A Data-Based Approach for the Prediction of Stuck-Pipe Events in Oil Drilling Operations

Pipe-sticking during drilling operations causes severe difficulties, including economic losses and safety issues. Therefore, stuck-pipe predictions are an important tool to preempt this problem and avoid the aforementioned troubles. In this study, we have developed a prediction technique based on artificial intelligence, in collaboration with industry, the government, and academia. This technique was an unsupervised learning model built using an encoder-decoder, long short-term memory architecture. The model was trained with the time series data of normal drilling operations and based on an important hypothesis: reconstruction errors between observed and predicted values are higher around the time of pipe sticking than during normal drilling operations. The trained model was then applied to 34 actual stuck-pipe events, where it was found that reconstruction errors increased prior to the pipe sticking in some cases (thereby partly confirming our hypothesis) and were sensitive to large variations in the drilling parameters.

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Approach for Real-Time Prediction of Pipe Stuck Risk Using a Long Short-Term Memory Autoencoder Architecture

Nakagawa

Inoue

Bilen

et al. 2021

Day 4 Thu, November 18, 2021

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Pipe-sticking during drilling operations causes severe difficulties, including economic losses and safety issues. Therefore, real-time stuck-pipe predictions are an important tool to preempt this problem and avoid the aforementioned troubles. In this study, we have developed a prediction technique based on artificial intelligence, in collaboration with industry, the government, and academia. This technique was developed by combining an unsupervised learning model built using an encoder-decoder, long short-term memory architecture, with a relative error function. The model was trained with the time series data of normal drilling operations and based on an important hypothesis: reconstruction errors between observed and predicted values are higher around the time of pipe sticking than during normal drilling operations. An evaluation method of stuck-pipe possibilities using a relative error function reduced false predictors caused by large variations of drilling parameters. The prediction technique was then applied to 34 actual stuck-pipe events, where it was found that reconstruction errors calculated with the relative error function increased 0.5-10 hours prior to the pipe sticking for 17 out of 34 stuck-pipe events (thereby partly confirming our hypothesis).

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A Data-Based Approach for the Prediction of Stuck-Pipe Events in Oil Drilling Operations

Cited by 6 publications

References 11 publications

Holistic autonomous model for early detection of downhole drilling problems in real-time

Holistic autonomous model for early detection of downhole drilling problems in real-time

An Unsupervised Learning Model for Pipe Stuck Predictions Using a Long Short-Term Memory Autoencoder Architecture

Approach for Real-Time Prediction of Pipe Stuck Risk Using a Long Short-Term Memory Autoencoder Architecture

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