TOPIC

A Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir.

Authors

• Carlos Pocasangre Jiménez (carlos.pocasangre@ues.edu.sv)

• Fidel Ernesto Cortez Torres (ernestocortez.sv@ieee.org)

• Rubén Alexander Henríquez Miranda (rubenhenriquez@ieee.org)

ABSTRACT

We present a Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir. The specific aims of this study are to use the volumetric method, “heat in place,” to estimate electrical energy production ability from a geothermal liquid-dominated reservoir, and to build a Python-based stochastic library with useful methods for running such simulations. Although licensed software is available, we selected the open-source programming language Python for this task. The Geothermal Power Potential Evaluation stochastic library (gppeval) is structured as three essential objects including a geothermal power plant module, a Monte Carlo simulation module, and a tools module.

For testing the application, a Jupyter Notebook example has been included in the example folder_.

HINT: Now, this application is available for Python 3.5

Reference

Pocasangre, C., & Fujimitsu, Y. (2018). A Python-based stochastic library for assessing geothermal power potential using the volumetric method in a liquid-dominated reservoir. Geothermics, 76, 164-176. https://doi.org/10.1016/J.GEOTHERMICS.2018.07.009

J. Lawless. 2010. Geothermal Lexicon For Resources and Reserves Definition and Reporting. 2nd Edition (2010) Edition. Adelaide, Southern Australia: Australian Geothermal Reporting Code Committee (AGRCC)

INSTALLATION

Required Packages

The following packages should be installed automatically (if using 'pip' or 'easy_install'), otherwise they will need to be installed manually:

• NumPy_ : Numeric Python
• SciPy_ : Scientific Python
• Matplotlib_ : Python plotting library
• Mcerp_ : Monte Carlo Error Propagation
• Iapws_ : The InternationalAssociation for the Properties of Water and Steam
• Beautifultable_ : Utility package to print visually appealing ASCII tables to terminal

How to install

You have several easy, convenient options to install the 'gppeval' package (administrative privileges may be required).

#. Simply copy the unzipped 'gppeval folder' directory to any other location that python can find it and rename it 'gppeval'.

#. From the command-line, do one of the following:

a. Manually download the package files below, unzip to any directory, and run:

   $ [sudo] python setup.py install

b. If 'pip' is installed, run the follow command (stable version and internet connection is required)

   $ [sudo] pip install [--upgrade] gppeval

CHANGES OF NEW ISSUE

#. gppeval (2024.08.04.0.1.dev1). Fixed bugs.

#. gppeval (2020.10.1.0.3.dev1). Added tho-phases reservoir equation. Fixed bugs.

#. gppeval (2019.4.17.0.6.dev1). Python 3.8 Fixed bugs.

#. gppeval (2019.4.17.0.2.dev1). Python 3.5 available

#. gppeval (2018.10.11.0.1.dev1). The input file csv has been modified. It includes the possibility of using volume as a input reservoir parameter. Using the word none is possible to exchange between either to use Area and Thickness or to use only Volume as a reservoir geometric parameter.

Example: Using Area and Thickness

    0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,T
    1,,,,Thickness,h,m,450,500,600,0,0,T
    2,,,,Volume,v,km3,4,6,8.2,0,0,none

Example: Using only Volume

    0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,None
    1,,,,Thickness,h,m,450,500,600,0,0,None
    2,,,,Volume,v,km3,4,6,8.2,0,0,T

#. gppeval (2018.4.6.0.1.dev1). Original issue after have been upload as a stable.

#. gppeval (2017.10.1.0.1.dev1). Original issue.

CONTACT

Please send feature requests, bug reports, or feedback to: Carlos O. POCASANGRE JIMENEZ_

.. _Monte Carlo methods: http://en.wikipedia.org/wiki/Monte_Carlo_method .. _latin-hypercube sampling: http://en.wikipedia.org/wiki/Latin_hypercube_sampling .. _error propagation: http://en.wikipedia.org/wiki/Propagation_of_uncertainty .. _math: http://docs.python.org/library/math.html .. _NumPy: http://www.numpy.org/ .. _SciPy: http://scipy.org .. _Matplotlib: http://matplotlib.org/ .. _scipy.stats: http://docs.scipy.org/doc/scipy/reference/stats.html .. _uncertainties: http://pypi.python.org/pypi/uncertainties .. _Mcerp: http://github.com/tisimst/mcerp .. _Beautifultable: https://github.com/pri22296/beautifultable .. _Gppeval: http://github.com/cpocasangre/gppeval .. _example folder: https://github.com/cpocasangre/gppeval .. _Carlos O. POCASANGRE JIMENEZ: mailto:carlos.pocasangre@ues.edu.sv .. _Iapws: https://pypi.org/project/iapws/