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Research Papers: Petroleum Engineering

Application of Artificial Intelligence Techniques to Estimate the Static Poisson's Ratio Based on Wireline Log Data

[+] Author and Article Information
Salaheldin Elkatatny

Department of Petroleum Engineering,
King Fahd University of
Petroleum and Minerals,
Post Box No. 5049,
Dhahran 31261, Saudi Arabia;
Petroleum Department,
Cairo University,
Cairo 12613, Egypt
e-mail: elkatatny@kfupm.edu.sa

Contributed by the Petroleum Division of ASME for publication in the JOURNAL OF ENERGY RESOURCES TECHNOLOGY. Manuscript received September 19, 2017; final manuscript received March 4, 2018; published online March 29, 2018. Assoc. Editor: Ray (Zhenhua) Rui.

J. Energy Resour. Technol 140(7), 072905 (Mar 29, 2018) (8 pages) Paper No: JERT-17-1497; doi: 10.1115/1.4039613 History: Received September 19, 2017; Revised March 04, 2018

Static Poisson's ratio (νstatic) is a key factor in determine the in-situ stresses in the reservoir section. νstatic is used to calculate the minimum horizontal stress which will affect the design of the optimum mud widow and the density of cement slurry while drilling. In addition, it also affects the design of the casing setting depth. νstatic is very important for field development and the incorrect estimation of it may lead to heavy investment decisions. νstatic can be measured in the lab using a real reservoir cores. The laboratory measurements of νstatic will take long time and also will increase the overall cost. The goal of this study is to develop accurate models for predicting νstatic for carbonate reservoirs based on wireline log data using artificial intelligence (AI) techniques. More than 610 core and log data points from carbonate reservoirs were used to train and validate the AI models. The more accurate AI model will be used to generate a new correlation for calculating the νstatic. The developed artificial neural network (ANN) model yielded more accurate results for estimating νstatic based on log data; sonic travel times and bulk density compared to adaptive neuro fuzzy inference system (ANFIS) and support vector machine (SVM) methods. The developed empirical equation for νstatic gave a coefficient of determination (R2) of 0.97 and an average absolute percentage error (AAPE) of 1.13%. The developed technique will help geomechanical engineers to estimate a complete trend of νstatic without the need for coring and laboratory work and hence will reduce the overall cost of the well.

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Figures

Grahic Jump Location
Fig. 1

Estimation of Poisson's ratio using AI models for training data (427 data points)

Grahic Jump Location
Fig. 2

Estimation of static Poisson's ratio using ANN model for testing data (183 data points)

Grahic Jump Location
Fig. 3

Architecture of static Poisson's ratio model

Grahic Jump Location
Fig. 4

Actual Poisson's ratio versus predicted one using Eqs. (2) and (3) for the unseen data (183 data points)

Grahic Jump Location
Fig. 5

Input log data for well-1

Grahic Jump Location
Fig. 6

Complete profile of the static Poisson's ratio using Eq. (3) for well-1

Grahic Jump Location
Fig. 7

Input log data for well-2

Grahic Jump Location
Fig. 8

Static Poisson's ratio prediction using Eq. (3) for well-2

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