Real-time prediction of bottom-hole circulating temperature in geothermal wells using machine learning models

Khaled, Mohamed Shafik; Wang, Ningyu; Ashok, Pradeepkumar; van Oort, Eric; Wisian, Ken

doi:10.1016/j.geoen.2024.212891

Cited by 3 publications

(1 citation statement)

References 28 publications

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“…In 2023, Somi et al amalgamated hidden Markov model-based clustering with XGBoost to identify sleeve incidents [40]. In 2024, Khaled et al reliably predicted bottom-hole circulating temperature under constant conditions with XGBoost [41].…”

Section: Introductionmentioning

confidence: 99%

Production Feature Analysis of Global Onshore Carbonate Oil Reservoirs Based on XGBoost Classier

Qi,

Liu

2024

Processes

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Carbonate reservoirs account for 60% of global reserves for oil, making them one of the most important types of sedimentary rock reservoirs for petroleum production. This study aimed to identify key production features that significantly impact oil production rates, enhancing reservoir management and optimizing production strategies. A comprehensive dataset is built from reserves and production history data of 377 onshore carbonate oilfields globally, encompassing features such as production, recovery rate, and recovery degree of the whole lifecycle of an oilfield. XGBoost classifier is trained by K-fold cross-validation and its hyperparameters are optimized by Optuna optimization framework. The results show that XGBoost has the best performance evaluated with metrics including accuracy, precision, recall, and F1 score comparing with decision tree, random forest, and support vector machine. Key production features are identified by analyzing the classification feature importance of XGBoost classifier, including build-up stage cumulative production, plateau stage cumulative production, plateau stage recovery rate, plateau stage recovery degrees, and peak production. In conclusion, oilfield reserve size, build-up stage cumulative production, plateau stage cumulative production, and peak production increase, while plateau stage recovery rate decreases, and the plateau stage recovery degree of small-sized oilfields is slightly greater than that of moderate and large oilfields. The research methodology of this study can serve as a reference for studying production features of other types of oil and gas reservoirs. By applying the methodology to low-permeability oilfields, this paper concludes the key production features that are as follows: low-permeability oilfields generally have lower peak recovery rate, lower plateau stage recovery rate, lower decline stage recovery degree, and lower decline stage recovery rate, along with a wide but generally lower range of decline stage cumulative production compared to conventional oilfields.

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Section: Introductionmentioning

confidence: 99%

Production Feature Analysis of Global Onshore Carbonate Oil Reservoirs Based on XGBoost Classier

Qi,

Liu

2024

Processes

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Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs

Gasmi,

Abed,

Dutta

et al. 2024

Case Studies in Thermal Engineering

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Progress of Gas Injection EOR Surveillance in the Bakken Unconventional Play—Technical Review and Machine Learning Study

Zhao,

Jin,

et al. 2024

Energies

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Although considerable laboratory and modeling activities were performed to investigate the enhanced oil recovery (EOR) mechanisms and potential in unconventional reservoirs, only limited research has been reported to investigate actual EOR implementations and their surveillance in fields. Eleven EOR pilot tests that used CO2, rich gas, surfactant, water, etc., have been conducted in the Bakken unconventional play since 2008. Gas injection was involved in eight of these pilots with huff ‘n’ puff, flooding, and injectivity operations. Surveillance data, including daily production/injection rates, bottomhole injection pressure, gas composition, well logs, and tracer testing, were collected from these tests to generate time-series plots or analytics that can inform operators of downhole conditions. A technical review showed that pressure buildup, conformance issues, and timely gas breakthrough detection were some of the main challenges because of the interconnected fractures between injection and offset wells. The latest operation of co-injecting gas, water, and surfactant through the same injection well showed that these challenges could be mitigated by careful EOR design and continuous reservoir monitoring. Reservoir simulation and machine learning were then conducted for operators to rapidly predict EOR performance and take control actions to improve EOR outcomes in unconventional reservoirs.

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Real-time prediction of bottom-hole circulating temperature in geothermal wells using machine learning models

Cited by 3 publications

References 28 publications

Production Feature Analysis of Global Onshore Carbonate Oil Reservoirs Based on XGBoost Classier

Production Feature Analysis of Global Onshore Carbonate Oil Reservoirs Based on XGBoost Classier

Heat recovery integration in a hybrid geothermal-based system producing power and heating using machine learning approach to maximize outputs

Progress of Gas Injection EOR Surveillance in the Bakken Unconventional Play—Technical Review and Machine Learning Study

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