Development of DNN Model for Predicting Surge Pressure Gradient During Tripping Operations

Introduction

The purpose associated with this book chapter is to explain the development of an intelligent model using deep neural network (DNN) for controlling well integrity problem during tripping operations. This model uses the historical or real-time data for predicting the surge/swab pressure associated with tripping operations. By prescribe remedial solution for lost circulation zone by integrating different data sets equip by various service companies. This model will further optimize the drilling operations, reduces non-productive time (NPT) and lowers drilling risks.

In comparison with other method, ML finds quick results from data due to its flexibility and automatic perception. Machine learning is a data analytics method that educates computers to do what comes normally to people and animals: learn from experience (Pankaj et al., 2018). Machine learning calculations utilize computational strategies to “learn” data specifically from information without depending on a foreordained condition as a model. The algorithms adaptively improve their execution as the number of tests available for learning increases (Pankaj et al., 2018). In fact, ML can deal with the lack of proper physical models or in the presence of uncertainty that usually effect the outcomes in other approaches. The thought of ML is to prepare a model on known input and yield data so that it can foresee future yields (supervised learning) or finding covered up designs or inherent structures in input data (unsupervised learning) (Raschka & Mirjalili, 2017). Then when different input arrives, the ML predict the correct output by applying what it has been taught. For this, ML will require a large set of selective data (historic and real-time data) to develop this model. The drilling business has endured a ton because of various drilling anomalies. An industry average estimate of the additional cost to the well caused by lost circulation specifically, is around 40 percent, with most of this cost due to rig down-time (Reddy, Zhang, & Ellis, 2016). This is excluding the apparatus costs because of the harm to the drill pipe and/or blowout. This could be optimized by developing predictive models.