Application of an artificial neural network in predicting the effectiveness of trapping mechanisms on CO2 sequestration in saline aquifers

Song, Youngsoo; Sung, Wonmo; Jang, Young Ho; Jung, Wonjin

doi:10.1016/j.ijggc.2020.103042

Cited by 44 publications

(20 citation statements)

References 33 publications

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“…How to obtain clinically satisfactory and diagnostic images while reducing the dose is the goal of our research. Through retrospective analysis of some cases in our hospital in recent two years, the image quality and radiation dose are obtained under different scanning machines and different scanning modes to further explore the clinical value of Xikouzi dual-source Flash scan in the diagnosis of infant bronchial foreign body [ 2 ]. Children are under five years of age due to the imperfect development of many functions such as chewing and laryngeal reflex.…”

Section: Introductionmentioning

confidence: 99%

Application of the Artificial Neural Network in the Diagnosis of Infantile Bronchial Bridge with 64-Slice Multislice Spiral CT

Chang

Lin

et al. 2021

Journal of Healthcare Engineering

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The objective is to study the application of spiral CT in the diagnosis of the trachea in children. In this study, the effect of 64-slice multislice spiral CT in diagnosing infant bronchial bridge was studied based on an artificial neural network. From June 2020 to December 2020, 100 cases of children with the trachea in the outpatient department of our hospital were selected as the research object. They were divided into the study group and the control group, with 50 cases in each group. The results showed that among the 50 cases in the control group, 42 cases were found to have a bronchial foreign body and 8 cases were missed; the detection rate was 84%. There were 7 cases of trachea foreign body, 15 cases of left bronchial foreign body, 14 cases of right bronchial foreign body, and 6 cases of medium bronchial foreign body. The detection rate of the study group was significantly higher than that of the control group, with a statistical significance ( P < 0.05 ). Conclusion. The detection rate of neural networks in MSCT is higher than that of X-ray, and the MSCT based on the artificial neural network can clearly show the morphology, position, and the relationship between the foreign body and trachea, which is worthy of clinical promotion and application.

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Section: Introductionmentioning

confidence: 99%

Application of the Artificial Neural Network in the Diagnosis of Infantile Bronchial Bridge with 64-Slice Multislice Spiral CT

Chang

Lin

et al. 2021

Journal of Healthcare Engineering

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“…The first step of developing any machine learning model is gathering the datasets. Here we collected 1509 simulation samples from reliable published literature (Ajayi et al, 2019;Alkhdheeawi et al, 2018;Al-Khdheeawi et al, 2018b, 2018a, 2018cAnchliya et al, 2012;Foroozesh et al, 2018;Hsieh et al, 2013;Jin et al, 2012;Jun et al, 2019;Khudaida and Das, 2020;Kim et al, 2019;Kumar et al, 2005;Lee et al, 2010;Liner et al, 2011;Liu et al, 2020;Mohajeri and Shariatipour, 2019;L Nghiem et al, 2009;Noushabadi et al, 2018;Pham et al, 2013;Sifuentes et al, 2009;Song et al, 2020;Sung et al, 2014;Vo Thanh et al, 2020b;Xiao et al, 2019;Zapata et al, 2020). The study area of collected data was conducted around the world, including Sleipner (Norway), Shenhua (China), Taiwan, Cuu Long Basin (Vietnam), Gorge V (Korea), Kansas (USA), Ketzin (Germany), Abu Dhabi, Israel, and UK.…”

Section: Data Preparationmentioning

confidence: 99%

“…CO2 storage in deep saline formations could be evaluated by conventional simulation or analytical methods (Aminu et al, 2017;Bachu, 2008;Zapata et al, 2020). Also, CO2 sequestration for field-scale simulation considered many data types such as geological background, petrophysical properties, and reservoir characterization (Song et al, 2020). To forecast storage efficiency and estimate field candidates' feasibility, the field study has been investigated in two approaches.…”

Section: Introductionmentioning

confidence: 99%

Predicting CO2 Trapping Efficiency In Saline Aquifers By Machine Learning System: Implication To Carbon Sequestration

Thanh¹,

Lee²

2021

Preprint

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Carbon dioxide (CO2) storage in saline formations has been identified as a practical approach to reducing CO2 levels in the atmosphere. The residual and solubility of CO2 in deep saline aquifers are essential mechanisms to enhance security in storing CO2. In this research, CO2 residual and solubility in saline formations have been predicted by adapting three Machine Learning models called Random Forest (RF), extreme gradient boosting (XGboost), and Support Vector Regression (SVR). Consequently, a diversity of the field-scale simulation database including 1509 data samples retrieved from reliable studies, was considered to train and test the proposed models to achieve this task. Graphical and statistical indicators were evaluated and compared the predictive ML model performance. The predicted results denoted that the proposed ML models are ranked from high to low as follows: XGboost>RF>SVR. Additionally, the performance analyses revealed that the XGboost model demonstrates higher accuracy in predicting CO2 trapping efficiency in saline formation than previous ML models. The XGboost model yields very low root mean square error (RMSE) and R2 for both residual and solubility trapping efficiency. At last, the applicable domain of XGboost model was validated, and only 24 suspected data points were recognized from the entire databank.

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“…Regarding the application of machine learning tools to sever as fast proxy models of high-fidelity reservoir simulation using regression approach 41 , artificial neural network 42 . Besides machine learning approach was supported for other reservoir engineering problems such as history matching 43 , reservoir characterization 44 .…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

Thanh

Sugai

Sasaki

2020

Sci Rep

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Residual Oil Zones (ROZs) become potential formations for Carbon Capture, Utilization, and Storage (CCUS). Although the growing attention in ROZs, there is a lack of studies to propose the fast tool for evaluating the performance of a CO2 injection process. In this paper, we introduce the application of artificial neural network (ANN) for predicting the oil recovery and CO2 storage capacity in ROZs. The uncertainties parameters, including the geological factors and well operations, were used for generating the training database. Then, a total of 351 numerical samples were simulated and created the Cumulative oil production, Cumulative CO2 storage, and Cumulative CO2 retained. The results indicated that the developed ANN model had an excellent prediction performance with a high correlation coefficient (R2) was over 0.98 on comparing with objective values, and the total root mean square error of less than 2%. Also, the accuracy and stability of ANN models were validated for five real ROZs in the Permian Basin. The predictive results were an excellent agreement between ANN predictions and field report data. These results indicated that the ANN model could predict the CO2 storage and oil recovery with high accuracy, and it can be applied as a robust tool to determine the feasibility in the early stage of CCUS in ROZs. Finally, the prospective application of the developed ANN model was assessed by optimization CO2-EOR and storage projects. The developed ANN models reduced the computational time for the optimization process in ROZs.

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Application of an artificial neural network in predicting the effectiveness of trapping mechanisms on CO2 sequestration in saline aquifers

Cited by 44 publications

References 33 publications

Application of the Artificial Neural Network in the Diagnosis of Infantile Bronchial Bridge with 64-Slice Multislice Spiral CT

Application of the Artificial Neural Network in the Diagnosis of Infantile Bronchial Bridge with 64-Slice Multislice Spiral CT

Predicting CO2 Trapping Efficiency In Saline Aquifers By Machine Learning System: Implication To Carbon Sequestration

Application of artificial neural network for predicting the performance of CO2 enhanced oil recovery and storage in residual oil zones

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