Application of Artificial Intelligence in Forecasting Geothermal Production

Wardoyo, Galih Kusumo; Pratama, Heru Berian; Sutopo, Sutopo; Ashat, Ali; Yudhistira, Yodha

doi:10.1088/1755-1315/732/1/012022

Cited by 7 publications

(4 citation statements)

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“…By eliminating assumptions, Wardoyo et al (2021) undertook a study aimed to establish new approaches for optimizing and estimating geothermal production. Artificial intelligence (AI) is an alternate way to predict reservoir productions with high degrees of uncertainty and optimize production.…”

Section: Introductionmentioning

confidence: 99%

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Application of Artificial Intelligence in Static Formation Temperature Estimation

Al-Fakih

Kaka

2023

Arab J Sci Eng

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This study presents a new model to predict the static formation temperature at multiple oil fields using multiple machine learning algorithms. Results are compared with the real temperatures obtained from two wells. The model developed in this study predicts static formation temperature according to several machine learning algorithms including artificial neural networks, fuzzy logic, k-nearest neighbors, and random forest algorithms. The following are the key findings: various geothermal wells in distinct fields or formations may have different machine learning connections. However, if a connection is defined using adequate field data, it is clear that static formation temperature can be approximated with great accuracy. Based on machine learning models we developed a novel model for forecasting static formation temperature, examined the modeling data and outcomes, and found that Random Forest, Fuzzy logic, and K-nearest neighbors outperformed Artificial Neural Network. Therefore, the performance of the proposed model for estimating static well temperature achieved a mean absolute percentage (AAPE) of 0.003%, and the coefficient of determination (R 2 ) is 0.99%. When the classical (mathematical) methods were compared to the artificial intelligence methods, the artificial intelligence methods produced more accurate results with varying percentages. The findings generated by the unique new model computation and the measured test data are substantially matched when compared to computed data and observed temperature data. The novelty of this newly developed AI model is that it will serve as a practical and inexpensive tool for SFT determination in geothermal and petroleum wells.

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Section: Introductionmentioning

confidence: 99%

“…Artificial intelligence (AI) is an alternate way to predict reservoir productions with high degrees of uncertainty and optimize production. The AI model was built using production measurement data (Wardoyo et al 2021).…”

Section: Introductionmentioning

confidence: 99%

Application of Artificial Intelligence in Static Formation Temperature Estimation

Al-Fakih

Kaka

2023

Arab J Sci Eng

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“…Many GSMs, especially warm and hot springs, geysers, and fumaroles, have drawn plenty of attention from the geothermal community in recent decades. Many researchers investigated geothermal anomalies related to GSMs such as hot springs using geophysics [9][10][11], geochemistry [12][13][14], remote sensing [15][16][17][18][19], Geographic Information System (GIS) [20], statistical modeling [21] and conventional Machine Learning (ML) [6,22,23]. For example, Gentana et al (2019) demonstrated that the fault system is correlated with the appearances of the GSMs in the Indonesia volcanic zone [24]; Freski et al (2021) tested the effects of alteration degree, moisture, and temperature on laser return intensity for the GSMs.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Preprint

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Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using Deep Learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three Machine Learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of Overall Accuracy (OA), Overall F1 score (OF) and Computational Time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers with the highest OA and the biggest OF for both validation and test, and the fastest CT for both validation and test. On the contrary, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. It suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain.

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“…A lot of GSMs, especially warm and hot springs, geysers, and fumaroles, have drawn plenty of attention from the geothermal community in recent decades. Many researchers investigated geothermal anomalies related to GSMs such as hot springs using geophysics [9][10][11], geochemistry [12][13][14], remote sensing [15][16][17][18][19], geographic information system (GIS) [20], statistical modeling [21] and conventional machine learning (ML) [6,22,23]. For example, Gentana et al (2019) demonstrated that the fault system is correlated with the appearances of the GSMs in the Indonesia volcanic zone [24]; Freski et al (2021) tested the effects of alteration degree, moisture, and temperature on laser return intensity for the GSMs.…”

Section: Introductionmentioning

confidence: 99%

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Xiong

Zhu

et al. 2022

Energies

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Geothermal surface manifestations (GSMs) are direct clues towards hydrothermal activities of a geothermal system in the subsurface and significant indications for geothermal resource exploration. It is essential to recognize various GSMs for potential geothermal energy exploration. However, there is a lack of work to fulfill this task using deep learning (DL), which has achieved unprecedented successes in computer vision and image interpretation. This study aims to explore the feasibility of using a DL model to fulfill the recognition of GSMs with photographs. A new image dataset was created for the GSM recognition by preprocessing and visual interpretation with expert knowledge and a high-quality check after downloading images from the Internet. The dataset consists of seven GSM types, i.e., warm spring, hot spring, geyser, fumarole, mud pot, hydrothermal alteration, crater lake, and one type of none GSM, including 500 images of different photographs for each type. The recognition results of the GoogLeNet model were compared with those of three machine learning (ML) algorithms, i.e., Support Vector Machine (SVM), Decision Tree (DT), and K-Nearest Neighbor (KNN), by using the assessment metrics of overall accuracy (OA), overall F1 score (OF), and computational time (CT) for training and testing the models via cross-validation. The results show that the retrained GoogLeNet model using transfer learning has significant advantages of accuracies and performances over the three ML classifiers, with the highest OA, the biggest OF, and the fastest CT for both the validation and test. On the contrary, the three selected ML classifiers perform poorly for this task due to their low OA, small OF, and long CT. This suggests that transfer learning with a pretrained network be a feasible method to fulfill the recognition of the GSMs. Hopefully, this study provides a reference paradigm to help promote further research on the application of state-of-the-art DL in the geothermics domain.

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Application of Artificial Intelligence in Forecasting Geothermal Production

Cited by 7 publications

References 0 publications

Application of Artificial Intelligence in Static Formation Temperature Estimation

Application of Artificial Intelligence in Static Formation Temperature Estimation

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

Recognition of Geothermal Surface Manifestations: A Comparison of Machine Learning and Deep Learning

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