Automated Approach for Modeling APB in Oil Wells

This paper presents an automated approach for modeling APB (Annular Pressure Build-up) in vertical oil wells. The APB phenomenon is characterized by an increase in pressure in the annular spaces of wells due to the variation in temperature of confined fluids, resulting in significant loading differentials on the casings. Therefore, to ensure the well’s structural integrity, it is essential to consider the effects of APB in the design of equipment and casings. However, the calculation of pressure buildup is complex and lacks a closed (or analytical) solution, requiring the use of computational tools. In the literature, some successful works can be identified that model the APB phenomenon in finite element-based software, such as ABAQUS, for example. However, creating models through the graphical interface is a slow and limited process. To achieve the proposed objective, a work methodology is developed based on the following steps: a) development of a strategy for modeling APB in ABAQUS; b) creation of Python scripts to automate all tasks (pre-processing and solving) of the developed strategy; and c) validation of the proposed strategy through scenarios with results available in the literature. In the developed approach, all scenario data is described in a JSON structured file. To model the APB, the main Python file is executed, and thus, the entire developed strategy is automatically executed in ABAQUS. Finally, the ABAQUS graphical interface is opened, displaying all results. All annular fluid modeling is performed using ABAQUS's own fluid cavity interaction. Compared to another approach available in the literature, the developed strategy shows relative errors of up to 10% in predicting APB. This discrepancy can be justified by the simplifications in calculations adopted by the reference work and by considering the Fluid Cavity at a constant temperature throughout the annulus, as done in this study. Despite the differences found this study contributes by providing an additional tool to assist studies related to the APB phenomenon and in predicting its corresponding effects on casings. Furthermore, the proposed automation adds speed to modeling and prevents errors in scenario construction or strategy reproduction.