Day 2 Wed, March 08, 2023最新文献

{"title":"Medstraum: Design and Operation of the First Zero-Emission Fast Catamaran","authors":"A. Papanikolaou, Mikal Dahle, E. Tolo, Y. Xing-Kaeding, Andreas Prinz, F. Jenset, E. Boulougouris, A. Kanellopoulou, G. Zaraphonitis, C. Jürgenhake, T. Seidenberg","doi":"10.5957/some-2023-005","DOIUrl":"https://doi.org/10.5957/some-2023-005","url":null,"abstract":"The paper deals with the design, construction and the early operation of the worldwide 1 st battery driven high-speed catamaran passenger ferry MS Medstraum. The paper elaborates on unique issues of the design process, on the superior hydrodynamic performance, on the modular construction of vessel and on the land-based electrical/charging installation. MS Medstraum was built by Fjellstrand AS and was launched in early June 2022. After successful sea trials that superseded the expectations of designers, builders and operators, achieving a maximum speed of over 27 knots, it started operations in the Stavanger/Norway area in late September 2022. The prototype character of MS Medstraum led to its selection as “Ship of the Year 2022” at the major international maritime exhibition SMM 2022 (September 2022, Hamburg). The presented research is in the frame of the H2020 funded project “TrAM – Transport: Advanced and Modular” (www.tramproject.eu).","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131243588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Determination of Drill-string Geometry for Numerical Drilling Models","authors":"B. Daireaux, L. Carlsen, E. Dvergsnes, Maria Johansen, M. Balov","doi":"10.2118/212457-ms","DOIUrl":"https://doi.org/10.2118/212457-ms","url":null,"abstract":"\u0000 \u0000 \u0000 Modern drilling automation and monitoring techniques rely heavily on the ability to predict the response of the wellbore to the drilling process. Numerical models are often used to reproduce the well’s behavior accurately and allow precise analyses of the main drilling measurements such as Stand-Pipe pressure, Hookload or Surface torque. Unfortunately, those models require a detailed configuration: drill-string characteristics, well geometry, mud properties all play a central role in the computations. In practice, the configuration process is challenging: the information may be difficult to find, be available late in the drilling process or even be erroneous. The objective of the present work is to develop statistical techniques to automatically determine the drill-string geometry from available sensor values, such that hydraulic and torque-drag models can achieve at least the same level of accuracy as if the configuration was manually entered by an operator.\u0000 \u0000 \u0000 \u0000 For an automated drill-string configuration to be of any use, it must be made available to the automation system early enough during the operations: ideally, reliable estimates should be provided during run-in-hole, and further refinements generated when circulation and rotation are first established, prior to the drilling phase. Those requirements impose to design flexible solutions that make optimum use of the few available measurements.\u0000 We based our system on ensemble techniques: many real-time simulations are run in parallel to continuously maintain an estimate of the distribution of the parameters that characterize the drill-string geometry. We describe the statistical techniques developed to perform the data assimilation, as well as the necessary modifications one must apply to standard numerical models to reach a sufficient number of parallel simulations.\u0000 Finally, we discuss the use of such a system in real conditions: since the drill-string geometry is a critical element of automation systems, special care must be taken when automating this part of the work procedures. We describe the different mechanisms that can be used to validate the results of the system during drilling operations.\u0000 \u0000 \u0000 \u0000 The system was intensively tested with offline data. We present and discuss the results of those tests. Of particular interest is the stability of the generated configuration as operations proceed, the quality of the configuration with respect to the predictive capabilities of the associated hydraulic and torque-drag models, and the subsequent impact on the automation system under consideration.\u0000 \u0000 \u0000 \u0000 The solution described in this contribution aims at automating parts of the configuration process associated to the use of numerical models for drilling automation systems. As per today, no such solution exists, and this process represent an important part of the manual work needed to properly run digital solutions. We therefore consider that such a system can facilitate the deploy","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132335437","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Transformation of the Rig Management System: A Systematic Approach to the Application of Software to Offshore Rigs","authors":"Gabriella Thomas, Åsmund Hjulstad, E. Wersland, S. Birkeland, A. I. Røsbak, Son Minh Tran, Morten Lien, Jo Svenningsson Nordstrand, Joseph Oghenezino Ojero, Felix Odebrett","doi":"10.2118/212563-ms","DOIUrl":"https://doi.org/10.2118/212563-ms","url":null,"abstract":"\u0000 Digitalization of the rig management system (RMS) into a single digital platform enables a safer, transparent, and more efficient operation whilst disrupting the long-established bureaucratic workflows. Equinor has approached this with user experience (UX) at the core, in close collaboration with the rig contractor and software providers. Several platforms were trialled with a ‘fail fast, learn faster’ strategy. This honed approach for implementation has successfully resulted in improved software usability and a cultural transformation.\u0000 Typically, the rig management system comprises of independent components that require significant human input to co-ordinate. This is often managed on multiple systems (some paper based) with limited transparency and a timely learning cycle. The traditional rig management system incorporates several key items such as:\u0000 The Activity Planner is a lookahead for operational timings and the basis for logistic decisions. The Operation Procedures are the instructions created by an onshore engineering team and executed offshore. The Daily Reports comprises of an operational breakdown of the last twenty-four hours. After Action Reviews are a post operation analysis of lessons for future improvements. The software systems trialled had each item of the rig management system analysed in detailed and adapted with an agile methodology.\u0000 The digital rig management system creates the environment where these components communicate with one another, resulting in immediate benefits for instance:\u0000 The standardised Digital instruction format has the user's interface (UI) in mind, highlighting only meaningful information for that operational step e.g., diagrams, videos, documents, and risks. Greater transparency for compliance audits, for example both onshore & offshore personnel can be verified when the procedures and checklists are signed off (with a timestamp) and made active. The experiences and improvements can be documented on each step which allows a live feedback loop to the engineering team. Reduced time spent on administrative tasks, such as following up on wet signatures for paper document and then uploading it to a digitised format, all of this is now instantaneous. Resulting in an observable reduction in e-mails, phone calls and printed pages. The Digital RMS has a seamless management of change function, it isolates the old revision whilst uploading the new and approved instructions. The users will also receive a notification once the change has been approved coupled with an updated digital change log.\u0000 This publication outlines the tried and tested approach for selecting and implementing Rig Management System software resulting in quantitative and qualitative benefits. It will further discuss the forthcoming strategy on scaling the application to a rig fleet and its potential interfaces with the Automated drilling control system.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132822689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fault Classification and Diagnosis for Rotating Equipment using Machine Learning Algorithms","authors":"D. Emiris","doi":"10.5957/some-2023-025","DOIUrl":"https://doi.org/10.5957/some-2023-025","url":null,"abstract":"Vessels typically house large sets of different, complex types of equipment; functional failures in them lead to operational stoppage or downgrade with impacts on performance, quality and/or cost. Preventive maintenance schedules are commonly employed, the optimization of which relates to the need of maintenance, the specific component where a problem is detected, the identified fault type, the severity, the expected remaining life within acceptable performance (confidence) limits, etc. Recent advances in sensors and in Machine Learning (ML) methods, have boosted both the fault diagnosis and prognosis, thus incenting companies to invest on the development of efficient Predictive Maintenance (PdM). In this work, we explore the PdM problem for a family of equipment, namely, compressors, through the application of ML techniques on large datasets obtained from on-board sensors. We first deal with the problem of identifying the most useful features in the frequency and time domains, that enable efficient classification and we demonstrate results on data pre-processing and feature extraction. We apply two different clustering and classification algorithms, namely, k-Nearest Neighbor (KNN) Support Vector Machines (SVM) on big datasets obtained from laboratory and industrial setups. We demonstrate that early failure prediction and fault classification is feasible and provides ample opportunities for the development of PdM tactics that reduce cost and minimize risk. Finally, we comment on the appropriateness of features and evaluate the classification accuracy for simple fault cases.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"74 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125763028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Short-Term Optimal Ship Routing via Reliable Satellite Current Data","authors":"Artemis Ioannou, Evangelos Moschos, B. Le Vu, A. Stegner","doi":"10.5957/some-2023-044","DOIUrl":"https://doi.org/10.5957/some-2023-044","url":null,"abstract":"Optimal ship routing systems require highly accurate oceanic data. Our technological innovation is based on the use of high-resolution currents derived from the fusion of various satellite observations by harnessing Artificial Intelligence methods. Today, routing strategies rely mainly on the outputs of operational oceanic models that cannot always guarantee the accurate prediction of surface currents. In this study we compare our HIRES currents data stemming from satellites, with commonly used operational oceanic models, reducing errors by more than a factor of two, both for a nowcast and short forecast scenarios. We explore a specific optimization example along a highly commercial shipping road in the eastern Mediterranean Sea, demonstrating the advantage of our method. We show that high reliability on the observed oceanic conditions allows for a short-term oceanic routing that can significantly optimize the ship’s voyage time as well as the ship’s fuel consumption. This low-cost/low-risk solution can be employed today to advance shipping decarbonization.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130361628","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving the Accuracy of a Kick-detection System by Reducing Effects of Rig Operational Practices","authors":"Deep R. Joshi, S. S. Yalamarty, C. Cheatham, M. Kamyab, Kelly Winklmann, Trish Ross, P. McCormack","doi":"10.2118/212452-ms","DOIUrl":"https://doi.org/10.2118/212452-ms","url":null,"abstract":"\u0000 Novel methods are presented that update a real-time cloud-based kick-detection system introduced in SPE-208770-MS to handle false kick identifications caused by rig operations. A common weakness in kick-detection systems is false indications of kicks due to rig operations and drilling practices that cause changes in tank volumes. In this work, we will discuss the modifications made to the existing real-time kick-detection system to handle rig operational practices and reduce false positives.\u0000 The existing kick-detection system analyzes the trends in the drilling data such as tank volumes, flow rates, and pump rates to detect well control events. Extensive field use of this system showed that the rig operations such as transfers between tanks, tank swaps, and adding material to the active tanks have a severe impact on the false-positive rates. Two approaches were developed to handle such operational practices: - Transfer identification: identify transfers between monitored tanks - Comment watcher: Evaluate the rig-memos to check if they might identify an operation that explains the variation in the tank volumes.\u0000 These approaches were tested with historical wells and live wells. Transfers were identified in several historical wells with help from the operator subject matter experts (SMEs). Thresholds such as the rate of transfer and the window size were tuned to optimally identify transfers. The tuned algorithms correctly identified transfers between monitored tanks with more than 85% accuracy. This workflow was added to the existing kick-detection framework. The efficiency of the kick detection logic depends on dynamically adjusting various thresholds. If any transfers were identified, the thresholds were reset which helped further reduce the false positives by 20% - 25%. For the comment watcher, a keyword library was developed with help from the operator SMEs. This library contained a list of keywords that the rig crew frequently uses in the rig memos to describe the operations. Each keyword from the library was mapped to an alarm type to be suppressed. A workflow was implemented to identify if a rig memo contains a keyword and suppress the respective alarm. The comment watcher feature was then implemented on historical wells along with the transfer identification. These updates resulted in a 40% reduction in false positives while maintaining a 100% true positive identification rate.\u0000 This work improves the accuracy and efficacy of a previously presented (SPE 208770) real-time cloud- based kick-identification system by detecting and avoiding the impact of rig operations. Features such as transfer identification and comment watcher are added to determine if the changes in the tank volumes can be attributed to rig operations. This update was tested on historical wells and live wells. Working together, these features helped reduce the false positives by up to 40%.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114876884","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Drill String Failure Prediction Methodology Using Data Analytics for Real Time Well Engineering","authors":"B. Thakur, Vishrut Chokshi, Kushan Patel, Robello Samuel","doi":"10.2118/212456-ms","DOIUrl":"https://doi.org/10.2118/212456-ms","url":null,"abstract":"\u0000 Vibration signals in the form of real-time accelerations recorded downhole often contain strong noise, making it difficult for fault or failure diagnosis during drillstring. Vibration signals include noise from sources, such as motors, bit and drillstring interactions with the borehole, rugged boreholes, and similar interactions. Sometimes, this noise is stronger than the underlying signal, which might lead to false alarms or misrecognition. This paper discusses a novel approach to diagnose failure in real-time, which is also robust to noise.\u0000 Existing methods for fault or failure diagnosis are based on threshold values of peak and average vibrational signals. This paper introduces a hybrid method of combining signal demodulation with spectral analysis to predict drillstring failure. This method deconvolutes the signal with the help of minimum entropy deconvolution (MED) and Teager-Kaiser energy operator (TKEO) to remove ambiguity as a result of noise. Then, the signal is decomposed into various intrinsic mode functions (IMFs) that have the highest correlation with the original signal and can be used for failure diagnosis.\u0000 This paper also discusses how spectral analysis can be applied on selected IMFs by comparing the IMF’s impact frequency with the system’s natural frequency so its harmonic drillstring failure can be diagnosed more precisely.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124678665","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Numerical analysis of the impact of the bow thruster opening on a fast catamaran’s resistance","authors":"S. Duman, E. Boulougouris, Myo Zin Aung, Xue Xu, A. Nazemian","doi":"10.5957/some-2023-034","DOIUrl":"https://doi.org/10.5957/some-2023-034","url":null,"abstract":"Steering a ship in confined waters is particularly important for the efficient operation of high-speed ferries. Bow thruster is the obvious solution. It may have however an adverse effect on the resistance of the vessel. The interference between demi hulls in the case of catamarans becomes an important and complex phenomenon as it affects many aspects of the hydrodynamic performance of the vessel. This study has focused on the analysis of the fluid flow around a fast catamaran with/without a bow thruster tunnel. A zero-carbon fast passenger ferry catamaran hull has been subjected to resistance simulations at two different speeds. Firstly, the catamaran hull without a bow opening has been simulated by commercial RANS solver software. After that, the simulations have been repeated for the catamaran hull with bow thruster opening. Finally, the bow opening has been filled with a gridded plane for garbage straining. The findings are very interesting for the fast catamaran ferry designers and operators. They are discussed for all three conditions in a comparative approach.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130527041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic Classification of PDC Cutter Damage Using a Single Deep Learning Neural Network Model","authors":"Abdulbaset Ali, Harnoor Singh, Daniel Kelly, Donald G. Hender, Alan Clarke, Mohammad Mahdi Ghiasi, Ronald Haynes, Lesley James","doi":"10.2118/212503-ms","DOIUrl":"https://doi.org/10.2118/212503-ms","url":null,"abstract":"\u0000 There is considerable value in automatically quantifying cutter damage from drill bit pictures. Current approaches do not classify cutter damage by type, i.e., broken, chipped, lost, etc. We, therefore, present a computer vision model using deep learning neural networks to automate multi-type damage detection in Polycrystalline Diamond Compact (PDC) drill bit cutters.\u0000 The automated bit damage detection approach presented in this paper is based on training a computer vision model on different cutter damage types aimed at detecting and classifying damaged cutters directly. Prior approaches detected cutters first and then classified the damage type for the detected cutters. The You Only Look Once version 5 (YOLOv5) algorithm was selected based on the findings of an earlier published study. Different models of YOLOv5 were trained with different architecture sizes with various optimizers using two-dimensional (2D) drill bit images provided by the SPE Drilling Uncertainty Prediction technical section (DUPTS) and labeled by the authors with training from industry subject matter experts. To achieve the modeling goal, the images were first annotated and labeled to create training, validation, and testing sub-datasets. Then, by changing brightness and color, the images allocated for the training phase were augmented to generate more samples for the model development. The categories defined for labeling the DUPTS dataset were bond failure, broken cutter, chipped cutter, lost cutter, worn cutter, green cutter, green gauge, core out, junk damage, ring out, and top view. These categories can be updated once the IADC upgrade committee finishes upgrading IADC dull bit grading cones.\u0000 Trained models were validated using the validation dataset of 2D images. It showed that the large YOLOv5 with stochastic gradient descent (SGD) optimizer achieved the highest metrics with a short training cycle compared to the Adam optimizer. In addition, the model was tested using an unseen data set collected from the local office of a drill bit supplier. Testing results illustrated a high level of performance. However, it was observed that inconsistency and quality of rig site drill bit photos reduce model accuracy. Therefore, it is suggested that companies produce large sets of quality images for developing better models. This study successfully demonstrates the integration of computer vision and machine learning for drill bit grading by categorizing/classifying damaged cutters by type directly in one stage rather than detecting the cutters first and then classifying them in a second stage. To guarantee the deployed model's robustness and consistency the model deployment has been tested in different environments that include cloud platform, container on a local machine, and cloud platform as a service (PaaS) with an online web app. In addition, the model can detect ring out and cored damages from the top view drill bit images, and to the best of the authors’ knowledge, this ha","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127128660","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Field Test Results for Real-time ROP Optimization Using Machine Learning and Downhole Vibration Monitoring - A Case Study","authors":"Ryan Robertson, Aidan Deans, Kriti Singh, Daniel Braga, Mohammedreza Kamyab, C. Cheatham, Tatiana Borges","doi":"10.2118/212568-ms","DOIUrl":"https://doi.org/10.2118/212568-ms","url":null,"abstract":"\u0000 A case study for a real-time field test of a machine learning (ML) ROP prediction and optimization algorithm and a vendor-neutral vibration monitoring system is presented for ten wells in Northeastern British Columbia, Canada. A novel auto-calibration feature adjusts the ML model in real-time to account for prediction bias. The paper is of interest to operators and service companies seeking to accelerate uptake of Artificial Intelligence (AI) by rig personnel.\u0000 A ten-well campaign in three target formations was drilled from one rig in the lateral sections to test the ML ROP prediction/optimization system. During the first two laterals, the operator office engineers visited the rig to train the rig team and gain their buy-in. For the remaining wells, tests were run in real-time advisory mode. Field test objectives were to develop trust in the ML model, validate real-time vibration monitoring tool with real-time downhole vibration data, and obtain feedback from the rig and office on functionality to accelerate uptake of AI.\u0000 Initially, the ML ROP model passed all success criteria in two of three formations, or four of six wells. One formation failed the ROP accuracy criterion because the predicted ROP was consistently too high, but the ML model accurately captured the variance. This led to the development of a novel automated calibration procedure that adjusts the \"bias\" of the machine learning ROP prediction in a manner like calibrating physics-based models (such as torque and drag hookload) using a calibration factor calculated in real-time by comparing predictions with actual ROP values. This enhancement enables meeting accuracy acceptance criteria and has opened the door for a broader application of the method in other formations and basins. To date the model has been successfully deployed for the lateral section and for bottomhole assemblies (BHAs) with a positive displacement motor.\u0000 The vibration monitoring system successfully provided real-time data to the operator at the rig and office that is generally available only to the MWD provider in real time, which enables the operator to gain new insights for situational awareness and decision making.\u0000 Feedback from rig personnel was very valuable and included new functionality, such as their ability to change parameter limits in the ROP system, which has been implemented and successfully used by the operator.","PeriodicalId":103776,"journal":{"name":"Day 2 Wed, March 08, 2023","volume":"810 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117052886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}