|dc.description.abstract||Data analysis is one of the most important topics in any industry. In petroleum engineering, the complexity of reservoir data presents a challenge for engineers to study and make decisions. A new approach to analyze complex data is called topological data analysis, which aims to extract meaningful information from such data. It relies on the concept that complex data have shapes and these shapes can be translated to information.
The objective of this research was to use topological data analysis in studying reservoirs connectivity and compartmentalization. This topic is an essential component of reservoir engineering because it ensures the accuracy of forecasts and development plans, the correctness of reservoir simulation, and the success of performance diagnostics and optimization. In addition, introducing topological data analysis to reservoir engineering allows identification of reservoir engineering data behavior, detection of anomalies and events, and minimizing uncertainties.
Topological data analysis had been applied on inverted four-dimensional (4D) time-lapse seismic datasets. Two simulation models were used to generate the datasets: Brillig, and Norne. First, data were prepared for topological data analysis. Then, similarity distance function and lenses were defined and used to create topological data analysis graphs. Once completed, graph features were identified and analyzed. Lastly, the results were validated.
Topological data analysis was able to compartmentalize the reservoir models with various process configurations. It identified regions that matched the actual reservoir compartments in the simulation model. It has been proven to extract valued information from petroleum engineering data.||en