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Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools

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Abstract

Unconfined compressive strength (UCS) is a major mechanical parameter of the rock which has an essential role in develo** geomechanical models. It can be estimated directly by lab testing of retrieved core samples or from well log data. These methods are very expensive and require huge efforts and time. Therefore, there is a need to develop a new technique for predicting UCS values in real-time. In this study, three artificial intelligence (AI) models were developed using artificial intelligence tools; artificial neural networks (ANN), adaptive neuro-fuzzy inference system (ANFIS), and support vector machine (SVM) to predict UCS of the downhole formations while drilling based on real-time recording of the drilling mechanical parameters. These parameters include rate of penetration (ROP), mud pum** rate (GPM), stand-pipe pressure (SPP), rotary speed in revolution per minute (RPM), torque (T), and weight on bit (WOB). A dataset of 1771 points from a Middle Eastern field was used to build the developed models: for training and testing processes. A new UCS correlation was developed based on the optimized AI model. Another set of data (2175 data points unseen by the model) was used to validate the model and the developed UCS correlation. The developed ANN-model outperformed the ANFIS- and SVM-models with a correlation coefficient (R-value) of 0.99 and an average absolute percentage error (AAPE) of 3.48% between the predicted and actual UCS values. The new UCS correlation outperformed the available correlations for UCS prediction and it was able to predict the UCS with AAPE of 4.2% compared to the actual UCS values.

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Abbreviations

AAPE:

Average absolute percentage error

AI:

Artificial Intelligence

ANN:

Artificial neural network

ANFIS:

Adaptive network-based fuzzy interference system

SVM:

Support vector machine

R:

Correlation coefficient

R2 :

Coefficient of determination

ROP:

Rate of penetration

WOB:

Weight on bit

RPM:

Rotating speed in revolution per minute

GPM:

Gallon per minute

SPP:

Standpipe pressure

T:

Torque

Fitnet Function:

Fitting neural network

Newdtdnn:

Create distributed time delay neural network

newnarx:

Create feedforward backpropagation network with feedback from output to input

newelm:

Create Elman backpropagation network

newfftd:

Create feedforward input-delay backpropagation network

newff:

Create feedforward backpropagation network

newlrn:

Layer-Recurrent Network

tansig:

Hyperbolic tangent sigmoid transfer function

logsig:

Log-sigmoid transfer function

hardlims:

Hard-limit transfer function

trainbr:

Bayesian regularization

purelin:

Linear transfer function

softmax:

Softmax transfer function

tribas:

Triangular basis transfer function

trainlm:

Levenberg–Marquardt backpropagation

trainbfg:

BFGS quasi-Newton backpropagation

traingdx:

Gradient descent with momentum and adaptive learning rule backpropagation

trainoss:

One step secant backpropagation

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Authors and Affiliations

  1. College of Petroleum Engineering and Geosciences, King Fahd University of Petroleum and Minerals, Box: 5049, Dhahran, 31261, Saudi Arabia

    Ahmed Gowida, Salaheldin Elkatatny & Hany Gamal

  2. Petroleum Department, Cairo University, Giza, 12613, Egypt

    Salaheldin Elkatatny

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  1. Ahmed Gowida
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  2. Salaheldin Elkatatny
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Correspondence to Salaheldin Elkatatny.

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Gowida, A., Elkatatny, S. & Gamal, H. Unconfined compressive strength (UCS) prediction in real-time while drilling using artificial intelligence tools. Neural Comput & Applic 33, 8043–8054 (2021). https://doi.org/10.1007/s00521-020-05546-7

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