Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network
作者: Mohammad AnemangelyAhmad RamezanzadehBehzad TokhmechiAbdollah MolaghabAram Mohammadian
作者单位: 1School of Mining, Petroleum and Geophysics engineering, Shahrood University of Technology, Shahrood, Iran
2National Iranian South Oil Company, Ahvaz, Iran
刊名: Journal of Geophysics and Engineering, 2018, Vol.15 (4)
来源数据库: Institute of Physics Journal
DOI: 10.1088/1742-2140/aaac5d
原始语种摘要: Rate of penetration (ROP) enhancement serves as a key factor in reducing drilling time and hence drilling costs. ROP enhancement requires identification of the parameters affecting this rate. However, the large number of effective parameters, which are further immersed in noise, makes it difficult to present a highly accurate and comprehensive model. In the present research, in order to predict the drilling ROP in one of the vertical wells drilled into the Karanj Oilfield, a hybrid model composed of a multilayer perceptron (MLP) neural network together with either a particle swarm optimization (PSO) algorithm or a cuckoo optimization algorithm (COA) was used. For this purpose, first petrophysical logs and drilling data were denoised using the Savitzky–Golay filter. Then, the ‘plus- l...
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  • perceptron 感知器感知机
  • neural 神经系统的
  • model 模型
  • parameters 参数
  • number 号码
  • drilling 钻进
  • information 报告
  • stipulated 合同规定的
  • error 误差
  • purpose 目的