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

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Abstract

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.

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Abbreviations

WOB :

Weight on bit

BRS :

Bit rotation speed

BFR :

Bit flow rate

PP :

Pump pressure

MW :

Mud weight

GR :

Gamma ray

T s :

Sonic shear velocity

T p :

Sonic compressional velocity

P p :

Pore pressure

NP :

Neutron porosity

DT :

Decision tree

RF :

Random forest

MLP :

Multi-layer perception

RBF :

Radial- basis function

SVR :

Support vector regression

PSO :

Particle swarm optimization

x :

Input variable value

W :

Weight matrix

b :

Bias vector

N :

Number of clusters

M :

Number of input and output variables

δ :

Slack variable

ε :

Error- monitoring parameter

a :

Lagrange multiplier

K :

Kernel function

c i :

Center of RBF unit i

RMSE :

Root mean square error

PI :

Performance index

VAF :

Variance account for

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Authors and Affiliations

  1. Department of Petroleum Engineering, Amirkabir University of Technology (Tehran polytechnic), 424 Hafez Avenue, Tehran, 15875-4413, Iran

    Mohammad Sabah, Mohsen Talebkeikhah, Rasool Khosravanian & Alireza Younesi

  2. DWA Energy Limited, Lincoln, UK

    David A. Wood

  3. Faculty of mining, Petroleum and Geophysics Engineering, Shahrood University of Technology, Shahrood, Iran

    Mohammad Anemangely

Authors
  1. Mohammad Sabah
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  2. Mohsen Talebkeikhah
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  3. David A. Wood
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  4. Rasool Khosravanian
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  5. Mohammad Anemangely
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  6. Alireza Younesi
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Correspondence to Mohammad Sabah.

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Communicated by: H. Babaie

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A supplementary Excel file listing all the variables for the 1000 data records for the compiled data set from the vertical well drilled in the Maroon Field (SW Iran) is made available for readers to download.

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Sabah, M., Talebkeikhah, M., Wood, D.A. et al. A machine learning approach to predict drilling rate using petrophysical and mud logging data. Earth Sci Inform 12, 319–339 (2019). https://doi.org/10.1007/s12145-019-00381-4

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