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 (2024.08.04.0.2.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/
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"description": "TOPIC\n===============================\nA Python-based stochastic library for assessing geothermal power potential using the volumetric\nmethod in a liquid-dominated reservoir.\n\nAuthors\n--------------\n- Carlos Pocasangre Jim\u00e9nez (carlos.pocasangre@ues.edu.sv)\n\n- Fidel Ernesto Cortez Torres (ernestocortez.sv@ieee.org)\n\n- Rub\u00e9n Alexander Henr\u00edquez Miranda (rubenhenriquez@ieee.org)\n\nABSTRACT\n===============================\nWe present a Python-based stochastic library for assessing geothermal power\npotential using the volumetric method in a liquid-dominated reservoir.\nThe specific aims of this study are to use the volumetric method, \u201cheat in\nplace,\u201d to estimate electrical energy production ability from a geothermal\nliquid-dominated reservoir, and to build a Python-based stochastic library\nwith useful methods for running such simulations. Although licensed\nsoftware is available, we selected the open-source programming language\nPython for this task. The Geothermal Power Potential Evaluation stochastic\nlibrary (*gppeval*) is structured as three essential objects including a\ngeothermal power plant module, a Monte Carlo simulation module, and a tools\nmodule.\n\nFor testing the application, a **Jupyter Notebook** example has been included in the `example\nfolder`_.\n\n*HINT*: **Now, this application is available for Python 3.5**\n\nReference\n--------------\nPocasangre, C., & Fujimitsu, Y. (2018). *A Python-based stochastic library for assessing\ngeothermal power potential using the volumetric method in a liquid-dominated reservoir*.\n**Geothermics**, 76, 164-176.\nhttps://doi.org/10.1016/J.GEOTHERMICS.2018.07.009\n\nJ. Lawless. 2010. Geothermal Lexicon For Resources and Reserves Definition\nand Reporting. 2nd Edition (2010) Edition. Adelaide, Southern Australia:\nAustralian Geothermal Reporting Code Committee (AGRCC)\n\nINSTALLATION\n============\n\nRequired Packages\n-----------------\n\nThe following packages should be installed automatically (if using 'pip'\nor 'easy_install'), otherwise they will need to be installed manually:\n\n- NumPy_ : Numeric Python\n- SciPy_ : Scientific Python\n- Matplotlib_ : Python plotting library\n- Mcerp_ : Monte Carlo Error Propagation\n- Iapws_ : The InternationalAssociation for the Properties of Water and Steam\n- Beautifultable_ : Utility package to print visually appealing ASCII tables to terminal\n\nHow to install\n--------------\n\nYou have **several easy, convenient options** to install the 'gppeval'\npackage (administrative privileges may be required).\n\n#. Simply copy the unzipped 'gppeval folder' directory to any other location that\n python can find it and rename it 'gppeval'.\n\n#. From the command-line, do one of the following:\n\n a. Manually download the package files below, unzip to any directory, and\n run:\n\n $ [sudo] python setup.py install\n\n b. If 'pip' is installed, run the follow command (stable version and internet connection is required)\n\n $ [sudo] pip install [--upgrade] gppeval\n\nCHANGES OF NEW ISSUE\n====================\n\n#. gppeval (2024.08.04.0.2.dev1).\n Fixed bugs.\n\n#. gppeval (2020.10.1.0.3.dev1).\n Added tho-phases reservoir equation.\n Fixed bugs.\n\n#. gppeval (2019.4.17.0.6.dev1).\n Python 3.8\n Fixed bugs.\n\n#. gppeval (2019.4.17.0.2.dev1).\n Python 3.5 available\n\n#. gppeval (2018.10.11.0.1.dev1).\n The input file csv has been modified. It includes the possibility of using volume as a input\n reservoir parameter. Using the word ``none`` is possible to exchange between either to use\n **Area** and **Thickness** or to use only **Volume** as a reservoir geometric parameter.\n\n Example: Using Area and Thickness\n\n 0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,T\n 1,,,,Thickness,h,m,450,500,600,0,0,T\n 2,,,,Volume,v,km3,4,6,8.2,0,0,none\n\n Example: Using only Volume\n\n 0,Name,14.00061,-88.73744,ReservoirArea,A,km2,5,6,7,0,0,None\n 1,,,,Thickness,h,m,450,500,600,0,0,None\n 2,,,,Volume,v,km3,4,6,8.2,0,0,T\n\n#. gppeval (2018.4.6.0.1.dev1).\n Original issue after have been upload as a stable.\n\n#. gppeval (2017.10.1.0.1.dev1).\n Original issue.\n\nCONTACT\n=======\n\nPlease send **feature requests, bug reports, or feedback** to: `Carlos O. POCASANGRE JIMENEZ`_\n\n.. _Monte Carlo methods: http://en.wikipedia.org/wiki/Monte_Carlo_method\n.. _latin-hypercube sampling: http://en.wikipedia.org/wiki/Latin_hypercube_sampling\n.. _error propagation: http://en.wikipedia.org/wiki/Propagation_of_uncertainty\n.. _math: http://docs.python.org/library/math.html\n.. _NumPy: http://www.numpy.org/\n.. _SciPy: http://scipy.org\n.. _Matplotlib: http://matplotlib.org/\n.. _scipy.stats: http://docs.scipy.org/doc/scipy/reference/stats.html\n.. _uncertainties: http://pypi.python.org/pypi/uncertainties\n.. _Mcerp: http://github.com/tisimst/mcerp\n.. _Beautifultable: https://github.com/pri22296/beautifultable\n.. _Gppeval: http://github.com/cpocasangre/gppeval\n.. _example folder: https://github.com/cpocasangre/gppeval\n.. _Carlos O. POCASANGRE JIMENEZ: mailto:carlos.pocasangre@ues.edu.sv\n.. _Iapws: https://pypi.org/project/iapws/\n\n\n",
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