The development of new equipment technologies constitutes one of the greatest challenges in the oil and gas industry, particularly for the well engineering area. It is necessary to ensure that new technologies have satisfactory and failure-free performance for high mission times, much longer than the typical and viable durations of qualification and reliability demonstration tests. Furthermore, the development process is complex and iterative, involving different types of data from tests, numerical simulations and multiphysics analyses, from its inception to full-scale operation. In this context, it is essential to have a way to collect and aggregate these different types of data as they become available to monitor and control the technological development process, being able to provide equipment developed with the desired reliability requirements. However, to achieve this objective two key challenges need to be overcome: (i) the heterogeneity of data obtained during development, since tests and analyzes are carried out on different models, components, and stressors; (ii) the low quality of information collected in tests for such long time horizons (such as mission times for completion equipment, which can reach 27 years in Brazilian fields) due to infrastructure, technology and cost limitations. To achieve this, the methodology presented in this thesis proposes the construction of a multilevel reliability model (MRM) and a Bayesian framework that allows the use of heterogeneous data to feed the reliability model of the new technology and aggregate test data with information from other sources. , such as the opinions of experts and databases of similar systems, which are treated as a baseline for the a priori analysis of the reliability of the new technology, being updated by the test results. Two methods for obtaining a priori reliability prediction with simple and intuitive elicitation are proposed and applied to an openhole expandable packer and a sliding sleeve valve, demonstrating the robustness and applicability of the solutions for continuously and non-continuously operated systems. Furthermore, the model allows the aggregation of new information as it becomes available, allowing a residual uncertainty analysis to be carried out at each stage of development and thus providing a powerful reliability monitoring tool throughout the development process of new equipment.