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Formation Resistivity Prediction Using Decision Tree and Random Forest

  • Research Article-Petroleum Engineering
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Arabian Journal for Science and Engineering Aims and scope Submit manuscript

Abstract

Formation resistivity (Rt) is a vital property for formation evaluation and calculation of water saturation and hydrocarbon in places. Rt can be estimated using core analysis and well logging. However, these processes are expensive and time-consuming. In addition, due to tool failure, and poor wellbore conditions, part of the well logging records may be missed. Hence, the objective of this paper is to predict the true formation resistivity in complex carbonate sections using decision tree (DT) and Random Forests (RF) machine learning (ML) techniques as a function of available well logging data. A data set of 5500 data points were collected from two vertical wells in carbonate formation. The data includes gamma-ray, bulk density, neutron density, compressional wave transit time, shear transient time, and the corresponding Rt. Data from Well-1 were used to develop the DT and RF models with training to the testing splitting ratio of 70:30. Dataset from Well-2 was used to validate the optimized models. The results showed the capabilities of the ML models to predict the formation resistivity from well-logging data. The correlation coefficient (R) between the actual and the predicted output values and the root mean square error (RMSE) was used to evaluate the models performance. R value for the RF model was found to be 0.99, and 0.98 for the training and the testing stages with a validation R value of 0.94. The RMSE for the developed models was less than 0.38 for training, testing, and validation stages. Using ML to predict the formation resistivity can fill the missing gaps in log tracks and save money by removing resistivity logs running in all offset wells in the same field.

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Abbreviations

API:

American petroleum institute

ANFIS:

Adaptive network-based fuzzy interference system

ANN:

Artificial neural networks

CGR:

Computed gamma ray

DT:

Decision tree

DTC:

Compressional transient time

DTS:

Shear transient time

FN:

Functional networks

GR:

Gamma ray

ILD:

Induction log deep

ILM:

Induction log medium

LLD:

Deep latero-log

LWD:

Logging while drilling

RMSE:

Root mean squared error

NPHI:

Neutron porosity

R:

Correlation coefficient

RF:

Random forests

RHOB:

Bulk density

RMSE:

Root mean squared error

R t :

True resistivity

R xo :

Flushed zone resistivity

SGR:

Spectral gamma ray

SVM:

Support vector machines

S w :

Water saturation

S xo :

Flushed zone water saturation

w :

Water

xo :

Flushed zone

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Funding

No external fund for this research and the authors would like to thank KFUPM for giving permission to publish this work.

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Authors and Affiliations

  1. College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Ahmed Farid Ibrahim, Ahmed Abdelaal & Salaheldin Elkatatny

  2. Center for Integrative Petroleum Research, King Fahd University of Petroleum and Minerals, Dhahran, 31261, Saudi Arabia

    Ahmed Farid Ibrahim & Salaheldin Elkatatny

Authors
  1. Ahmed Farid Ibrahim
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  2. Ahmed Abdelaal
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The manuscript was written through the contributions of all authors. All authors have approved the final version of the manuscript.

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Correspondence to Salaheldin Elkatatny.

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Ibrahim, A.F., Abdelaal, A. & Elkatatny, S. Formation Resistivity Prediction Using Decision Tree and Random Forest. Arab J Sci Eng 47, 12183–12191 (2022). https://doi.org/10.1007/s13369-022-06900-8

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  • DOI: https://doi.org/10.1007/s13369-022-06900-8

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