Real-Time Drillstring Vibration Characterization Using Machine Learning

Millan, Ekaterina; Ringer, Maurice; Boualleg, Riadh; Li, Denis

doi:10.2118/194061-ms

Cited by 24 publications

(13 citation statements)

References 16 publications

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“…Torsional action causes fatigue in the bottom hole assembly connections and might damage bits. This vibration is usually expected in conditions such as deep wells and high-inclination long lateral wells 4 .…”

Section: Introductionmentioning

confidence: 86%

“…The downhole drill string vibration is a critical issue during the drilling operation and many drilling problems can be associated with the extraordinary level of vibrations as wear and tear for the drill bit and drillstring, inefficient performance for the drilling operation, power and cost losses, drilled hole instability issues 1 , 2 , besides equipment failure due to existing stresses and deformation 3 . Consequently, close monitoring of the downhole drillstring vibration will help to make the right decision for the drilling operation to mitigate all of the consequent problems 4 .…”

Section: Introductionmentioning

confidence: 99%

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Detecting downhole vibrations through drilling horizontal sections: machine learning study

Saadeldin

Gamal

Elkatatny

2023

Sci Rep

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During the drilling operations and because of the harsh downhole drilling environment, the drill string suffered from downhole vibrations that affect the drilling operation and equipment. This problem is greatly affecting the downhole tools (wear and tear), hole problems (wash-out), mechanical energy loss, and ineffective drilling performance. Extra non-productive time to address these complications during the operation, and hence, extra cost. Detecting the drillstring vibrations during drilling through the downhole sensors is costly due to the extra service and downhole sensors. Currently, the new-technology-based solutions are providing huge capabilities to deal intelligently with the data, and machine learning applications provide high computational competencies to learn and correlate the parameters for technical complex problems. This research presented a successful case study for developing machine learning models through a comprehensive methodology process for vibration detection using surface rig data through data collection, preprocessing, analytics, training and optimizing the models’ parameters, and evaluating the performance to have the best prediction results. Evaluating the models’ performance showed that obtained predictions have a great match with actual measurements for the different stages of training, testing, and even during models’ validation with unseen well data. Real-field horizontal drilling data was utilized to feed and train the models through different tools named radial basis function (RBF), support vector machines (SVMs), adaptive neuro-fuzzy inference system (ANFIS), and functional networks (FN) to auto-detect the three types of downhole vibrations (axial, torsional, and lateral). The study results showed a high correlation coefficient (higher than 0.9) and technically accepted average absolute percentage error (below 7.5%) between actual readings and predictions of the developed ML models. The study outcomes will add to the automation process of drilling operations to avoid many tools failure by comparing predicted vibrations versus downhole tools limits such as red zone and continuing drilling without interruption to the well total depth especially while drilling horizontal sections.

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Section: Introductionmentioning

confidence: 86%

Section: Introductionmentioning

confidence: 99%

Detecting downhole vibrations through drilling horizontal sections: machine learning study

Saadeldin

Gamal

Elkatatny

2023

Sci Rep

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“…1,2 Therefore, the drilling crew should provide more attention to the drillstring vibration indications while drilling to be able to mitigate the associated complications. 3 The drilling practices while the drillstring and bit penetrate the rock downhole by the action of the mechanical power system for rotating the drilling system lead to some kind of disturbance and unplanned downhole string vibration levels. The drillstring vibration has three modes that are named lateral, axial, and torsional modes of vibrations based on the resultant forces applied to the drillstring in terms of bottom hole assembly components (BHA).…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section

Saadeldin,

Gamal,

Elkatatny

2023

ACS Omega

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The downhole vibration is one of the most crucial factors that affect downhole equipment performance and failure, besides wellbore instability. Downhole tool failure, hole problems, mechanical energy loss, and ineffective drilling performance are commonly associated with drillstring high vibration levels. The high vibration level will lead to more complications while drilling that might cause nonproductive time and extra cost. Meanwhile, the downhole sensors for detecting the drillstring vibrations add more cost to the operation. Consequently, the new solutions based on technology capabilities provide a powerful tool to integrate and interpret the drilling data for the best use of @@the data for operation performance enhancement. This study provides a successful application for utilizing the surface drilling data to automate drillstring vibration detection during the drilling curve section employing machine learning (ML) techniques. The axial, torsional, and lateral vibration modes are detected through testing four ML techniques named the @@adaptive neuro-fuzzy inference system (ANFIS), radial basis function (RBF), functional networks (FN), and support vector machines (SVMs) with real field data. The models’ development was achieved by comprehensive study starting from data gathering, wrangling, statistical analysis, developing the ML models, evaluating the model prediction accuracy, and reporting the high accuracy results. The developed models were evaluated, and results showed that ANFIS and SVM models provided the highest accuracy with a coefficient of correlation (R) ranging from 0.9 to 0.99 followed by the RBF and FN models through model training and testing (R ranging from 0.82 to 0.96). Validating the models over unseen data confirmed the high accuracy prediction for the three vibration modes. Generally, the developed models provided technically accepted accuracy with R higher than 0.93 and AAPE less than 2.8% for SVM and ANFIS models while FN and RBF showed R between 0.82 and 0.95 and AAPE less than 5.7% between actual readings and predictions. Based on these results, the developed ML algorithm might be utilized as an intelligent solution to autodetect downhole vibration while drilling from surface sensor data only, which will save the downhole tool cost.

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“…Numbers of drill string vibration measurement instruments have been developed by researchers, and most of which use accelerometer, 7–9 but there are also a few other methods, such as using acoustic sensors 10 and surface measurement method. 11 However, the main measurement methods at present have the following two problems.…”

Section: Introductionmentioning

confidence: 99%

Research on the self-powered downhole vibration sensor based on triboelectric nanogenerator

Huang

Yang

et al. 2021

Proceedings of the Institution of Mechanical Engineers, Part C:

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Drill string vibration during the drilling must be measured in real-time as it will cause damage to the construction. This paper proposed a self-powered downhole vibration sensor based on the triboelectric nanogenerator. The downhole vibration sensor relies on the drill string vibration to induce triboelectric charge and electrostatic induction of nanomaterials, thereby realizing self-powered vibration measurement. Sensing performance test results show that the measurement range is between 0 to 5 Hz, the measurement error does not exceed 3.5%, and the output voltage amplitude with a range of 2 V to 5.5 V decreases with the increases of vibration frequency. Self-powered performance test results show that the output current can reach a maximum value of about 35 × E−8 A when a 50 ohm resistance is connected in series, the output power can reach a maximum value of about 924.5 × E−12 W when a 20k ohm resistance is connected in series at a vibration frequency of 0.8 Hz, and the output current and power all decrease with the increase of the vibration frequency.

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Real-Time Drillstring Vibration Characterization Using Machine Learning

Cited by 24 publications

References 16 publications

Detecting downhole vibrations through drilling horizontal sections: machine learning study

Detecting downhole vibrations through drilling horizontal sections: machine learning study

Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section

Research on the self-powered downhole vibration sensor based on triboelectric nanogenerator

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