Drilling Rate Optimization by Automatic Lithology Prediction Using Hybrid Machine Learning

It is essential to obtain valuable information during drilling from the formation that is being drilled for rate optimization. In the drilling operation, the process of lithology and formation determination is extremely obscurant and it seems machine learning, as a novel prediction method that can model complicated situations having a high degree of uncertainty, could be beneficial. In this work, the real-time drilling data was applied to predict the formation type and lithology while drilling that formation using a genetic algorithm and Taguchi design of experiment optimized artificial neural network. Drilling data of twelve wells in one of Iranian gas fields were applied for this work. 47500 sets of data were selected, and after data control, 31200 data sets were selected as valid data and imported to artificial neural networks. For performing this research, by changing the network features and optimizing the structure of the network using the Taguchi method and optimizing the weight and biases of the network using the genetic algorithm, a unique artificial neural network was designed. The results show that the developed hybrid machine learning method can predict formation and lithology with a high degree of accuracy.

[1]  Jeffrey L. Baldwin,et al.  Application Of A Neural Network To The Problem Of Mineral Identification From Well Logs , 1990 .

[2]  Morteza Ahmadi,et al.  Design of neural networks using genetic algorithm for the permeability estimation of the reservoir , 2007 .

[3]  H. B. Woods,et al.  Best Constant Weight and Rotary Speed for rotary Rock Bits , 1963 .

[4]  E. C. Onyia,et al.  Relationships Between Formation Strength, Drilling Strength, and Electric Log Properties , 1988 .

[5]  B. Yven,et al.  Multivariate and spatial statistical analysis of Callovo-Oxfordian physical properties from lab and borehole logs data: Towards a characterization of lateral and vertical spatial trends in the Meuse/Haute-Marne Transposition Zone , 2011 .

[6]  R. Lewis,et al.  In‐site nickel assay by prompt gamma neutron activation wireline logging , 1998 .

[7]  Williams.  Mapping Subsurface Alteration Using Gravity and Magnetic Inversion Models , 2007 .

[8]  Timothy R. Carr,et al.  Neural network prediction of carbonate lithofacies from well logs, Big Bow and Sand Arroyo Creek fields, Southwest Kansas , 2006, Comput. Geosci..

[9]  Patrick M. Wong,et al.  A CRITICAL COMPARISON OF NEURAL NETWORKS AND DISCRIMINANT ANALYSIS IN LITHOFACIES, POROSITY AND PERMEABILITY PREDICTIONS , 1995 .

[10]  G. Wadge,et al.  Inferring the lithology of borehole rocks by applying neural network classifiers to downhole logs: an example from the Ocean Drilling Program , 1999 .

[11]  F. S. Young,et al.  A Multiple Regression Approach to Optimal Drilling and Abnormal Pressure Detection , 1974 .

[12]  S. Gstalder,et al.  Measurement of Some Mechanical Properties of Rocks And Their Relationship to Rock Drillability , 1966 .

[13]  Keith K. Millheim,et al.  Applied drilling engineering. Volume 2 , 1986 .

[14]  Lakhmi C. Jain,et al.  Neural Network Training Using Genetic Algorithms , 1996 .

[15]  Ehsan Khamehchi,et al.  A robust model for computing pressure drop in vertical multiphase flow , 2015 .

[16]  Killeen.  Borehole Geophysics : Exploring the Third Dimension , 1997 .

[17]  S. Maiti,et al.  Sediment classification using neural networks: An example from the site-U1344A of IODP Expedition 323 in the Bering Sea , 2016 .

[18]  Jean Raynal,et al.  Organization of Field Tests and Evaluation Of Tricone Bit Performance Using Statistical Analysis and Sonic Logs , 1971 .

[19]  M. Zamora,et al.  Subsurface geophysics of the Phlegrean Fields: New insights from downhole measurements , 2003 .

[20]  Saumen Maiti,et al.  Neural network modelling and classification of lithofacies using well log data: A case study from KTB borehole site , 2007 .

[21]  A. J. Garnier,et al.  Phenomena Affecting Drilling Rates at Depth , 1959 .

[22]  J. Granek.  Computing geologically consistent models from geophysical data , 2011 .

[23]  Kevin Swingler,et al.  Applying neural networks - a practical guide , 1996 .

[24]  Sigve Hovda,et al.  Automatic lithology prediction from well logging using kernel density estimation , 2018 .

[25]  田口 玄一,et al.  System of experimental design : engineering methods to optimize quality and minimize costs , 1987 .

[26]  M. C. Sheppard,et al.  Separating Bit and Lithology Effects from Drilling Mechanics Data , 1988 .

[28]  Richard S. Smith,et al.  Clustering of downhole physical property measurements at the Victoria property, Sudbury for the purpose of extracting lithological information , 2015 .

[29]  Laibin Zhang,et al.  Predicting formation lithology from log data by using a neural network , 2008 .

[30]  Lakhmi C. Jain,et al.  Automatic Generation of Neural Network Architecture Using Evolutionary Computation , 1997, Advances in Fuzzy Systems - Applications and Theory.

[31]  Abouzar Mirzaei Paiaman,et al.  Effect of Drilling Fluid Properties on Rate of Penetration , 2009 .