Utilizing Artificial Neural Network for Real-Time Prediction of Differential Sticking Symptoms

Abstract

Stuck pipe is one of the leading causes of non-productive time (NPT) while drilling. Machine learning (ML) techniques can be used to predict and avoid stuck pipe issues. In this paper, a model based on ML to predict and prevent stuck pipe related to differential sticking (DS) is presented. The stuck pipe indicator is established by detecting and predicting abnormalities in the drag signatures during tripping and drilling activities. The solution focuses on detecting differential sticking risk via assessing hookload signatures, based on previous experience from historical wells. Therefore, selecting the proper training set has proven to be a crucial stage of model development, especially considering the challenges in data quality. The model is trained with historical wells with and without differential sticking issues. The solution is based on the Artificial Neural Network (ANN) approach. The model is designed to provide users, i.e., driller or monitoring specialist, a warning whenever a risk is identified. Since multi-step forecasting is used, the warning is given with enough time for the driller or monitoring specialist to evaluate which preventative action or intervention is necessary. The warnings are provided typically between 30 minutes and 4 hours ahead. The model validation includes the performance metrics and a confusion matrix. Practical cases with real-time wells are also provided. The ML model was proven robust and practical with our data sets, for both historical and live wells. The huge amount of data produced while drilling holds valuable information and when smartly fed into an Artificial Intelligence (AI) model, it can prevent NPT such as stuck pipe events as demonstrated in this paper.

Prediction Models of Shielding Effectiveness of Carbon Fibre Reinforced Cement-Based Composites against Electromagnetic Interference

“…Moreover, conducting such experiments for the shielding properties is also expensive and time consuming compared to numerical modelling. The previous literature has shown that the interaction between the materials’ properties and machine learning algorithms has grown rapidly, and the good performance of machine learning algorithms in estimating or predicting these properties using mixed design parameters has been witnessed by a wide range of research articles [ 13 , 14 , 15 , 16 , 17 ]. Machine learning models have a series of advantages, such as time saving and cost saving, over conducting physical experiments.…”

Factors affecting drilling incidents: Prediction of suck pipe by XGBoost model

“…For this purpose, standardization is a severe independent challenge. The main objective of the present study is to highlight the significant factors affecting nondestructive time [22][23][24][25][26], stuck risk [27] and fatigue optimization [28] reported during well construction.…”