User eXperience (UX) has been used to achieve improvements in digital information systems based on how people perceive them. In particular, this paper establishes a framework that employs methods for eye and mouse tracking, keyboard input, self-assessment questionnaire and artificial intelligence algorithms to evaluate user experience and categorize users in terms of performance profiles. The results obtained with this framework are artifacts that can be used to support customizations of the User Interface (UI) on the websites. Moreover, the established framework is generic and flexible and can be applied to any information system, such as the case study shown in the website of the Federal Revenue of Brazil (RFB). The main objectives of this paper are as follows: (i) to set out a powerful UX framework based on three tracking techniques – the AIT2-UX; (ii) to provide the T2-UXT to collect, collate, process and visualize data obtained from users’ interactions (iii) to use and compare machine learning algorithms with the classification of user performance profiles; (iv) to use the artifacts generated by the framework to manually customize the UI with the website.

Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts’ work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.

The development of an oil reservoir consists in drilling wells that maximize revenue. The quest for this configuration is often based on optimization processes that use the net present value as the evaluation function. Determining quantity, location, type, and trajectory of wells in a reservoir is a complex optimization problem, which has a high computational cost due to the continuous use of simulators. Many researchers have proposed the replacement of the reservoir simulator by a proxy, to reduce the computational cost, with promising results. However, in order to analyze all relevant variables and obtain a better solution, a comprehensive proxy model must also consider the conditions of the surface behavior, incorporating the complete integrated system. None of the previous works has developed a model based on the integrated simulation behavior. This article presents the proposal of a new proxy model based on neural networks, called SimProxy, which integrates reservoir and surface behavior, to reduce the computational cost of a decision support system. The proposed model was evaluated in a real oil reservoir. The results indicate that SimProxy can efficiently replace the use of commercial simulators in an optimization process, providing good accuracy with a substantial decrease in computational cost.