Development of a new rate of penetration model using self-adaptive differential evolution-artificial neural network

  • Salaheldin ElkatatnyEmail author
Original Paper


The rate of penetration (ROP) is one of the key factors that affect the drilling costs. Optimizing the ROP is a big challenge as it depends on many factors such as revolutions per minute (RPM), weight on bit (WOB), torque (T), horsepower (HP), and uniaxial compressive strength (UCS) of the drilled rocks. In addition, drilling fluid properties have a major effect on ROP. The main goal of this study is to develop a new ROP model using an artificial neural network (ANN) combined with the self-adaptive differential evaluation (SaDE) technique. The model was built using different drilling mechanical parameters and drilling fluid properties. A new ROP empirical correlation was developed by extracting the weights and biases of the optimized SaDE-ANN model. The optimized ANN architecture based on SaDE is 5-30-1, where five input parameters were used in the input layers to predict the ROP which are drilling fluid density to plastic viscosity ratio, RPM, WOB/D, T/UCS, and HP. The optimized number of neurons was 30 and the output layer consists of one output parameter which is ROP. The data was divided into 60% training and 40% testing. The developed ROP model based on SaDE-ANN showed high accuracy where the correlation coefficient (R) was 0.98 and the average absolute percentage error (AAPE) was 5%. The new ROP empirical correlation outperformed the previous ROP models.


Self-adaptive Artificial neural network Drilling fluid properties Drilling parameters, rate of penetration 



rate of penetration, ft/h


uniaxial compressive strength, psi


mud density, pcf


plastic viscosity, cP


bit diameter, in.


weight on bit, klbf


torque, klbf-ft


standpipe pressure, psi


flow rate, gpm


revolutions per minute


horsepower, HP


correlation coefficient


coefficient of determination


average absolute percentage error


true vertical depth, ft


self-adaptive differential evolution


Compliance with ethical standards

Conflict of interest

The author declares that there is no conflict of interest.


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Copyright information

© Saudi Society for Geosciences 2019

Authors and Affiliations

  1. 1.Department of Petroleum EngineeringKing Fahd University of Petroleum & MineralsDhahranSaudi Arabia

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