Best Practices to Prevent Stuck Pipe Incidents in Offshore Shaly Formations

Aljubran, Mohammad; Al-Yami, Abdullah; Madan, M. A.

doi:10.2118/183717-ms

Cited by 8 publications

(20 citation statements)

References 14 publications

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“…The total training time to finish all the possible combination in the [19, 10, 10, 10, 1] network topology generally hovered around 5 to 6 h. Any attempt to increase the number of neurons in each layer would result in an exponential increase in the training time. The increment in training time can be explained by (1) the increased number of models to be trained. For example, a [19,11,11,11,1] network topology would have 1331 models to be trained as compared to 1000 models in a [19, 10, 10, 10, 1] network topology.…”

Section: Ann Model Buildingmentioning

confidence: 99%

“…The increment in training time can be explained by (1) the increased number of models to be trained. For example, a [19,11,11,11,1] network topology would have 1331 models to be trained as compared to 1000 models in a [19, 10, 10, 10, 1] network topology. This would result in 33% more models to be trained.…”

Section: Ann Model Buildingmentioning

confidence: 99%

“…On the other hand, model no. 580 has a network topology of [19,10,8,6,1]. The accuracy, sensitivity and specificity were 88.89%, 91.89% and 86.36%, respectively.…”

Section: Figurementioning

confidence: 99%

“…A stuck pipe can be defined as the inability to retrieve a drill string by means of pulling it out of the bore-hole even though the drill string does not lose its ability to move down, rotate and circulate partially or freely [1]. Other definitions of a stuck pipe include a pipe unable to be moved and where extra efforts such as jarring and spotting fluid placement have been started [2].…”

Section: Introductionmentioning

confidence: 99%

“…Stuck pipe mechanisms can be categorized into two major types in general: differential sticking and mechanical sticking. Mechanical sticking can be due to various issues such as pack-off and bridging as well as wellbore geometry [1]. These two mechanisms can be triggered by factors such as poor bore-hole cleaning, poor mud rheology, wellbore instability, improper drilling practices, the quality of the mud-cake and excessive reaming or back-reaming [8].…”

Section: Introductionmentioning

confidence: 99%

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Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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Stuck pipe incidents are one of the contributors to non-productive time (NPT), where they can result in a higher well cost. This research investigates the feasibility of applying machine learning to predict events of stuck pipes during drilling operations in petroleum fields. The predictive model aims to predict the occurrence of stuck pipes so that relevant drilling operation personnel are warned to enact a mitigation plan to prevent stuck pipes. Two machine learning methodologies were studied in this research, namely, the artificial neural network (ANN) and support vector machine (SVM). A total of 268 data sets were successfully collected through data extraction for the well drilling operation. The data also consist of the parameters with which the stuck pipes occurred during the drilling operations. These drilling parameters include information such as the properties of the drilling fluid, bottom-hole assembly (BHA) specification, state of the bore-hole and operating conditions. The R programming software was used to construct both the ANN and SVM machine learning models. The prediction performance of the machine learning models was evaluated in terms of accuracy, sensitivity and specificity. Sensitivity analysis was conducted on these two machine learning models. For the ANN, two activation functions—namely, the logistic activation function and hyperbolic tangent activation function—were tested. Additionally, all the possible combinations of network structures, from [19, 1, 1, 1, 1] to [19, 10, 10, 10, 1], were tested for each activation function. For the SVM, three kernel functions—namely, linear, Radial Basis Function (RBF) and polynomial—were tested. Apart from that, SVM hyper-parameters such as the regularization factor (C), sigma (σ) and degree (D) were used in sensitivity analysis as well. The results from the sensitivity analysis demonstrate that the best ANN model managed to achieve an 88.89% accuracy, 91.89% sensitivity and 86.36% specificity, whereas the best SVM model managed to achieve an 83.95% accuracy, 86.49% sensitivity and 81.82% specificity. Upon comparison, the ANN model is the better machine learning model in this study because its accuracy, sensitivity and specificity are consistently higher than those of the best SVM model. In conclusion, judging from the promising prediction accurateness as demonstrated in the results of this study, it is suggested that stuck pipe prediction using machine learning is indeed practical.

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Section: Ann Model Buildingmentioning

confidence: 99%

Section: Ann Model Buildingmentioning

confidence: 99%

“…On the other hand, model no. 580 has a network topology of [19,10,8,6,1]. The accuracy, sensitivity and specificity were 88.89%, 91.89% and 86.36%, respectively.…”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

et al. 2020

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Predicting and avoiding hazardous occurrences of stuck pipe for the petroleum wells at offshore Vietnam using machine learning techniques

Khanh

Hoang

Nguyen

et al. 2022

IOP Conf. Ser.: Earth Environ. Sci.

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Over many years, the petroleum industry has been facing the hazardous occurrences associated with drilling stuck pipe. It is actually one of the most challenging and costly problems at offshore drilling rigs, especially in the high-risk, high-cost environments as the ultra-deep or deep water. Nowadays, with the development of Artificial Intelligence these problems can be predicted and avoided by applying machine learning techniques. The purpose of study is to apply the two most powerful machine learning techniques including Artificial Neural Network (ANN) and Support Vector Machine (SVM) to predict and avoid stuck pipe occurrences for the petroleum wells at offshore Vietnam. Modeling ANN and SVM using Matlab programming language based on many actual drilling parameters to estimate the risk of stuck pipe. The output of the models of both ANN and SVM machine learning techniques is the probability of non-stuck or stuck pipe at different measured depth points. Predictive results from both ANN and SVM techniques are compared to real measurements with the accuracy of all models above 85%. These machine learning techniques can help predicting stuck pipe occurrences as well as avoiding them by considering the changes of some important drilling operational parameters.

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The Role of Drilling Azimuth on Wellbore Geomechanical Instability in a Major Offshore Field

Aljubran

Alsalman

Al-Yami

et al. 2017

Day 4 Thu, April 27, 2017

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Saudi Aramco workover re-entry operations within Khafji sand stringers are widespread across Zuluf field. These horizontal sidetracks maintain maximum reservoir contact and introduce substantial increases in oil production and ultimate recovery. Wellbore instability is a hot topic surrounding these operations due to the presence of geo- and hydro pressurized shale within the reservoir. The in-situ stress regime in this field is predominantly strike-slip faulting and the drilling azimuth represents a dominant stability component. The current study analyzes 58 laterals sidetracked across the Khafji formation across Zuluf field, and the effect of the drilling azimuth on wellbore stability. All laterals were cut across 7 in. casings and drilled as 6-1/8 in. open hole, with an average of 4,200 ft. in length. To accurately evaluate these data, the azimuth plane was divided into four angular regions, with respect to the maximum and minimum principal horizontal stresses: maximum stress (17 laterals), transitional maximum stress (12 laterals), transitional minimum stress (14 laterals) and minimum stress (15 laterals). Stuck pipe and severe tight-hole problems are mainly addressed for this purpose, as they are the main wellbore instability signs in this case. The descriptive correlational approach showed that nearly 63% of the total stuck pipe and lateral abandonment incidents occurring due to shale bedding and instability, were actually along the minimum horizontal stress. Furthermore, 53% of the laterals drilled along the minimum horizontal stress experienced wellbore shale instability with either stuck pipe or severe tight holes. Hence, the findings confirm the sensitivity of wellbore stability to the drilling azimuth in strike-slip regions. They also suggest a statistically significant correlation between laterals drilled along the minimum principal horizontal stress and wellbore instability problems across Khafji reservoir in Zuluf field. This paper will further highlight the descriptive correlational method, examine the observed wellbore instability problems, and compare the findings to previously conducted laboratory tests and geomechanical simulations.

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Best Practices to Prevent Stuck Pipe Incidents in Offshore Shaly Formations

Cited by 8 publications

References 14 publications

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Comparison of Machine Learning Classifiers for Accurate Prediction of Real-Time Stuck Pipe Incidents

Predicting and avoiding hazardous occurrences of stuck pipe for the petroleum wells at offshore Vietnam using machine learning techniques

The Role of Drilling Azimuth on Wellbore Geomechanical Instability in a Major Offshore Field

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