Prediction of Reservoir Temperatures Using Hydrogeochemical Data, Western Anatolia Geothermal Systems (Turkey): A Machine Learning Approach

Haklidir, Fusun Tut; Haklıdır, Mehmet

doi:10.1007/s11053-019-09596-0

Cited by 57 publications

(29 citation statements)

References 19 publications

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“…Deep learning prediction methods are used to learn complex relationships between data by continuously simulating and implementing human learning behaviour, where computers can use statistical techniques without explicit representation [2].…”

Section: Deep Neural Network and Model Uncertainty Analysismentioning

confidence: 99%

Heat Flow Prediction in Songliao Basin Based on Deep Neural Network

Bai,

Li,

Zeng

2023

J. Phys.: Conf. Ser.

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The heat flow is the key data for accurately predicting the contribution of underground heat, but due to the high cost of measurement, the spatial difference is huge, and the heat flow information in some areas is little known. This paper uses deep neural network technology to perform machine learning on a large number of relevant geological and geophysical features and heat flow measurements on a global scale. In addition, the model uncertainty quantification process is introduced, and the reliability of the prediction results is discussed based on the global sensitivity analysis and correlation calculation of the deep learning method. Finally, for the distribution map of heat flow and absolute difference in the Songliao Basin in the study area, the measured data error is less than 11%, and the predicted absolute difference is less than ±20 mW/m2. The results prove the accuracy of the method and the abnormal high heat flow in the central region of the basin. The existence of, further combined with the results of geophysical surveys, provides evidence for the analysis of regional thermal causes.

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Section: Deep Neural Network and Model Uncertainty Analysismentioning

confidence: 99%

Heat Flow Prediction in Songliao Basin Based on Deep Neural Network

Bai,

Li,

Zeng

2023

J. Phys.: Conf. Ser.

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“…They found out that the ANN model predicted the reservoir temperature with quite an accuracy with mean percentage error to be ranging from 2% to 11% between simulated and measured data. Similarly, Haklidir and Haklidir [14] also utilized ML approach to predict the reservoir temperature using hydrogeochemical data set for western Anatolia geothermal system in Turkey. Their study advocated that Deep Neural Network (DNN) showed the least root mean square error and mean absolute error and predicted the reservoir temperature most accurately.…”

Section: Literature Reviewmentioning

confidence: 99%

Prediction of Cumulative Installed Power of Geothermal Power Plants in Turkey by Using Artificial Neural Network and Bidirectional Long Short-Term Memory

Özdemir¹,

Aylak²

2022

European Journal of Science and Technology

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Turkey has a great potential for renewable energies. The number of power plants (PP) producing electricity from renewable energy sources and accordingly the installed power has risen over the years. As of the end of December 2021, the cumulative installed power of Turkey reached 99819.6 MW and the share of the total installed power of the PPs generating electricity from renewable energy sources was 53.72%. Although the installed power has increased, the percentage of PPs using renewable energy sources in total electricity generation is not yet at the desired level. However, geothermal energy is being used more and more in electricity generation alongside the other most well-known types of renewable energy. It can be observed that the installed power of geothermal power plants (GPP) in Turkey started to increase gradually after 2007, and as of the end of December 2021, the cumulative installed power reached 1676.2 MW. In this study, with the data for the cumulative installed power of GPPs in Turkey in the 2007-2021 period, the cumulative installed power of GPPs in Turkey for 2022 was predicted by using Artificial Neural Network (ANN) and Bidirectional Long Short-Term Memory (BLSTM) methods, and the results were compared and interpreted.

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“…Haklidir and Haklidir [18] developed several ML methods to predict reservoir temperatures. They analyzed data from 83 thermal springs, out of which 66 springs were chosen for training and the remaining 17 springs for testing.…”

Section: Use Of Machine Learning Tools In Geothermal Energy Productionmentioning

confidence: 99%

Machine Learning Tools for Fossil and Geothermal Energy Production and Carbon Geo-sequestration—a Step Towards Energy Digitization and Geoscientific Digitalization

et al. 2021

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Eighty percent of current world energy consumption is satisfied by subsurface resources. In future, billions of watts of electrical power will be generated from geothermal energy sources. Subsurface earth can store the energy produced from renewable sources, such as wind and solar, and could provide safe storage of contaminants and hazardous nuclear waste. Engineering of subsurface earth is critical for fossil energy production, geothermal energy production, and carbon geo-sequestration. However, the subsurface is opaque, inaccessible, and heterogenous with nano-scale to kilo-scale processes that limits our understanding (characterization) and constrains the efficient engineering of the complex subsurface earth resources. There has been rapid increase in sensor deployment, data acquisition, data storage, and data processing for purposes of better engineering and characterizing the subsurface earth. This has promoted large-scale deployment of datadriven methods, machine learning, and data analytics workflows. Subsurface data ranges from nano-scale to kilometer-scale passive as well as active measurements. Subsurface data is acquired in the form of physical fluid/solid samples, images, 3D scans, time-series data, waveforms, and depth-based multi-modal signals, to name a few. Subsurface data sense various physical phenomena, e.g., transport, chemical, mechanical, electrical, and thermal properties. Integration of such varied data sources being acquired at varying scales, rates, resolutions, and volumes mandates robust machine learning methods to better characterize and engineer the subsurface earth. Machine learning has shown to improve the efficiency, efficacy, and productivity of subsurface engineering and characterization efforts required to produce fossil/geothermal energy and to sequester carbon dioxide.

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Prediction of Reservoir Temperatures Using Hydrogeochemical Data, Western Anatolia Geothermal Systems (Turkey): A Machine Learning Approach

Cited by 57 publications

References 19 publications

Heat Flow Prediction in Songliao Basin Based on Deep Neural Network

Heat Flow Prediction in Songliao Basin Based on Deep Neural Network

Prediction of Cumulative Installed Power of Geothermal Power Plants in Turkey by Using Artificial Neural Network and Bidirectional Long Short-Term Memory

Machine Learning Tools for Fossil and Geothermal Energy Production and Carbon Geo-sequestration—a Step Towards Energy Digitization and Geoscientific Digitalization

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