters. Also, many parameters which can affect lost circulation are not considered with the input data [28]. [PERSON] et al. (2014) designed two models using Artificial intelligence and data collected from 38 wells located in Maroun oilfield, in the Middle East. Both models were developed by a modular neural network for the prediction of the amount of mud loss in any coordinates of a field using operational and geological data. They concluded that the neural network had an accurate prediction of lost circulation in the Asmari formation of the Maroun oilfield and recommended utilizing an artificial neural network just for the field under study [32]. Unfortunately, the model is applicable only in a specific area, so other fields should utilize a new neural network [52]. [PERSON] et al. (2016) studied the impact of petrophysical rock properties on lost circulation in terms of lack of information. Their research was performed by cluster analysis (FCM-method). Cluster-analysis or automatic classification problems are widely used in various fields, including in the petroleum industry [51]. [PERSON] et al. (2017) applied an artificial neural network to predict the fracture width and determine the particle sizes of the loss prevention materials. Their results were \begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Source**} & \multicolumn{4}{c}{**Severity of Loss**} \\ \cline{2-5}  & **Seepage** & **Partial** & **Severe** & **Total** \\  & **(Minor)** & **(Moderate/Medium)** & **(Massive/Complete)** & **(Complete)** \\ \cline{2-5}  & \multicolumn{4}{c}{**Typical Loss Rate (m\({}^{3}\)/h)**} \\ \hline \hline \multirow{4}{*}{[PERSON], 2020 [45]; Drilling Specialities Company, 2014 [46]} & \multicolumn{4}{c}{Water-based mud} \\ \cline{2-5}  & \(<\)4 & 4 to 16 & \(>\)16 & no returns \\ \cline{2-5}  & \multicolumn{4}{c}{Oil-based mud} \\ \cline{2-5}  & \(<\)1.6 & 1.6 to 4.8 & \(>\)4.8 & no returns \\ \hline [PERSON] et al., 2019 [8] & \(<\)1.6 & 1.6 to 3.2 & 3.2 to 32 & no returns \\ \hline [PERSON] et al., 2019 [47] & - & - & \(>\)25 & no returns \\ \hline [PERSON] et al., 2018 [48] & \(<\)1.6 & 1.6 to 16 & 16 to 80 & no returns \\ \hline MI-Swaco, 2011 [36] & \(<\)1.6 & 1.6 to 16 & - & no returns \\ \hline [PERSON] et al., 2016 [7] & - & 1.6 to 8 & - & no returns \\ \hline [PERSON] et al., 2014 [11]; [PERSON] et al., 2007 [9] & - & 16 to 8 (s.c.) & 8 to 24 (s.c.) & \\  & \multicolumn{4}{c}{10 to 20\% of the mud} & 50 to 100\% of the mud & no returns \\  & \multicolumn{4}{c}{(d.c.)} & (d.c.) \\ \hline [PERSON], 1987 [43]; [PERSON], 2006 [44]; [PERSON] et al., 2014 [4] & 0.16 to 1.6 & 1.6 to 80 & \(>\)80 & no returns \\ \hline [PERSON] et al., 2003/2004 [49] & \(<\)1.6 & \(>\)1.6, but some fluid & - & no returns \\ \hline [PERSON] et al., 1997 [10] & 1.6 to 3.2 (s. c.): 0.16 to & - & 8 to 24 (s. c.); 50 to & \\  & 1.6 (d. c.) & - & 100\% of the mud (d. c.) & no returns \\ \hline [PERSON] et al., 2018 [50] & \(<\)1 & 1 to 10 & \(>\)15 & no returns \\ \hline \hline \end{tabular} Note: s.c.—static condition; d.c.—dynamic conditions.  
\end{table} 
Table 3: Classification of losses according to different authors and companies. compared with existing 2D fracture models and benchmarked against experimental results published in the literature.