An integrated, mesh-independent geothermal modelling framework. (May 2023) - O'Sullivan, John; Popineau, Joris; Gravatt, Michael; Renaud, Theo; Riffault, Jeremy; Croucher, Adrian; Yeh, Angus; O'Sullivan, Michael
Abstract: A new geothermal reservoir modelling framework is discussed. The framework has two main objectives: first, all the geoscience and reservoir engineering data should be stored in a simple manner, not in any way dependent on the model grid to be used, and secondly, the data storage protocols should be easily transferable from one modelling project to the next. In our framework, some of the data are stored as part of a digital conceptual model created in Leapfrog Geothermal®, while most of the rest, including well-by-well reservoir engineering data, are stored in human- and machine-readable JSON files. Finally, some of the data related to the specification of production history and future scenario parameters are stored in control spreadsheets. The reservoir model files required for running natural state, production history and future scenario simulations are set up using Leapfrog, a suite of Python scripts and control spreadsheets. The Python scripts are set up in a general way so that they require little or no modification for use on a new modelling project. The model set-up process is mainly automatic with very little manual intervention required. Due to the generality of the process, it is easy to modify the reservoir model input files when new data become available (such as updated production data). Similarly, the mesh independent database allows new models to be set up easily and quickly. This includes modification to the grid (for example, by adding local refinement) or the use of a new grid. Also, a suite of re-useable Python scripts has been developed for plotting standard sets of results from reservoir models. Highlights: An integrated framework is presented for setting up conceptual models and then numerical models of geothermal systems. The digital conceptual model is implemented in Leapfrog and the numerical model is implemented using the simulators AUTOUGH2 or Waiwera. Usually, naturals state and production history models are calibrated, and then future scenario simulations are run. Integration of modelling is carried out with Python scripts and data stored in human- and machine-readable JSON files.