Improper hole cleaning or drilled-cutting transportation impacts drilling operations. To illustrate, inefficient cleaning of the wellbore can lead to many drilling problems such as low drilling rate (i.e. low ROP), early bit wear and, in the severe cases, a complete loss of the well due to stuck pipe. To understand efficiency in cutting transport in drilling and to provide solutions for the problem, many studies have been conducted. In all cases, they provide empirical models based on experimental data. In this study, different artificial intelligence (AI) techniques are employed to estimate the concentration of cuttings present in the wellbore. The purpose of this study is to indirectly measure the hole-cleaning efficiency in order to predict the cutting concentration from drilling parameters using artificial intelligence techniques. The study is based on 116 experimental data records from the studies. Two AI techniques were selected, namely artificial neural network (ANN) and support vector machine (SVM), to estimate the cutting concentration in the wellbore. The input parameters comprise mud density and mud rheological properties (yield point and plastic viscosity) in addition to drilling parameters including the hole inclination angle, pipe eccentricity (i.e. location of the drill pipe from the axis of the well), the rate of penetration (ROP), flow rate (GPM), drill pipe rotary speed (RPM) and temperature. The results obtained show the ability of the two employed techniques to accurately predict the cutting concentration in the wellbore with average absolute errors (AAE) less than 5% and correlation coefficients (R) higher than 0.9. Comparison of these results with a literature model showed that the AI techniques provide better predictions of cutting concentration and higher accuracy than that model. Applying the developed AI technique will help the drilling engineers to assess the hole cleaning in a real time.

Continuous monitoring of the rheological properties of the drilling mud is essential so that any drilling operation can be completed more effectively and efficiently with the least problems. Mud rheological properties play a vital role in the in the efficiency of the drilling fluid to lift the cuttings from the wellbore. The mud rheological properties include the plastic viscosity, apparent viscosity, and the yield point. However, these properties are not measured continuously during the drilling process and they are only measured once or twice a day while other mud properties, such as the mud weight, the Marsh funnel viscosity, and solid content, are measured regularly and continuously. Therefore, it is valuable to come up with a relation that relates the mud rheological properties to these parameters. Many researchers tried to introduce models that allow for the prediction of the apparent viscosity from the Marsh funnel viscosity. However, these models have the deficiency that the prediction is with high errors. For the first time, the solid percent was used to predict the rheological properties of the oil-based drilling fluid based on the artificial neural network using actual field measurements. The purpose of this study is to use the Artificial Neural Networks (ANN) Technique to develop a model that allows the prediction of the mud rheological properties such as the plastic viscosity, apparent viscosity, the rheometer readings at 600 and 300 rpm and the flow behavior index for oil-based mud from the mud weight, the Marsh funnel viscosity and solid content. The study is based on 400 data points collected from the field measurements of actual drilling fluid samples. The obtained results showed that the five developed models using ANN technique can be used to predict the rheological properties of oil- based drilling fluid with a high accuracy; the average absolute error was less than 5% and the correlation coefficient was higher than 90%. The developed technique is inexpensive with no additional required equipment. It will help the drilling engineers to calculate the equivalent circulation density, surge and swab pressures, and hole cleaning which are strong functions of the rheological parameters in a real time. The method and approach used in this paper to predict and determine the unknown drilling fluid properties and trend out of accurately defined parameters is futuristic and progressive. The method is one step forward toward automating the drilling fluid system which is another step forward toward fully automating the drilling process overall.