%0 Journal Article
%@ 21900558
%A Sulaimon, A.A.
%A Teng, L.L.
%D 2020
%F scholars:13522
%I Springer
%J Journal of Petroleum Exploration and Production Technology
%K Abandoned wells; Compressive strength; Elastic moduli; Forecasting; Gas industry; Geomechanics; MATLAB; Neural networks; Oil wells; Sand; Shear strain; Well logging, Failure envelope; Geomechanical model; Geomechanical properties; Logging data; Oil and Gas Industry; Unconfined compressive strength; Well abandonment; Wellbore pressure, Oil well logging
%N 2
%P 537-555
%R 10.1007/s13202-019-00784-5
%T Modified approach for identifying weak zones for effective sand management
%U https://khub.utp.edu.my/scholars/13522/
%V 10
%X Sand production is a major problem that the oil and gas industry has been facing for years. It can lead to loss of production, equipment damage or complete well abandonment. Prediction of sand has been historically challenging due to the periodic nature of sand production, insufficient laboratory tests and lack of field tests validation. Analyses have been performed to identify weak zones for planned wells, and common technique is the application of shear modulus and mechanical properties log (MPL) criteria developed by Tixier et al. (J Pet Technol 27:283�293, 1975). However, the set criteria have been found to be generally inadequate to detect transition zone or predict weak formation in some fields. In this study, using the knowledge of rock behavior, geomechanical properties and well log data, we have established new simple criteria for identifying fragile sections within a transition zone. In situ logging data from a field X, located in Sabah, Malaysia, and Field Y, located in Shimokita, Japan, were used in this study. Using the threshold for shear modulus and MPL, the criteria for the geomechanical properties are set to differentiate formation strengths at different depths. The threshold for Poisson�s ratio is 0.34, Young�s modulus at 1.6 � 106 psi and the unconfined compressive strength at 2400 psi. The MPL and geomechanical models were generated to predict sanding incident. The results were subsequently validated with artificial neural network using MATLAB. Also, critical wellbore pressure is calculated and acts as a guide to operate outside the sand failure envelope. Thus, the prediction of the weak formation using geomechanical properties has been further established in this study. © 2019, The Author(s).
%Z cited By 7