Earth Science Informatics

, Volume 12, Issue 3, pp 319–339 | Cite as

A machine learning approach to predict drilling rate using petrophysical and mud logging data

  • Mohammad SabahEmail author
  • Mohsen Talebkeikhah
  • David A. Wood
  • Rasool Khosravanian
  • Mohammad Anemangely
  • Alireza Younesi
Research Article


Predicting the drilling rate of penetration (ROP) is one approach to optimizing drilling performance. However, as ROP behavior is unique to specific geological conditions its application is not straightforward. Moreover, ROP is typically affected by various operational factors (e.g. bit type, weight-on-bit, rotation rate, etc.) as well as the geological characteristics of the rocks being penetrated. This makes ROP prediction an intricate and multi-faceted problem. Here we compare data mining methods with several machine learning algorithms to evaluate their accuracy and effectiveness in predicting ROP. The algorithms considered are: artificial neural networks (ANN) applying a multi-layer perceptron (MLP); ANN applying a radial basis function (RBF); support vector regression (SVR), and an hybrid MLP trained using a particle swarm optimization algorithm (MLP-PSO). Data preparation prior to executing the algorithms involves applying a Savitzky–Golay (SG) smoothing filter to remove noise from petrophysical well-logs and drilling data from the mud-logs. A genetic algorithm is applied to tune the machine learning algorithms by identifying and ranking the most influential input variables on ROP. This tuning routine identified and selected eight input variables which have the greatest impact on ROP. These are: weight on bit, bit rotational speed, pump flow rate, pump pressure, pore pressure, gamma ray, density log and sonic wave velocity. Results showed that the machine learning algorithms evaluated all predicted ROP accurately. Their performance was improved when applied to filtered data rather than raw well-log data. The MLP-PSO model as a hybrid ANN demonstrated superior accuracy and effectiveness compared to the other ROP-prediction algorithms evaluated, but its performance is rivalled by the SVR model.


Rate of penetration Data mining Machine-learning predictions ROP variables Feature selection ranking Data filtering 



Weight on bit


Bit rotation speed


Bit flow rate


Pump pressure


Mud weight


Gamma ray


Sonic shear velocity


Sonic compressional velocity


Pore pressure


Neutron porosity


Decision tree


Random forest


Multi-layer perception


Radial- basis function


Support vector regression


Particle swarm optimization


Input variable value


Weight matrix


Bias vector


Number of clusters


Number of input and output variables


Slack variable


Error- monitoring parameter


Lagrange multiplier


Kernel function


Center of RBF unit i


Root mean square error


Performance index


Variance account for


Supplementary material

12145_2019_381_MOESM1_ESM.xlsx (110 kb)
ESM 1(XLSX 110 kb)


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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2019

Authors and Affiliations

  • Mohammad Sabah
    • 1
    Email author
  • Mohsen Talebkeikhah
    • 1
  • David A. Wood
    • 2
  • Rasool Khosravanian
    • 1
  • Mohammad Anemangely
    • 3
  • Alireza Younesi
    • 1
  1. 1.Department of Petroleum EngineeringAmirkabir University of Technology (Tehran polytechnic)TehranIran
  2. 2.DWA Energy LimitedLincolnUK
  3. 3.Faculty of mining, Petroleum and Geophysics EngineeringShahrood University of TechnologyShahroodIran

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