ABSTRACT: Static Poisson’s ratio plays a vital role in calculating the minimum and maximum horizontal stresses which are required to alleviate the risks associated with the drilling and production operations. Incorrect estimation of Static Poisson’s ratio may wrongly lead to inappropriate field development plans which consequently result in heavy investment decisions. Static Poisson’s ratio can be determined by retrieving cores throughout the depth of the reservoir section and performing laboratory tests, which are extremely expensive as well as time consuming. The objective of this paper is to develop a robust and an accurate model for estimating static Poisson’s ratio based on 610 core sample measurements and their corresponding wireline logs data using artificial neural network. The obtained results showed that the developed ANN model was able to predict the static Poisson’s ratio based on log data; bulk density, compressional time, and shear time. The developed ANN model can be used to estimate static Poisson’s ratio with high accuracy; the correlation coefficient was 0.98 and the average absolute error was 1.3%. In the absence of core data, the developed technique will help engineers to estimate a continuous profile of the static Poisson’s ratio and hence reduce the overall cost of the well.

ABSTRACT: Unconfined compressive strength (UCS) is the key parameter to; estimate the in situ stresses of the rock, alleviate drilling problems, design optimal fracture geometry and to predict optimum mud weight. Retrieving reservoir rock samples throughout the depth of the reservoir section and performing laboratory tests on them are extremely expensive as well as time consuming. Therefore, mostly UCS predicted from empirical correlations. Most of the empirical correlations for UCS prediction are based on elastic parameters or on compressional wave velocity. These correlations were developed using linear or non-linear regression techniques. This paper presents a rigorous empirical correlation based on the weights and biases of Artificial Neural Network to predict UCS. The testing of new correlation on real field data gave a less error between actual and predicted data, suggesting that the proposed correlation is very robust and accurate. Therefore, the developed correlation can serve as handy tool to help geo-mechanical engineers in order to determine the UCS.

ABSTRACT: Good understanding of the mechanical behavior of reservoir rock is very important in reducing the problems related to wellbore stability, sand production and reservoir subsidence. To carry out any operation, a continuous profile of rock mechanical parameters is needed. Retrieving reservoir rock samples throughout the depth of the reservoir and performing laboratory tests are extremely expensive and time consuming. Therefore, these parameters are estimated from the sonic and compressional wave velocities obtained from well-logs. Parameters obtained from laboratory tests are termed as static parameters while those obtained from sonic logs are dynamic parameters. The former case represents closely the condition in the reservoir. Since the well-logs provide a continuous profile of parameters, they have to be calibrated with respect to the static parameters. This paper presents a rigorous empirical correlations based on the weights and biases of Artificial Neural Network to predict sonic logs (compressional and shear wave travel times), elastic parameters (static Young’s modulus and Poisson’s ratio) and failure parameter (Unconfined compressive strength).The testing of new correlations on real field data resulted in less error between actual and predicted values, suggesting that the proposed correlations are very robust and accurate, and can help geo-mechanical engineers to construct representative earth model. 1. INTRODUCTION