Article · Journal of Petroleum Exploration and Production Technology · December 29, 2015
Wellbore stability model based on iterative coupling method in water alternating gas injection
Mahmood Bataee, Sonny Irawan, Syahrir Ridha
Abstract
Ensuring wellbore integrity is the most important factor in injection well design. The water alternating gas (WAG) injection is increasingly applied globally as the effective enhanced oil recovery (EOR) method in oil wells. High injection pressure or low injection temperature could lead to compressive wellbore failure. The rock stress around the wellbore is a function of the wellbore fluid flow and it should be precisely determined to avoid the wellbore failure. The purpose of this study is to propose a method to ensure the stability of the wellbore for the WAG process using iterative coupling method. The parameters of pressures, temperature, saturations and stresses are obtained for the multiphase flow condition using mathematical modeling. In this study, finite difference method is used to solve pressure, temperature and saturations; and finite volume method is acquired to solve the rock stresses. Iterative coupling method is employed to improve the accuracy of the results. This study introduces improved iterative coupling method between flow and stress models to reduce the processing time of obtaining corrected stress and failure results. Wellbore stability model is developed to determine the maximum pressure values, which lead to wellbore failure in WAG injection process for some different boundary conditions.
More by Sonny Irawan
- An explainable physics-informed neural network prototype for synthetic proppant transport in a two-dimensional fracture domainPhysics of Fluids · 2026
- Physics‐Guided Neural Regression Modeling of Energy‐Balance Proxies for Hydraulic Fracture Propagation in 1D–3DInternational Journal for Numerical and Analytical Methods in Geomechanics · 2026
- Modeling and Prediction of Kappaphycus alvarezii Viscosity Using Artificial Neural Network and Adaptive Neuro-Fuzzy Inference SystemJournal of Earth Energy Science Engineering and Technology · 2025 · Citations: 1
- Physics-Informed Machine Learning for Predicting Drilling Fluid Losses in Naturally Fractured Caspian FormationsSPE Annual Caspian Technical Conference and Exhibition · 2025 · Citations: 4
