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Technical Paper: Artificial Neural Network Drilling Parameter Optimization System Improves ROP by Predicting/Managing Bit Wear

Society: SPE
Paper Number: 149801
Presentation Date: 2012
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Abstract

Tool manufacturers have made significant progress improving downhole drilling technologies, but little effort has focused on optimizing the drilling process. The set-it-and-forget-it approach and inherent inefficiencies of the automatic driller are inadequate for keeping bit parameters matched to lithology and wellbore conditions. The industry requires a new methodology to help rig-site personnel make informed drilling parameter decisions based on real-time offset data analysis that increases operating efficiency to reduce drilling costs.

To solve the problem an artificial neural network (ANN) drilling parameter optimization system was developed to provide rig-site personnel real-time information to ensure maximum run length from all bits and downhole tools at the highest possible ROP. Benefits of the new system include extended tool life, fewer trips, and the ability to manage bits' dull condition. 

The objective is to replace the human factor of applying operating parameters such as WOB and RPM with the intelligent ANN learned experience. Using the ANN software system, operating parameters can be selected based on the documented physical rock characteristics (offset log data) of formations being penetrated and then fine tuned for the bits specific cutting structure and wear rate. By following the real-time ANN recommendations, changes can be implemented to increase overall ROP while maximizing bit life by managing the dull condition.

The overall project results were positive and proved successful in all the trails carried out after this field trial. This paper will address the methodology of the new approach and highlight the importance of planning and implementing the drilling parameters in realtime.

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