Lognormal Power Density Resource Capacity Estimate

Converts the Cumming Excel tool into simple Python web-app

Click here for the app

---

Contributors

Initiated during the 2021 Geothermal Hackathon and now incrementally improved.

Excel tool and method design:

• William (Bill) Cumming

Python app initiator:

• Irene Wallis (Cubic Earth)

App development collaborators:

• Hannah Wood
• Jan Niederau
• Jeff Jex
• Will Middlebrook

Feedback and contributors welcome.

---

Repo structure

• data = data open sourced for this project
• docs = published papers and original excel method
• environments = environment file for conda users
• figures = plots called by either the streamlit app or other files
• notebooks = ipynb used to build the methods and explore the data
• power_user_class = advanced methods under development
• streamlit -
  • Power-Density_streamlit.py = runs streamlit app
  • requirements.txt = environment file used by streamlit sharing server
• power_dens.py = primary function for the lognormal power density method

---

The Power Density Method

Power density is one of the methods used estimate the capacity of a conventional geothermal resource. It is typically used when there are insufficient data to undertake a numerical simulation.

The lognormal power density excel tool was developed by William (Bill) Cumming in the 90's and documented in the paper linked below.

Cumming (2016) Resource Capacity Estimation Using Lognormal Distributions of Power Density Derived from Producing Fields and Area Derived from Resource Conceptual Models; Advantages, Pitfalls and Remedies

This method relies on the development of coherent conceptual models of the resource, as described in the following paper.

Cumming, William (2016) Resource Conceptual Models of Volcano-Hosted Geothermal Reservoirs for Exploration Well Targeting and Resource Capacity Assessment: Construction, Pitfalls and Challenges

The subsurface geometries of the P10-P50-P90 reservoir conceptual models are defined by available resource data and analogue reasoning. The potential production area is then defined using these geometries.

Cumming (2009) Geothermal Resource Conceptual Models Using Surface Exploration Data

Where only surface data and geophysical (MT) data are available, a range of possible subsurface resource geometries are possible. The below linked paper provides a continuum of reservoir geometries that may be used by resource teams as templates while they develop their conceptual model alternatives.

Wallis, I. C., Rowland, J. V., Cumming, W. and Dempsey, D. E., 2017, The subsurface geometry of a natural geothermal reservoir. New Zealand Geothermal Workshop: Rotorua, New Zealand.

The power density is selected based on analogue resources. Wilmarth et al. (2015, 2020+1) compiled a database of power density for conventional geothermal reservoirs. They have provided the latest version of this database for inclusion in the data section of this repo.

Wilmarth, M and Stimac, J (2015) Power Density in Geothermal Fields, Proceedings World Geothermal Congress 2015

Below are two case studies where this power density method was used to estimate resource capacity.

• Catherine Boseley, William Cumming, Luis Urzua-Monsalve, Tom Powell and Malcolm Grant (2010) A Resource Conceptual Model for the Ngatamariki Geothermal Field Based on Recent Exploration Well Drilling and 3D MT Resistivity Imaging

• Steve Sewell, William Cumming, Lutfhie Azwar, Candice Bardsley (2012) Integrated MT and Natural State Temperature Interpretation for a Conceptual Model Supporting Reservoir Numerical Modelling and Well Targeting at the Rotokawa Geothermal Field, New Zealand