MWD Vibration Measurements: A Time for Standardisation

Osnes, Svein Magne; Amundsen, Per Amund; Weltzin, Tore; Nyrnes, Erik; Grindhaug, Gaute

doi:10.2118/119877-ms

Cited by 17 publications

(4 citation statements)

References 5 publications

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“…Guidelines for "real time" vibrations mitigation have been developed for field use in the industry. However since the measurements and interpretation processes are dissimilar for different companies, there is still debate around the identification methods 6 . Vibrations control involves two strategies, prevention and mitigation.…”

Section: Vibrations -Modes Conventional Sources and Categorizationsmentioning

confidence: 99%

Little Things, Big Effect-Identifying Causes and Addressing Vibrations Issues in Challenging Deepwater Applications

et al. 2013

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As performance dysfunctions 1 , Vibrations 2 have drastic consequences on drilling operations. The challenges presented by vibrations get amplified on deepwater drilling programs. Vibrations cause downhole tool failures, thereby forcing multiple bottomhole assembly (BHA) trips. In some instances, connections become over-torqued and cannot be broken out on rigs. This situation creates logistical problems for service partners and operators alike (due to the requirement to have multiple back-ups for various tools), and with huge implications on project costs. Vibrations also create drilling inefficiencies with regards to borehole verticality and directional performance. Considering the total measured depths (TD) on deepwater projects, borehole verticality is usually a critical operational requirement. In other instances, the need to achieve specific wellbore trajectory requirements directionally based on project specifics also becomes critical. As a result, rotary steerable systems (RSS) are commonly used on these projects. However, and as documented extensively, vibrations compromise achievement of these goals even when RSS tools are used. Additionally, vibrations have negative effects on rate of penetration (ROP). This is why vibrations control continues to preoccupy the industry, particularly on deepwater projects. Considering the complexities associated with these projects and their much higher rig and spread rates, vibrations control is critical for project success.

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Section: Vibrations -Modes Conventional Sources and Categorizationsmentioning

confidence: 99%

Little Things, Big Effect-Identifying Causes and Addressing Vibrations Issues in Challenging Deepwater Applications

et al. 2013

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“…The axial vibration is represented by the vertical or longitudinal vibration mode and this might cause drill bit damage due to the bounce phenomena. The three vibration modes affect the drilling performance and equipment failure, however, the lateral mode has the least damaging impact 6 . The lateral type of downhole vibration characterizes the lateral motion from side to side and causes the eccentric rotation of the drillstring (whirl phenomena).…”

Section: Introductionmentioning

confidence: 99%

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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“…The bit bounce phenomenon is considered one of the damages that occurred due to the longitudinal or axial vibration mechanism during the drilling operation. The lateral drillstring vibration mode is one of the least destructive types among the other downhole vibration modes during drilling operations . The lateral vibration mode translates the lateral drillstring motion that affects the drillstring eccentric rotation, which is technically called the whirl phenomenon.…”

Section: Introductionmentioning

confidence: 99%

“…The lateral drillstring vibration mode is one of the least destructive types among the other downhole vibration modes during drilling operations. 5 The lateral vibration mode translates the lateral drillstring motion that affects the drillstring eccentric rotation, which is technically called the whirl phenomenon. The lateral vibration can reach a very high level of damaging impact when the BHA hits the surface of the drilled hole during the drilling, and sometimes, it might lead to backward whirl, which is one of the most damaging mechanisms that cause fatigue to the BHA connections, bit wear, over gauge drilling, and by the end, inefficient drilling performance.…”

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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MWD Vibration Measurements: A Time for Standardisation

Cited by 17 publications

References 5 publications

Little Things, Big Effect-Identifying Causes and Addressing Vibrations Issues in Challenging Deepwater Applications

Little Things, Big Effect-Identifying Causes and Addressing Vibrations Issues in Challenging Deepwater Applications

Detecting downhole vibrations through drilling horizontal sections: machine learning study

Machine Learning Solution for Predicting Vibrations while Drilling the Curve Section

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