Applications and theoretical perspectives of artificial intelligence in the rate of penetration
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TL;DR: The accuracy of AI algorithms are better than the empirical models thus, will improve the drilling efficiency, reduce cost and enhance the development of pad wells.
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About: This article is published in Petroleum. The article was published on 28 Aug 2020. and is currently open access. The article focuses on the topics: Computation & Artificial neural network.
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Citations
Half a century experience in rate of penetration management: Application of machine learning methods and optimization algorithms - A review
TL;DR: A review of machine learning methods for rate of penetration (ROP) management in drilling operations can be found in this paper, where some of the studies by using these methods as the main approach for ROP management are reviewed to achieve a better understanding of this concept, its economic benefits and also its research capacities.
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Anti-drilling ability of Ziliujing conglomerate formation in Western Sichuan Basin of China
TL;DR: In this paper , the physical, mechanical, and drillability characteristics were investigated for conglomerate rock that collected from the lower Jurassic Ziliujing formation in the Western Sichuan Basin of China.
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Half a century experience in rate of penetration management: Application of machine learning methods and optimization algorithms - A review
TL;DR: In this article , a review of machine learning methods and meta-heuristic algorithms for rate of penetration (ROP) management in drilling operations is presented, showing that simple and optimized artificial neural networks (ANNs), support vector machines (SVMs), fuzzy logic (FL) or adaptive neuro-fuzzy inference system (ANFIS) outperform modified ANNs in terms of prediction accuracy.
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Analysis of rate of penetration prediction in drilling using data-driven models based on weight on hook measurement
TL;DR: The methodology employed in this paper performed well in predicting ROP in the absence of some unavailable parameters such as UCS, indicating that using a deep artificial feed-forward network with logarithmic sigmoid transfer function as a predictor model provided the most accurate model.
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References
A comprehensive data mining approach to estimate the rate of penetration: Application of neural network, rule based models and feature ranking
TL;DR: A novel and reliable computational approach for prediction of ROP is proposed and fscaret package in R environment was implemented to find out the importance and ranking of the inputs parameters and Monotone Multi-Layer Perceptron model with 6 inputs showed a reliable accuracy.
71
Fault diagnosis of downhole drilling incidents using adaptive observers and statistical change detection
TL;DR: It is shown how estimated friction parameters and ow rates can be used to detect and isolate the type of incident, as well as isolating the position of a defect, in downhole abnormal incidents.
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Application of Real-Time Field Data to Optimize Drilling Hydraulics Using Neural Network Approach
Yanfang Wang,Saeed Salehi +1 more
TL;DR: In this article, an artificial neural network (ANN) model to predict hydraulics was implemented through the fitting tool of matlab and the sensitivity analysis of input parameters on the created model was investigated by using forward regression method.
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[INVITED] Computational intelligence for smart laser materials processing
TL;DR: This work is a review of the state-of-the-art on the methodology and applications of CI in laser materials processing (LMP), which is nowadays receiving increasing interest from world class manufacturers and 4.0 industry.
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Fuzzy expert system for solving lost circulation problem
Leonid Sheremetov,Ildar Z. Batyrshin,Denis M. Filatov,Jorge Martinez,Hector Rodriguez +4 more
- 01 Jan 2008
TL;DR: This paper describes a distributed hybrid intelligent system, called SmartDrill, using fuzzy logic, expert system framework and Web services for helping petroleum engineers to diagnose and solve lost circulation problems.
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