SPE-208675-MS: Tim S. RobinsonDalila Gomes, Exebenus;  Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Tengu Ezharudin, Eswadi Othman, Faazmiar Technology SDN BHD

Abstract: Despite many drilling technology improvements during recent years, hole cleaning remains a significant challenge. The variation of equivalent circulation density (ECD) is a symptom of borehole instability. Therefore, the ability to accurately estimate ECD is a key consideration for preventing hole cleaning problems that may lead to a stuck pipe, and well pressure management more generally.

In this work, we demonstrate a Machine Learning approach to estimating downhole ECD in real-time using a deep neural network. Surface measurements that are widely available from most rigs are used as the model inputs, hence less configuration information is required relative to hydraulic simulations for pressure loss. Mean Absolute Errors of ~0.3-0.4 ppg were achieved on 16 validation wells and 7 holdout wells (blind test); these wells were independent of those in the training data. Prediction errors often reflect offsets between reference and predicted values; however, even with these offsets, trends in ECD behavior can still be captured correctly. The model shows promise for real-time ECD monitoring purposes to complement existing numerical methods and downhole tools.

Beyond real-time estimation, other applications could include forecasting ECD a short time ahead to provide early indications of hole cleaning issues; case studies obtained from a real-time monitoring centre where this approach is used are presented as part of this work. The software tool was capable of detecting such symptoms in advance, giving the driller opportunity to take preventive actions to avoid a potential stuck pipe.

Link to complete technical paper