Presented is a new methodology for selecting rotary drilling bits in an oil or gas well. Currently, bits are selected based on the performance of similar bits at offset wells. Parameters affecting a bit performance have a complex pattern. The relationship between formation properties, drilling fluid characteristics, bit design, and operational parameters in these patterns are not easily understood.

For a given field, studied were variables such as bit size, weight on bit, rotary speed, pump rate, drilled interval, and bit type. A three-layer artificial neural network was designed and trained with field data. This method incorporates computational intelligence to define the relationship between the variables. Further, it can be used to estimate other drilling parameters. The results indicate that the back propagation achitecture with two hidden slabs is the most effective neural network design for predicting the optimum bit type.

With the given data sets, this new model successfully predicted the bit types for several fields. For different data sets used in this study, the correlation coefficients for the predicted and field used bit types ranged between 0.857 and 0.975.

Keywords:
penetration, spe drilling conference, bit type, operational parameter, new approach, bit size, spe eastern regional meeting, bilgesu, aminian, mud circulation rate
Subjects:
Drill Bits, Information Management and Systems, Bit design, Neural networks
Copyright 2000, Society of Petroleum Engineers
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