Development of DNN Model for Predicting Surge Pressure Gradient During Tripping Operations
| Autor: | Shwetank Krishna, Pandian Vasant, Syahrir Ridha |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
Tripping 0202 electrical engineering electronic engineering information engineering 020206 networking & telecommunications 02 engineering and technology Surge 010502 geochemistry & geophysics 01 natural sciences Pressure gradient 0105 earth and related environmental sciences Marine engineering |
| Popis: | Application of machine learning tools in drilling hydrocarbon well is still exploratory in its stage. This chapter presents a brief review of various applied research in drilling operations using machine learning (ML) tools and develop a deep neural network (DNN) model for predicting the downhole pressure surges while tripping. Tripping in or out drill-string/casing with a certain speed from the wellbore will result in downhole pressure surges. These surges could result in well integrity or well control problems, which can be avoided if pressure imbalances are predicted before this operation is engaged. Existing analytical models focus on forecasting the pressure imbalance but requires cumbersome numerical analysis. This could be solved by integrating DNN tool with the best existing analytical model predicted dataset. Consequently, the aim of this chapter is to provide an overview of various applications of machine learning tools in drilling and presenting a step-by-step process of developing a DNN model for the prediction of downhole pressure surges during tripping operation. |
| Databáze: | OpenAIRE |
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