A New Approach Solves Differential Sticking Problems in Geleki and Ahmedabad Fields

Krishan, Pal; Mangla, V.K.; Joshi, N.P.; Tewari, P.P.; Bose, U.N.

doi:10.2523/63055-ms

Cited by 3 publications

(1 citation statement)

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“…Conventionally, torque and drag analysis is an effective method to identify potential drilling problems by comparing the actual torque, hook load readings with the modeled ones (Abbas et al 2018; Adelung et al 1991; Bradley et al 1991;Fisk et al 1998;Iversen et al 2013;Krishan et al 2000). However, this method as well as other analytical models highly relies on the engineer's expertise and experience.…”

Section: Introductionmentioning

confidence: 99%

A Model for Early Detection of Stuck Pipe Using Random Forest Algorithm

Duan

Tian

Long

et al. 2023

Preprint

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Stuck pipe remains as one of the major risks in the drilling operation that could cause significant non-productive time (NPT). The earlier stuck pipe risk is predicted and mitigated, the higher the chance of preventing its occurrence in the first place. In this study, a new model was proposed using random forest (RF) algorithm together with sliding window technique to quickly detect the symptoms of potential stuck pipe, by capturing the hidden pattern and trends of certain real-time drilling parameters (hook load, Torque, standpipe pressure and weight on bit) fall outside of the boundaries calculated dynamically from the Pauta Criterion. After being trialed on wells for both offshore and unconventional, the developed model is able to give the early indication of an impending stuck pipe from 3 minutes to 43 minutes ahead of its actual occurrence, thus leave the rig crew valuable time window to take proper interventions.

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Section: Introductionmentioning

confidence: 99%

A Model for Early Detection of Stuck Pipe Using Random Forest Algorithm

Duan

Tian

Long

et al. 2023

Preprint

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Intelligent Prediction of Stuck Pipe Remediation Using Machine Learning Algorithms

Abbas

Flori

Almubarak

et al. 2019

SPE Annual Technical Conference and Exhibition

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Stuck pipe is still a major operational challenge that imposes a significant amount of downtime and associated costs to petroleum and gas exploration operations. The possibility of freeing stuck pipe depends on response time and subsequent surface action taken by the driller during and after the sticking is experienced. A late and improper reaction not only causes a loss of time in trying to release stuck pipe but also results in the loss of an important portion of expensive tubular, downhole equipment and tools. Therefore, a fast and effective response should be made to release the stuck pipe. Investigating previous successful responses that have solved stuck pipe issues makes it possible to predict and adopt the proper treatments. This paper presents a study on the application of machine learning methodologies to develop an expert system that can be used as a reference guide for the drilling engineer to make intelligent decisions and reduce the lost time for each stuck pipe event. Field datasets, including the drilling operation parameters, formation type, and fluid mud characteristics, were collected from 385 wells drilled in Southern Iraq from different fields. The new models were developed to predict the stuck pipe solution for vertical and deviated wells using artificial neural networks (ANNs) and a support vector machine (SVM). The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results being more promising. These machine learning methods offer insights that could improve response time and strategies for treating stuck pipe.

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Application of Machine Learning Approach for Intelligent Prediction of Pipe Sticking

Abbas

Almubarak

Abbas³

et al. 2019

Day 2 Tue, November 12, 2019

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Stuck pipe has been recognized as one of the serious problems in drilling operations that has a significant impact on drilling efficiency and well costs. The events related to the stuck pipe can be responsible for losses of hundreds of millions of dollars each year in the drilling industry. This paper presents a study on the application of machine learning methodologies to predict the stuck pipe occurrence which can be utilized to modify drilling variables to minimize the likelihood of sticking. The new models were developed to predict the stuck pipe incidence for vertical and deviated wells using artificial neural networks (ANNs) and a support vector machine (SVM). The proposed models were examined using a few examples of real stick pipe cases from the field. The results of the analysis have revealed that both ANNs and SVM approaches can be of great use, with the SVM results being more promising. The present analysis supplies knowledge that can be used during well pre-planning and developmental phases to make informed decisions that will avoid pipe sticking problems and essentially optimize drilling performance. The risk of pipe sticking can then be minimized and the costs associated with its occurrence will be reduced.

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A New Approach Solves Differential Sticking Problems in Geleki and Ahmedabad Fields

Cited by 3 publications

References 1 publication

A Model for Early Detection of Stuck Pipe Using Random Forest Algorithm

A Model for Early Detection of Stuck Pipe Using Random Forest Algorithm

Intelligent Prediction of Stuck Pipe Remediation Using Machine Learning Algorithms

Application of Machine Learning Approach for Intelligent Prediction of Pipe Sticking

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