Machine learning enhancement of thermal response tests for geothermal potential evaluations at site and regional scales

Bourhis, Paul; Cousin, Benoît Claude Henri; Loria, Alessandro F. Rotta; Laloui, L.

doi:10.1016/j.geothermics.2021.102132

Cited by 23 publications

(4 citation statements)

References 7 publications

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“…Understanding and quantifying the uncertainties of all included data and the modelling approach will also significantly improve the reliability of the technical geothermal potential on a regional scale. By using machine learning approaches the (geological) input data needed for the model may be estimated on a regional (Bourhis et al, 2021) or country (Assouline et al, 2019) scale, which indicates that continental scale technical geothermal potential studies are possible in the near future.…”

Section: Heating Supply Ratesmentioning

confidence: 99%

Linking technical geothermal potential from borehole heat exchangers and heating demand scenarios on a regional scale

Miocic,

Krecher

2021

Preprint

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Extracting shallow geothermal energy using borehole heat exchangers (BHEs) can help decarbonising the residential heating sector. To assist urban planers and policy makers in developing carbon-neutral heating plans the regional technical shallow geothermal potential must be analysed. Here, we propose a methodology to estimate the technical geothermal potential of BHE fields on a regional scale while taking potential thermal interference between BHEs, geological conditions, as well as space available for BHE installation into account. The number of BHEs placed is maximized and heat extraction rate from each BHE is optimized taking regional regulations into account. When the methodology is applied to the German state of Baden-Württemberg on a building-block scale, results suggest an annual technical potential of 33.5 TWh. This technical geothermal potential is then linked to heating demand scenarios per building block and the results show that, depending on the renovation status of the buildings, between 44 % and 93 % of all building blocks can heated using only BHEs. This allows for the identification of building blocks in which BHEs are not able to meet the heating demand and where other means of heat supply will be needed.

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Section: Heating Supply Ratesmentioning

confidence: 99%

Linking technical geothermal potential from borehole heat exchangers and heating demand scenarios on a regional scale

Miocic,

Krecher

2021

Preprint

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“…Extensive research is vital prior to drilling in geothermal resource areas to determine the most suitable locations, which can help reduce the costs associated with geothermal drilling and enhance the utilization of geothermal resources. Identifying RTs of geothermal waters necessitates complex geological surveys and analyses, which are time-consuming, costly, and complex [14][15][16][17][18][19][20][21][22].…”

Section: Introductionmentioning

confidence: 99%

AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature

Gurgenc,

Altay,

Altay

2024

Applied Sciences

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To ascertain the optimal and most efficient reservoir temperature of a geothermal source, long-term field studies and analyses utilizing specialized devices are essential. Although these requirements increase project costs and induce delays, utilizing machine learning techniques based on hydrogeochemical data can minimize losses by accurately predicting reservoir temperatures. In recent years, applying hybrid methods to real-world challenges has become increasingly prevalent over traditional machine learning methodologies. This study introduces a novel machine learning approach, named AOSMA-MLP, integrating the adaptive opposition slime mould algorithm (AOSMA) and multilayer perceptron (MLP) techniques, specifically designed for predicting the reservoir temperature of geothermal resources. Additionally, this work compares the basic artificial neural network and widely recognized algorithms in the literature, such as the whale optimization algorithm, ant lion algorithm, and SMA, under equal conditions using various evaluation regression metrics. The results demonstrated that AOSMA-MLP outperforms basic MLP and other metaheuristic-based MLPs, with the AOSMA-trained MLP achieving the highest performance, indicated by an R2 value of 0.8514. The proposed AOSMA-MLP approach shows significant potential for yielding effective outcomes in various regression problems.

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“…Accordingly, applicability of the AI techniques in the form of artificial neural network, machine/deep learning, evolutionary algorithms, and hybrid structures in producing the predictive 3D subsurface models have been highlighted [46][47][48][49][50][51]. Due to characterized features in creating transferable solutions and learnability from high-level data attributes [52] the feasibility of AI techniques in geothermal modeling [53,54] and compared performance by field prospecting methods [55,56] have been notified in several studies dealing with predicting the location of hot spot structures [57][58][59][60], estimating the temperature distribution [61,62], and potential of geothermal production associated with geological data [63,64].…”

Section: Introductionmentioning

confidence: 99%

Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

et al. 2022

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Mapping the subsurface temperatures can efficiently lead to identifying the geothermal distribution heat flow and potential hot spots at different depths. In this paper, an advanced adaptive multitask deep learning procedure for 3D spatial mapping of the subsurface temperature was proposed. As a result, predictive 3D spatial subsurface temperatures at different depths were successfully generated using geolocation of 494 exploratory boreholes data in Catalonia (Spain). To increase the accuracy of the achieved results, hybridization with a new modified firefly algorithm was carried out. Subsequently, uncertainty analysis using a novel automated ensemble deep learning approach for the predicted temperatures and generated spatial 3D maps were executed. Comparing the accuracy performances in terms of correct classification rate (CCR) and the area under the precision–recall curves for validation and whole datasets with at least 4.93% and 2.76% improvement indicated for superiority of the hybridized model. According to the results, the efficiency of the proposed hybrid multitask deep learning in 3D geothermal characterization to enhance the understanding and predictability of subsurface spatial distribution of temperatures is inferred. This implies that the applicability and cost effectiveness of the adaptive procedure in producing 3D high resolution depth dependent temperatures can lead to locate prospective geothermally hotspot active regions.

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Machine learning enhancement of thermal response tests for geothermal potential evaluations at site and regional scales

Cited by 23 publications

References 7 publications

Linking technical geothermal potential from borehole heat exchangers and heating demand scenarios on a regional scale

Linking technical geothermal potential from borehole heat exchangers and heating demand scenarios on a regional scale

AOSMA-MLP: A Novel Method for Hybrid Metaheuristics Artificial Neural Networks and a New Approach for Prediction of Geothermal Reservoir Temperature

Generating 3D Geothermal Maps in Catalonia, Spain Using a Hybrid Adaptive Multitask Deep Learning Procedure

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