Application of artificial neural networks to optimum bit selection

Optimum bit selection is one of the important issues in drilling engineering. Usually, optimum bit selection is determined by the lowest cost per foot and is a function of bit cost and performance as well as penetration rate. Conventional optimum rock bit selection program involves development of computer programs created from mathematical models along with information from previously drilled wells in the same area. Based on the data gathered on a daily basis for each well drilled, the optimum drilling program may be modified and revised as unexpected problems arose. The approach in this study uses the power of Artificial Neural Networks (ANN) and fractal geostatistics to solve the optimum bit selection problem. In order to achieve this goal a back-propagation ANN model was developed by training the model using real rock bit data for several wells in a carbonate field. The training and fine-tuning of the basic model involved use of both gamma ray and sonic log data. After that the model was tested using various drilling scenarios in different lithologic units. It was observed that the model provided satisfactory results.

[1]  Shahab D. Mohaghegh,et al.  A Methodological Approach for Reservoir Heterogeneity Characterization Using Artificial Neural Networks , 1994 .

[2]  F. Zausa,et al.  Rock Strength Measurements on Cuttings as Input Data for Optimizing Drill Bit Selection , 1999 .

[3]  Edit J. Kaminsky,et al.  Neural network classification of remote-sensing data , 1995 .

[4]  Cheng Dang Zhou,et al.  Determining Reservoir Properties in Reservoir Studies Using a Fuzzy Neural Network , 1993 .

[5]  A. O. Kumoluyi.  Higher-Order Neural Networks in Petroleum Engineering , 1994 .

[6]  Iraj Ershaghi,et al.  Complexities of Using Neural Network in Well Test Analysis of Faulted Reservoirs , 1993 .

[7]  P. M. Wong,et al.  Modeling Porosity Distribution in the A'nan Oilfield: Use of Geological Quantification, Neural Networks, and Geostatistics , 1999 .

[8]  E. N. Miranda,et al.  Neural Networks in Petroleum Engineering: a Case Study , 1996, Int. J. Neural Syst..

[9]  Simon Haykin,et al.  Neural Networks: A Comprehensive Foundation , 1998 .

[10]  David E. Goldberg,et al.  Genetic Algorithms in Search Optimization and Machine Learning , 1988 .

[11]  Alex van der Spek,et al.  Neural-Net Identification of Flow Regime With Band Spectra of Flow-Generated Sound , 1999 .

[12]  Kurt Hornik,et al.  Multilayer feedforward networks are universal approximators , 1989, Neural Networks.

[13]  Andrea Morelli,et al.  Neural networks and discrimination of seismic signals , 1995 .

[14]  J. K. Ali,et al.  Neural Networks: A New Tool for the Petroleum Industry? , 1994 .

[15]  J. D. Rogers,et al.  Artificial Neural Networks for Identification of Beam Pump Dynamometer Load Cards , 1990 .

[16]  Graham Mensa-Wilmot,et al.  Formation drillability-definition, quantification and contributions to bit performance evaluation , 1999 .

[17]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[18]  S. Akin,et al.  Genetic algorithm for estimating multiphase flow functions from unsteady-state displacement experiments , 1998 .

[19]  Peter V. Coveney,et al.  Brief: Predicting quality and performance of oilfield cements with artificial neural networks and FTIR spectroscopy , 1995 .

[20]  Clayton V. Deutsch,et al.  GSLIB: Geostatistical Software Library and User's Guide , 1993 .

[21]  Graham Mensa-Wilmot,et al.  Drilling Index - A New Approach to Bit Performance Evaluation , 1997 .

[22]  A. U. Al-Kaabi,et al.  Author's reply to discussion of using artificial neural nets to identify the well-test interpretation model , 1993 .

[23]  Hong Xu,et al.  A practical method for modeling bit performance using mud logging data , 1997 .

[24]  H. Rabia,et al.  Oilwell Drilling Engineering : Principles and Practice , 1986 .

[25]  Kenneth L. Mason.  Three-cone bit selection with sonic logs , 1987 .