A New Approach to Predict Bit Life Based on Tooth or Bearing Failures

Bilgesu, H.I.; Altmis, U.; Ameri, S.; Mohaghegh, S.; Aminian, K.

doi:10.2523/51082-ms

Cited by 5 publications

(3 citation statements)

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“…According to Mohaghegh et al [15], AI is aimed at building a model or an algorithm, which enables machines to perform duties that need knowledge, understanding, and experience when performed by humans. A broader definition includes problem-solving, language perception, and conscious and unconscious processes [16]. AI is also known as a subfield of computer science involving the use of computers in tasks, which usually needs reasoning, knowledge, learning, and understanding abilities.…”

Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

et al. 2020

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Drilling a high-pressure, high-temperature (HPHT) well involves many difficulties and challenges. One of the greatest difficulties is the loss of circulation. Almost 40% of the drilling cost is attributed to the drilling fluid, so the loss of the fluid considerably increases the total drilling cost. There are several approaches to avoid loss of return; one of these approaches is preventing the occurrence of the losses by identifying the lost circulation zones. Most of these approaches are difficult to apply due to some constraints in the field. The purpose of this work is to apply three artificial intelligence (AI) techniques, namely, functional networks (FN), artificial neural networks (ANN), and fuzzy logic (FL), to identify the lost circulation zones. Real-time surface drilling parameters of three wells were obtained using real-time drilling sensors. Well A was utilized for training and testing the three developed AI models, whereas Well B and Well C were utilized to validate them. High accuracy was achieved by the three AI models based on the root mean square error (RMSE), confusion matrix, and correlation coefficient (R). All the AI models identified the lost circulation zones in Well A with high accuracy where the R is more than 0.98 and RMSE is less than 0.09. ANN is the most accurate model with R=0.99 and RMSE=0.05. An ANN was able to predict the lost circulation zones in the unseen Well B and Well C with R=0.946 and RMSE=0.165 and R=0.952 and RMSE=0.155, respectively.

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Section: Artificial Intelligence (Ai)mentioning

confidence: 99%

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

et al. 2020

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“…Input: lithology, torque, ROP, WOB, RPM. Output: bit wear, including bearing wear and tooth wear [7,8].…”

Section: Literature Reviewmentioning

confidence: 99%

Real Time Application of Bearing Wear Prediction Model Using Intelligent Drilling Advisory System

Tahmeen

Hareland

2011

Volume 7: Dynamic Systems and Control; Mechatronics and Intelligent Machines, Parts a and B

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The real-time prediction of bearing wear for roller cone bits using the Intelligent Drilling Advisory system (IDAs) may result in better performance in oil and gas drilling operations and reduce total drilling cost. IDAs is a real time engineering software and being developed for the oil and gas industry to enhance the performance of complex drilling processes providing meaningful analysis of drilling operational data. The prediction of bearing wear for roller cone bits is one of the most important engineering modules included into IDAs to analyze the drilling data in real time environment. The ‘Bearing Wear Prediction’ module in IDAs uses a newly developed wear model considering drilling parameters such as, weight on bit (WOB), revolution per minute (RPM), diameter of bit and hours drilled as a function of IADC (International Association of Drilling Contractors) bit bearing wear. The drilling engineers can evaluate bearing wear status including cumulative wear of roller cone bit in real time while drilling, using this intelligent system and make a decision on when to pull out the bit in time to avoid bearing failure. The wear prediction module, as well as the intelligent system has been successfully tested and verified with field data from different wells drilled in Western Canada. The estimated cumulative wears from the analysis match close with the corresponding field values.

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“…1,2 About 80% of roller cone bit failures are due to the failure of the bearing system, and early seal failures bring about 30% of bearing failures. 3,4 The development of the cone bit bearing seal has gone through a long process. In 1985, the metal seal appeared in the cone bit bearing seal system.…”

Section: Introductionmentioning

confidence: 99%

Optimization of cone bit bearing seal based on failure analysis

Huang

2018

Advances in Mechanical Engineering

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To improve the performance of the cone bit bearing seal, experimental study and simulation analysis of seal failure were carried out, and optimization of the seal was conducted. Results show that damages are located on the shoulder of the rubber rings due to high shear stress caused by large compression ratio, and serious wear appears on the sealing face of the metal rings because of unreasonable contact pressure distribution. Optimization was conducted by finite element method on three aspects: the dip angles (3°-8°) of the sealing face, the widths (1.3-3.9 mm) of the sealing face, and the back support structure (10°-15°). These three aspects were optimized one by one. Each aspect was optimized by different values, with the average compression ratio and the contact pressure distribution of the sealing face acting as evaluations. The optimal value of the previous optimization step performs as the static value in the next step, and finally, the optimized structure was selected from the last step. The performance of the optimized seal is significantly improved. The compression ratio of the optimized seal rubber drops to less than 14%. The wear mount of the new structure reduces to 20%-38% of the original structure.

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A New Approach to Predict Bit Life Based on Tooth or Bearing Failures

Cited by 5 publications

References 0 publications

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

Application of Artificial Intelligence Techniques in Predicting the Lost Circulation Zones Using Drilling Sensors

Real Time Application of Bearing Wear Prediction Model Using Intelligent Drilling Advisory System

Optimization of cone bit bearing seal based on failure analysis

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