Title:

Deploying Digital Twins for Geothermal Operations with the GOOML Framework

Authors:

Iraklis KONSTANTOPOULOS, Paul SIRATOVICH, Grant BUSTER, Nicole TAVERNA, Jon WEERS, Andrea BLAIR, Jay HUGGINS, Christine SIEGA, Warren MANNINGTON, Alex URGEL, Johnathan CEN, Jaime QUINAO, Robbie WATT, John AKERLEY

Key Words:

field optimization, digital twins, plant operations, algorithms, machine learning, Wairakei, strategy, modelling

Conference:

Stanford Geothermal Workshop

Year:

2023

Session:

Modeling

Language:

English

Paper Number:

Konstantopoulos

File Size:

660 KB

View File:

Abstract:

We present GOOML, a Geothermal operations optimisation framework based on machine learning and components modelling under constraints informed by physics. The framework was developed in the real world, using data from steam fields of various types (e.g., brine and dry steam) and configurations (e.g., single plant, binary, direct heat) to develop digital twins. GOOML aims to increase the output of steam fields all over the world by allowing operators to run countless scenarios by simulating changes to their fields. In doing so we remove the need to experiment in the physical world, therefore removing the significant costs associated with developing new operational strategies. During the research phase GOOML has suggested strategies that can bring about a few percent increase in electricity generation. In the next phase we hope to deploy this system across the world.


ec2-3-230-143-213.compute-1.amazonaws.com, you have accessed 0 records today.

Press the Back button in your browser, or search again.

Copyright 2023, Stanford Geothermal Program: Readers who download papers from this site should honor the copyright of the original authors and may not copy or distribute the work further without the permission of the original publisher.


Attend the nwxt Stanford Geothermal Workshop, click here for details.

Accessed by: ec2-3-230-143-213.compute-1.amazonaws.com (3.230.143.213)
Accessed: Friday 19th of April 2024 03:25:56 AM