Title:

Improved Filtering for a New Resource Assessment Method

Authors:

Andrew POWER, Michael GRAVATT, Ken DEKKERS, Oliver MACLAREN, Ruanui NICHOLSON, John O'SULLIVAN, Alex DE BEER, Theo RENAUD, Michael O'SULLIVAN

Key Words:

pre-exploration data, resource assessment uncertainty, geothermal energy ppotential, Waiwera, high-performance computing.

Conference:

Stanford Geothermal Workshop

Year:

2023

Session:

Modeling

Language:

English

Paper Number:

Power

File Size:

1924 KB

View File:

Abstract:

Keeping the planet sustainable for future generations is a global goal of society. Furthermore, energy from fossil fuels is getting scarcer and more expensive. Geothermal energy is still an undervalued renewable energy source and should be utilised more. However, there is a large economic risk involved for the exploration and installation of a green field. Managing the economic risk of a green field is done by assessing the potential of the new geothermal energy source. This is currently still done by stored heat calculations and applying a roughly estimated recovery factor. We have shown a new method for assessing the potential of a green field by using numerical modelling and uncertainty quantification. This new method uses the same available data as in stored heat calculations but also includes reservoir physics, wellbore physics and realistic energy extraction scenarios. This method has shown interesting resource assessment results. Currently, we have improved our method to include more data and information of the geothermal system in the pre-filtering of the steady state sample models. This information includes, but is not limited to, the size and shape of the thermal plume, distinctive for a geothermal system, the temperature outside the active reservoir, and surface features. Including more data to condition the steady state samples on improves the pre-filtering process. By using the same algorithm for extraction of geothermal fluid from the filtered samples we assess the value of conditioning on different types of data. The results of the production scenarios show a similar result for the resource assessment but with less uncertainty. This shows that updating the resource assessment when there is new available data, improves the accuracy of the forecasts and thus helps managing the economic risks that come with the exploration of a green field.


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