To mitigate risks and improve performance during the drilling of an oil well and its various hole sections, it is recommended that its operational parameters and trajectory be monitored in real-time. This activity is crucial to avoid several problems during drilling campaigns, especially if the drilling specialist can have all the data and some level of automatic interpretation on hand, so quick decisions can be made. However, most current monitoring software do not have an interactive or immersive visualization of this data, only track plots with multiple curves. To improve specialist experience, a 3D visualization system has been developed to unify both the drilling monitoring and analysis process of charts, drilling trajectory, lithology, and seismic data. The Divisor system consists of a cloud platform that unifies and processes data from different sources and a digital 3D visualization application. Its visualization module can be used in two forms: a traditional desktop interface enhanced with 3D visualizations and an immersive mode using a Virtual Reality (VR) headset. Both allow the operator to view real-time or historical data in multiple ways, perform assessments and simulations. Additionally, in VR mode, it is possible to navigate through a full-scale virtual environment, interact with the drill hole tridimensional visualization, and freely position charts with the essential variables to be monitored. This allows for better data manipulation granting better insights related to the numerous data captured, improving the decision-making process and enhancing the interaction in troubleshooting activities. The visualization application connects to a database that contains both static/design information and real time data, enabling a deeper analysis of all data together and the execution of artificial intelligence (AI) algorithms to generate new information and predictions according to the collected data. With both tools working in synchronization, it is possible to insert data from reports, convert them to a readable standard format, and generate visualizations customized by the user. The streamlining of consumption, analysis, and understanding of data allows for savings through the reduction in the numbers of software used as well as the time required for their implementation. The system can also be used as a training environment using historical data to operators in order to check their capacity of response in different scenarios, as well as guarantee the consistency of the operational activities. As future work, this tool will be extended with more views in VR and desktop modes, including new data generated by AI and comparison of design data (real and simulated), as well as an integration with a Digital Twin platform.

The objective of this work is to present a methodology based on the analysis of drilling parameters to infer if a reservoir formation is well consolidated or not, as a support to the selection of sand control strategies. This work proposes a statistical classification model and the usage of a memory based neural network, known as LSTM (long short-term memory) network. This model explores time series characteristics of the problem and it is validated using a cross strategy. Training performance is evaluated using F1-score, which is a metric that balances precision (percentage of true positives compared to false positives) and recall (percentage of true positives compared to false negatives), chosen because the dataset is unbalanced, there are more samples of one class than the other. The dataset consists of pre-tagged wells, each of them with at least nine hours of drilling data. Considering 48 cases from different drilled wells, the model was trained to learn how to tag between both patterns. The model analyzes 23 different drilling variables to reach a conclusion. After training the model, tests were performed and the results showed a high identification efficiency: around 90% of accuracy. That way, mechanical data analysis from the drilling process plays a very important role, supplementing that information and allowing a better understanding of formation behavior by employing what can be considered full-size and a real-time scratch test. Match the collected data with those from wells in which there is logging information, provides geomechanics calibration, and allows consistent rock profiling. It helps to define not only if there is a need for sand control but also the kind of technique to be applied to the analyzed formation accordingly to its consolidation state. The impact of that information is expressive to the completion process. This feature will be very useful in Brazilian post-salt wells that present sandstone as its reservoir rock formation. Also, as this tool was designed to run in a drilling digital twin, it can be automatically run as soon as the total depth is reached in the drilling phase, providing a fast insight to anticipate completion design. It is the first time in literature that this approach is used for this specific objective: define if a gravel pack or even any kind of sand control is indeed necessary to be installed based on information gathered while drilling the well. Its great results led this tool to the deployment phase. This work also aims to illustrate the first outcomes of that application in real-time decision-making.