AccuPipePred: A Framework for the Accurate and Early Detection of Stuck Pipe for Real-Time Drilling Operations

Abstract

Thorough preplanning and best drilling practices are effective in reducing stuck pipe incidents, data analytics offer additional insight into further reducing the significant non-productive time (NTP) that results from this unplanned event. The severity of the stuck pipe problem may stop the drilling operations for a short time, or in more difficult cases, the drill string has to be cut and the borehole is sidetracked or plugged and abandoned. Consequently, detecting the early signs of this problem, in order to take the right actions, may considerably or entirely reduce the risk of a stuck pipe. Although computational models have been proposed for the early detection of the stuck pipe incidents, the models are derived from a reduced set of wells with stuck pipe incidents, which may result in under-trained models that predict a large number of false positive alarms. A sufficient amount of data or wells that statistically represent the parameters surrounding stuck pipe incidents under different circumstances is required in order to derive a generalizable and accurate prediction model. For this, we first derived a framework to automatically and systematically extract relevant data from the historical data. As such, our framework searches through the historical data and localizes the surface drilling and rheology parameters surrounding the stuck pipe incidents. Moreover, we performed feature selection by selecting the top-ranked parameters from the analysis of variance, which measures the capability of the drilling and rheology parameters to discriminate between stuck pipe incidents and normal drilling conditions, such as, weight on bit, revolutions per minute, among others. Using the relevant features selected by the analysis of variance, we derived a robust and fast classification model based on random forests that is able to accurately detect stuck pipe incidents. The implemented framework, which includes the automated data extraction module, the analysis of variance for feature selection, and prediction, is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew in order to minimize or avoid the NTP due to a stuck pipe.

Deep Learning and Time-Series Analysis for the Early Detection of Lost Circulation Incidents During Drilling Operations

“…In addition, wellbore collapse may occur due to reduced pressure in the annulus resulting from lost circulation, leading to drill pipe and downhole tools being buried in the hole [6]. Loss of annular pressure may result in stuck pipe incidents [7,8], which would substantially increase the complexity of mitigation operations and hence NPT. Globally, the cost of lost circulation NPT is estimated to be US$ 2-4 billion yearly [1].…”

“…Consequently, normalizing these data enables the models to focus on the trends rather than on the values. Magana-Mora et al demonstrated that the generalization capabilities of the model considerably improved after normalizing the drilling data [8]. Normalization is also known as the "tricks and trade" of developing ML/DL models as it expedites training where standardization limits oscillation on the ML/DL loss function before reaching an optimal point [40][41][42].…”

Well Control Space Out: A Deep-Learning Approach for the Optimization of Drilling Safety Operations

“…The three main causes of drilling non-productive-time (NPT) are 1) stuck pipe incidents, 2) mud circulation losses, and 3) well influx. Stuck pipe incidents occur when downhole force(s) prevent the movement of the drillstring [23][24][25]52]. Mud circulation losses occur when the drilling mud flows from the wellbore into the formations (due to natural or induced factors) [26,27,53,54].…”

Development of an In-Tank Tuning Fork Resonator for Automated Viscosity/Density Measurements of Drilling Fluids

“…In general, viscosity and density provide basic quality assurance/control (QA/QC) parameters indicative of the overall health of the drilling fluid. Variations in viscosity/density may reveal subtle compositional changes signaling underlying operational problems, such as mud contamination from reservoir fluid invasion, circulation losses, or stuck pipe due to differential sticking [2]. Therefore, proper monitoring and control of these mud properties is critical to ensure safe and efficient drilling operations.…”