Abstract
The study of the geomechanical parameters is necessary for field planning and development. Two of the most critical parameters used to describe the rock strength are the tensile (Ts) and the uniaxial compressive strength (UCS). Measuring these two parameters in the lab is time-consuming. Consequently, non-destructive methods have been developed to predict these parameters fast and reliable. Field drilling data can be reliable, continuous, and rapid technology in predicting UCS and Ts. Herein, an artificial neural intelligence network (ANN) predicts Ts and UCS from actual drilling data collected from two fields in the Middle East. The data include rate of penetration (ROP), weight on bit (WOB), torque (T), drilling fluid injection rate (Q), and the standpipe pressure (SPP). Several sensitivity analyses were conducted to optimize the models’ parameters and inputs, followed by extracting the weights and biases for develo** ANN-based relations for Ts and UCS. The results showed that the ANN was highly accurate during the training phase in predicting UCS with an AAPE of 0.28%, and Ts with an AAPE of 0.28%. The developed correlation effectively predicted Ts and UCS for an average AAPE of 0.59 % during the testing phase and only 0.65 % for the validation data set for both parameters. This method provides a real-time effective tool for predicting the strength parameters in continuous, fast, and reliable measurements from the drilling field data.
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Abbreviations
- AI :
-
artificial intelligence
- ANN :
-
artificial neural network
- ANFIS :
-
adaptive neuro-fuzzy inference system
- ML :
-
machine learning
- FIS :
-
fuzzy inference system
- SFS :
-
Stochastic Fractal Search
- R :
-
correlation coefficient
- AAPE :
-
absolute average percentage error
- IWO :
-
invasive weed optimization
- PSO :
-
particle swarm optimization
- Rn :
-
Schmidt hammer rebound number
- Vp :
-
p-wave velocity
- Is50 :
-
point load strength index
- BTS :
-
Brazilian tensile strength
- BPI :
-
block punch index
- RL :
-
lithology type
- W :
-
weathering grade
- CPI :
-
cylinder punch index
- UCS :
-
unconfined compressive strength
- Ts :
-
tensile strength
- y i :
-
dependent parameter
- x i :
-
independent parameter
- σ x :
-
standard deviation of independent parameter
- σ y :
-
standard deviation of independent parameter
- μ x :
-
mean of independent parameter
- μ y :
-
mean of dependent parameter
- WOB :
-
weight on bit
- ROP :
-
rate of penetration
- Q :
-
drilling fluid injection rate
- T :
-
torque
- SPP :
-
standpipe pressure
- SDI :
-
four cycle slake durability index
- Id4 :
-
four-cycle SDI (%)
- μ:
-
Poisson’s ratio
- b:
-
dry unit weight kN/m3
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Acknowledgements
The authors would like to thank King Fahd University of Petroleum & Minerals (KFUPM) for employing its resources in conducting this work.
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Appendix
Appendix
Unit conversion table
SI metric conversion factors | |
|---|---|
cP × 1* | E-03 = Pa s |
(°F-32) × 5/9 + 273.15 | E+00 = K |
in. × 2.54* | E-02 = m |
psi × 145.038 | MPa |
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Hiba, M., Ibrahim, A.F. & Elkatatny, S. Real-time prediction of tensile and uniaxial compressive strength from artificial intelligence-based correlations. Arab J Geosci 15, 1546 (2022). https://doi.org/10.1007/s12517-022-10785-0
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DOI: https://doi.org/10.1007/s12517-022-10785-0