Journal ArticleDOI
Application of Optimized Least Square Support Vector Machine and Genetic Programming for Accurate Estimation of Drilling Rate of Penetration
Meysam Naderi,Ehsan Khamehchi +1 more
- Vol. 7, Iss: 4, pp 92-108
TLDR
Two novel methods based on least square support vector machine (LSSVM) and genetic programming (GP) are presented and results indicate that LSSVM is superior over GP in terms of average relative error, average absolute relative errors, root mean square error, and the coefficient of determination.Abstract:
This article describes how the accurate estimation of the rate of penetration ROP is essential to minimize drilling costs. There are various factors influencing ROP such as formation rock, drilling...read more
Citations
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Journal ArticleDOI
Accurate artificial intelligence-based methods in predicting bottom-hole pressure in multiphase flow wells, a comparison approach
TL;DR: In this paper, the authors used genetic programming (GP), least square support vector machines (LSSVM), and radial basis function (RBF) neural networks to predict bottom-hole pressure in producing multiphase flow petroleum wells.
Journal ArticleDOI
Marine biological characteristics and environmental legal policy optimization based on heterogeneous computing environment
TL;DR: In this paper, a scheduling algorithm based on priority queue division is proposed to determine the number of priority queues according to number of input nodes of the directed DAG task set of the acyclic graph and divide the task queue into communication overhead and computational overhead.
References
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Book ChapterDOI
GPTIPS 2: an open-source software platform for symbolic data mining
TL;DR: GPTIPS as mentioned in this paper is a free, open source MATLAB based software platform for symbolic data mining, which uses a multigene variant of the biologically inspired machine learning method of genetic programming (MGGP) as the engine that drives the automatic model discovery process.
Journal ArticleDOI
Analysis of rate of penetration (ROP) prediction in drilling using physics-based and data-driven models
TL;DR: The authors have formulated a method to calculate the uncertainty (confidence interval) of ROP predictions, which can be useful in engineering based drilling decisions and provide a better fit than traditional models.
Journal ArticleDOI
Use of machine learning and data analytics to increase drilling efficiency for nearby wells
Chiranth Hegde,Kenneth E. Gray +1 more
TL;DR: In this article, a machine learning model is used to predict the rate of penetration (ROP) during drilling to a great accuracy as shown by Hegde, Wallace, and Gray (2015).
Journal ArticleDOI
Fuzzy logic-driven and SVM-driven hybrid computational intelligence models applied to oil and gas reservoir characterization
TL;DR: A successful application of the hybridization of three Artificial Intelligence techniques in one of the real-life problems encountered in oil and gas production where high quality information and accurate predictions are required for better and more efficient exploration, resource evaluation and their management.
Journal ArticleDOI
Efficient estimation of natural gas compressibility factor using a rigorous method
TL;DR: Results from present study show that implementation of LSSVM can lead to more accurate and reliable estimation of natural gas compressibility factor.