| Title | Greenfield resource assessment: maximising early stage data to constrain uncertainty |
|---|---|
| Authors | T. Jones, C. Saunders, K. Dekkers, M. Gravatt, R. Nicholson, O. Maclaren, T. Renaud, M. OSullivan, J. OSullivan |
| Year | 2023 |
| Conference | New Zealand Geothermal Workshop |
| Keywords | Resource assessment, reservoir simulation, uncertainty quantification, approximate Bayesian computation |
| Abstract | Geothermal fields are a valuable resource in the energy sector. However, there is significant cost and risk involved in determining the viability of a field for production. We have, therefore, improved a method to assess the potential of a geothermal resource using numerical modelling and uncertainty quantification. We have further conducted a study on the impact of different data types for resource assessment of a geothermal field. The research focuses on understanding the significance of each data type and its contribution to the accuracy of the resource estimations. A systematic methodology was developed to analyse and compare the effectiveness of various data types in assessing key parameters, including reservoir temperature, surface features, mass outflows, and clay cap formations. This was conducted by filtering models on the above parameters individually and in succession. These filtered models were then used in production simulation to gauge a representation of the predicted geothermal resource output. |