Differential pipe sticking has a major effect on drilling efficiency and well costs. This problem is affected by many parameters, such as drilling fluid properties and the characteristics of the mud cake that is formed while drilling. Comparatively little research has assessed the prediction of stuck pipe. Traditionally, stuck pipe problems are solved by using some standard methods and techniques after they occur, but the real key to savings and success is in the avoidance of the risks associated with the stuck pipe. If these risks are identified in advance, better solutions can be provided to reduce the associated costs. To account for all of the aspects of differential pipe sticking and anticipated nonlinear behavior of the variables involved, fuzzy logic and neural network modeling can be used as primary predictive tools. These methods are widely used in other industries and in the petroleum field, especially in reservoir and core analyses. This paper presents a study of the application of the concepts of fuzzy logic to the problem of differentially stuck pipe. These methods make it possible to estimate the risk of stuck pipe occurrence in the well planning procedure and during drilling in real time. The nonconvolutional analysis of the model is based on the constraints of different drilling variables. Discriminant function analysis and fuzzy logic were used to classify drilling and mud variables. Extensive simulations were performed. This paper presents a case study in which the fuzzy logic and neural network modelling are successfully used to estimate and predict pipe sticking.

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