William Wagner Matos Lira’s research while affiliated with Federal University of Alagoas and other places

This study aims to study and compare numerical modeling strategies for reproducing the phenomenon of Annular Pressure Build-up (APB) in vertical oil wells, in order to contribute to the development of procedures with higher accuracies. APB occurs due to the tendency of confined fluids filling the annular region between casings to expand in response to thermal variations within the well. This phenomenon is critical in the petroleum industry, especially in deepwater environments, where greater temperature and pressure differentials are present. APB leads to increased stresses on well casings, which can cause structural failures and, in extreme situations, could in human, environmental, and economic losses. Therefore, studying the origins and effects of this phenomenon and considering them during the well design phase are essential to ensure safety and efficiency. Motivated by the significance of the topic and the challenge of reproducing APB analytically, several authors have sought to model the phenomenon and its effects using finite element-based computational software like Abaqus. To achieve the proposed objective, the methodology adopted includes: a) literature review of existing strategies for APB modeling; b) definition of a simplified scenario for reproducing selected strategies; c) comparison of methodologies and results obtained from each; and d) discussion on discrepancies, gaps, and potential improvement opportunities. This study evaluates two modeling strategies in Abaqus, both utilizing fluid cavity interaction to model fluid behavior within a plane axisymmetric analysis. The difference lies in the approach to thermal expansion. While one calculates APB directly from thermal variation, the other does so by introducing an equivalent mass flow. Furthermore, the strategies will be compared not only in terms of results and accuracy, but also with regards to computational cost, aiming to identify the most efficient approach for modeling the phenomenon. Despite methodological differences, both approaches yield similar results, with the second providing the flexibility to model fluids with different behaviors. Thus, this study contributes to understanding and optimizing APB modeling, aiding in the development of more robust and efficient strategies for predicting the effects of this phenomenon.

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.

Oil and gas wells are typically constructed in highly complex and harsh environments. Casing is a crucial component of the well structure, enduring a wide range of loads throughout the well life cycle, including internal and external pressures, and axial forces. After installation, these tubulars may develop wear grooves on their inner walls due to contact with the tool joints of the drill string. This reduction in wall thickness, combined with initial geometric imperfections such as ovality and eccentricity, as well as residual stresses, can significantly reduce tubular resistance, especially under external pressure (collapse). Thus, models capable of accurately estimating collapse pressure, validated with realistic data, are extremely relevant. In this context, some collapse prediction models of worn casing have been proposed in the literature, based on experimental, analytical, or numerical approaches. However, in several cases, the data used to derive these models are limited in quantity and variety. The present study aims to investigate and propose improvements in the equations for the collapse pressure of worn casing by utilizing a large database of numerical simulations. Several Finite Element (FE) simulations are performed to generate a substantial database of collapsed pipes. The accuracy of collapse prediction models from the literature is then evaluated, and new model parameters are calibrated to enhance precision. The study adopts material and geometric configurations commonly observed in worn casing tubulars of oil and gas wells. Two subsets of the large database are generated: one for fitting the models and the other for testing them. The exploratory analysis of the FE database provides insights into the collapse strength deration concerning relevant parameters such as damage depth, tool joint radius, and tube slenderness. The results compare the accuracy of the models, and a parallel discussion about the influence of various features is conducted.

This work presents a casing integrity study that begins with data collected from an ultrasonic log inspection and progresses to the estimation of the residual collapse strength of damaged tubes, using a probabilistic-based Finite Element (FE) modeling. The goal is to provide a data-driven enhanced assessment of the structural integrity of tubular components in a high complexity and risk environment, as in the case of the Brazilian pre-salt region. Damage identification employs a technique that seeks an ellipse geometry resembling the probable intact shape of the casing's inner wall. Then, the residual collapse strength is estimated by using physically and geometrically nonlinear FE modeling. The probabilistic analysis is carried out using the First Order Reliability Method (FORM), considering the FE modeling limit state and random variables associated with material and geometry of the tubulars, as well as damage parameters such as maximum depth and position. Geometries of ovalized and eccentric cross sections, associated with multiple damages at various intensities and positions are evaluated. The nonlinear FE modeling allowed the capture of different collapse modes of the element, depending on its slenderness and damage configuration. Through the probabilistic approach, it became possible to account for inherent uncertainties associated with different design and damage parameters, enabling the calculation of the probability of element failure. In the conducted case study, the loads were set equal to the collapse pressures calculated with design equations from the literature. As the obtained probability of failure did not meet a pre-defined target, a finding procedure was implemented to achieve an adequate collapse pressure for design purposes. Although this procedure is computationally expensive and only two critical cross section were assessed, it mitigates many simplifications commonly observed in other works, and the results can significantly contribute to casing design and intervention plans.

This work presents a framework to classify and evaluate distinct research abstract texts which are focused on the description of processes and their applications. In this context, this paper proposes natural language processing algorithms to classify, segment and evaluate the results of scientific work. Initially, the proposed framework categorize the abstract texts into according to the problems intended to be solved by employing a text classification approach. Then, the abstract text is segmented into problem description, methodology and results. Finally, the methodology of the abstract is ranked based on the sentiment analysis of its results. The proposed framework allows us to quickly rank the best methods to solve specific problems. To validate the proposed framework, oil production anomaly abstracts were experimented and achieved promising results.

This work presents a methodology for developing oil well casing designs to make this process more trustworthy and agile. This is achieved by using an application developed inside a collaborative web environment where all the relevant information for the casing design can be obtained. The methodology makes two major steps on the casing design automatically and according to the company's rules: the input of the data necessary for the design and applying the appropriated loads to all the casings. The automation of these steps has not only significantly increased the casing design team productivity but also raised the reliability of the generated designs since they are guaranteed to be made according to the standards.