Real-Time Stuck Pipe Detection based on Historic Data Analysis

Prasad Chandrakant Zende

Research output: ThesisMaster's Thesis


Pipe sticking has always been a major problem and one of the major Non-Productive Time (NPTs) for the drilling operations. Various methods have been used by industry ranging from training the personnel involved in rig operations to various on-field handling of the solution. The recent method which has been proposed is a real-time method using Torque & Drag (T & D) and Hydraulics analysis to predict impending stuck-pipe with warning signs to prevent it. The method was predicting the drilling parameters based on the sensor data in real-time, based on T & D and Hydraulics modelling. These predicted values were used as a baseline for the drilling parameters to compare with real-time drilling parameters. The main shortcoming of this method is the use of models for predicting the drilling activity parameters as it is well known that the models do not cover the entire physics of the wellbore. From this perspective, the purpose of this thesis is to propose an alternative real-time method to predict impending stuck-pipe based on historic data analysis. After carefully reviewing and studying the shortcomings of the recently developed stuck-pipe detection methods, a new method is proposed, the detailed steps of development of the proposed method are provided by the thesis. Briefly, the proposed method consists of two phases, preparation phase and implementation phase. The preparation phase consists of development of predictive models based on offset well data. The method for selection of input parameters for the development of models has been proposed in the thesis. In implementation phase, the predicted values of drilling parameters will be compared in real-time with actual drilling parameters to calculate the % deviation. Based on the deviation, the alerts would be generated, signalling the impeding stuck-pipe.
Translated title of the contributionEchtzeit Stuck-Pipe Erkennung basierend auf der Analyse historischer Bohrdaten
Original languageEnglish
Awarding Institution
  • Montanuniversität
  • Elmgerbi, Asad, Co-Supervisor (internal)
  • Thonhauser, Gerhard, Supervisor (internal)
Award date14 Dec 2018
Publication statusPublished - 2018

Bibliographical note

no embargo


  • Pipe Sticking
  • historic data analysis

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