# Intelligent Prediction of Stuck Pipe Using Combined Data-Driven and Knowledge-Driven Model
## 2 Related Research  
This section introduces the related research on the analysis and prediction of stuck pipe, including knowledge-driven and data-driven model. For the knowledge-driven model, in [2], the paper shows how to estimate two friction coefficients on a foot-by-foot basis at the wellsite with both measurement-while-drilling (MWD) and surface values of weight on bit (WOB) and torque. A log of the coefficients with depth can be used to diagnose drilling problems in directional wells. Field examples are given that show how the technique detects incipient stuck pipe. Since downhole data cannot be transmitted in real time, this method is mainly used after drilling. In [3], they present the results of the application of torque and drag model to directional wells drilled worldwide which was effectively used in these cases to aid in planning the directional program before spudding, to monitor the wells during drilling, and to analyze particular drilling problems after completion. In [4], the paper describes investigation of the friction between the pipe and the mud cake. It was found that the apparent coefficient of friction, or the \"stuck pipe\" coefficient, was not a constant; instead, it increased with increased time of contact between plate and mud cake. They think that the friction factor and mud cake quality are the factors that cannot be ignored. In [5], mud logging and daily reports of 75 wells in one of Iranian Southwest oil fields were studied. Mud weight, yield point, plastic viscosity and so on, a total of seven parameters were employed to introduce a new parameter called Reducing Stuck Index (RSI). For this field, the comparison of RSI of the current well with those of drilled wells predicted the probability of occurrence of stuck pipe very well. In [6], [PERSON] conducted a theoretical study on the bit stuck pipe caused by borehole deformation. It is found that this phenomenon is related largely to the magnitude of the lateral earth stresses, formation mechanical properties, and drilling-tool geometry. For the data-driven models, [PERSON] [7] used the multivariate discriminant analysis method to study stuck pipe for the first time. This method can be used to distinguish mechanical stuck, differential pressure stuck and non-stuck. [PERSON] [8] introduced mathematical methods such as fuzzy comprehensive discrimination and neural networks to predict stuck pipe, and proposed new methods for early warning and risk assessment of stuck pipe. Multivariate statistical regression and a discrimination analysis were developed for an Iranian field and presented by [PERSON] [9] to predict and reduce the risks of stuck pipe. [PERSON] and [PERSON] [10] used multivariate analysis to quantify the impact of design variables on the overall stuck pipe. Artificial Neural Network (ANN) has been extensively used in literature to solve stuck pipe related issues. Some studies used ANN to predict a stuck pipe [11; 12; 13], using different input parameters and data sizes, which were collected from several fields. They concluded that ANN was capable of predicting stuck pipe events with varying model accuracy. In [13], [PERSON] suggested coupling the ANN model output with a drilling log viewer for monitoring. In [14], [PERSON] applied several methods including ANN to predict stuck pipe events. Their study showed that both ANN and adaptive neuro-fuzzy inference systems are capable of predicting stuck pipe events with the same accuracy and suggested using ANN for simplicity. In [15], [PERSON] used ANN along with the support vector machine to predict stuck pipe events. In [16], Fuzzy logic and active learning were used for stuck pipe prediction. In [17], Automated real-time modeling and data analysis used to predict the risk of stuck pipe events were addressed by [PERSON]. In [18], [PERSON] and [PERSON] developed a stuck pipe risk analysis prediction model by introducing a stuck pipe index parameter. In [19], [PERSON] divided the drilling conditions into three situations and use ANN to realize the prediction of stuck-pipe.