stuck pipe causing events, downtime and nonproductive time

National oil company uses Machine Learning to steer through hazardous, high-dogleg intervals while running 9 5/8” casing

Predictive machine learning agents were used by the real-time operation center to make informed decisions and guide the rig crew through hazardous, high dogleg and high inclination intervals.

National Oil Company (NOC) experienced a stuck pipe while drilling the main wellbore of a production well, and as a consequence needed to drill a costly side-track to reach the target. The side-track required casing to be run through two high dogleg-severity (DLS) and high inclination intervals, adding further risk of stuck pipe and cost increases.

Case Studies for the Successful Deployment of Wells Augmented Stuck Pipe Indicator in Wells Real Time Centre

IPTC-21199-MS: Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Tengku Ezharuddin Tengku Bidin, Faazmiar Technology Sdn Bhd; Dalila Gomes, Exebenus

Abstracts: The restriction or inability of the drill string to reciprocate or rotate while in the borehole is commonly known as a stuck pipe. This event is typically accompanied by constraints in drilling fluid flow, except for differential sticking. The stuck pipe can manifest based on three different mechanisms, i.e. pack-off, differential sticking, and wellbore geometry. Despite its infrequent occurrence, non-productive time (NPT) events have a massive cost impact. Nevertheless, stuck pipe incidents can be evaded with proper identification of its unique symptoms which allows an early intervention and remediation action. Over the decades, multiple analytical studies have been attempted to predict stuck pipe occurrences. The latest venture into this drilling operational challenge now utilizes Machine Learning (ML) algorithms in forecasting stuck pipe risk.

Performance Improvement of Wells Augmented Stuck Pipe Indicator via Model Evaluations

IPTC:21455-MS: Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Tengku Ezharuddin Tengku Bidin, Faazmiar Technology Sdn Bhd; Dalila Gomes, Exebenus

Abstracts: The advancement of technology in this era has long profited the oil and gas industry by means of shrinking non-productive time (NPT) events and reducing drilling operational costs via real-time monitoring and intervention. Nevertheless, stuck pipe incidents have been a big concern and pain point for any drilling operations. Real-time monitoring with the aid of dynamic roadmaps of drilling parameters is useful in recognizing potential downhole issues but the initial stuck pipe symptoms are often minuscule in a short time frame hence it is a challenge to identify it in time. Wells Augmented Stuck Pipe Indicator (WASP) is a data-driven method leveraging historical drilling data and auxiliary engineering information to provide an impartial trend detection of impending stuck pipe incidents. WASP is a solution set to tackle the challenge. The solution is anchored on Machine Learning (ML) models which assess real-time drilling data and compute the risk of potential stuck pipe based on drilling activities, probable stuck pipe mechanisms, and operation time.

Leveraging Machine Learning Model For Real-Time Prediction of Differential Sticking Symptoms

ITPC-21221-MS: Meor M. Hakeem Meor Hashim, M. Hazwan Yusoff, M. Faris Arriffin, and Azlan Mohamad, PETRONAS Carigali Sdn Bhd; Dalila Gomes and Majo Jose, EXEBENUS; Tengku Ezharuddin Tengku Bidin, FAAZMIAR TechnologySdn Bhd

Abstracts: 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.