• Data Boosted Stuck Pipe Prevention: Integrating Machine Learning Models into the Mud Plan Design Workflow

    The main objective of this project is to develop a model that, based on the data of previously drilled wells, allows the user to evaluate the mud plan and identify the probability of a stuck pipe event occurrence. This is done by using statistical predictive models, particularly in this case, neural network models. The proposed model takes the input data from the Volve’s dataset.

    The trained model shows high accuracy, higher than 84% on the test data. After the model is trained and validated, it’s implemented into a frontend which will serve as the front user interface where he can upload the mud plan parameters and obtain a stuck pipe event chance evaluation. Having these results, the user can improve the mud plan in order to reduce the chance that an event of this type occurs.

    The main advantage of this methodology is that it allows to include historical data from the other drilled wells, this way, boosting the mud plan design with real data and reducing the chances that a stuck pipe event, that account for a high percentage of NPT’s during drilling operations, occur.

    Our archived 2020-21 event content is now available free to SPE members! This presentation in English is from the 2020 SPE Latin American and Caribbean Petroleum Engineering Conference.

    • Paper Number: SPE-198972-MS
    • Published: 20 July 2020
    • Authors: Carlos Andrés Berdugo Arias; Angie Tatiana León Torrado; Juan Sebastián Llano Carvajal

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  • Data Boosted Stuck Pipe Prevention: Integrating Machine Learning Models into the Mud Plan Design Workflow