Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs

Yu, J.; Zhu, Linqi; Qin, Ruibao; Zhang, Zhansong; Li, Li; Huang, Tianzi

doi:10.1155/2021/9321565

Cited by 13 publications

(4 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Hydrate saturation was used as a comparison parameter to evaluate the effectiveness of the classification method. Parameters such as the clay content, porosity, and grain size, which are commonly used in previous literature to reflect rock and physical properties, were used as input parameters for classification using the K-means clustering algorithm (Yu et al, 2021). These data were sourced from previous articles (Deng et al, 2021;Lai et al, 2021;Wei et al, 2021).…”

Section: Reservoir Classification Methods and Modelling Processesmentioning

confidence: 99%

Reservoir classification and log prediction of gas hydrate occurrence in the Qiongdongnan Basin, South China Sea

et al. 2023

Self Cite

View full text Add to dashboard Cite

Classifying natural gas hydrate reservoirs effectively and carrying out reservoir classification modelling is crucial, but to date, research on building artificial intelligence-assisted logging curve reservoir classification models is not abundant. As exploration and development have progressed, an increasing number of fine-grained reservoirs are being discovered, and their strong heterogeneity makes correct reservoir classification even more important. Two wells used for detecting hydrates in the Qiongdongnan (QDN) Basin are used to explore the relationship between logging response parameters and reservoir quality, as well as the method of building a logging-based reservoir classification model. Through K-means clustering and Adaboost methods, the K-means method is considered to be able to correspond to the hydrate enrichment degree, while the random forest method can establish an effective reservoir classification model (the recognition accuracy is 95%). In the different categories of reservoirs, the physical properties of the reservoirs are obviously poor, and the corresponding hydrate saturation is also low, which indicates that heterogeneity has indeed affected the enrichment of hydrates in fine-grained reservoirs. This reservoir classification research method can effectively recognize reservoirs.

show abstract

Section: Reservoir Classification Methods and Modelling Processesmentioning

confidence: 99%

Reservoir classification and log prediction of gas hydrate occurrence in the Qiongdongnan Basin, South China Sea

et al. 2023

Self Cite

View full text Add to dashboard Cite

show abstract

“…The use of KM clustering to divide the data into categories helps to address the issue of poor relationship between logging response and blood flow, by grouping similar data points based on their characteristics. The RF or SVM algorithm is then applied to each category separately, allowing for a more accurate prediction of blood flow based on the specific features of each group [ 22 ].…”

Section: Methodsmentioning

confidence: 99%

Multi-exposure Laser Speckle Contrast Imaging (MECI)-Based Prediction of Blood Flow Using Random Forest (RF) With K-Means (KM)

Jain¹,

Gupta²

2023

Cureus

View full text Add to dashboard Cite

Blood flow prediction is very important for medical diagnosis, drug development, tissue engineering, and continuous monitoring. One commonly used method for studying blood flow is called multi-exposure laser speckle contrast imaging (MECI). It provides valuable insights into how blood flows through tissues and helps in diagnosing circulatory diseases. In our study, we used MECI to measure blood flow in real-time by taking multiple measurements with different exposure times and contrasts. To predict different blood flow rates ranging from 0.1 to 1 mm/s, we employed machine learning (ML) techniques like clustering and random forest (RF) or support vector machine (SVM) algorithms. The study showed that RF with K-means performance is found to be the most accurate technique for flow classification, with an accuracy of 98.5%, a precision of 92%, a specificity of 98.9%, and a classification error of 1.5%. Our study demonstrates that employing clustering and RF algorithms in MECI provides a robust and effective approach to predicting blood flow. This technique holds great potential for a wide range of applications in the medical and healthcare fields.

show abstract

“…Subsequently, the EL algorithm can be applied to each category independently, resulting in more precise blood flow predictions that account for the specific attributes of each group. Figure 3 shows the modelling and forecasting flow process of the algorithm [31].…”

Section: Machine Learningmentioning

confidence: 99%

Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering

Jain,

Gupta

2024

Biomed. Phys. Eng. Express

View full text Add to dashboard Cite

Purpose: Accurately visualizing and measuring blood flow is of utmost importance in maintaining optimal health and preventing the onset of various chronic diseases. One promising imaging technique that aids in visualizing perfusion in biological tissues is Multi-exposure Laser Speckle Contrast Imaging (MELSCI). MELSCI technique allows real-time quantitative measurements using multiple exposure times to obtain precise and reliable blood flow data. Additionally, the application of machine learning (ML) techniques can further enhance the accuracy of blood flow prediction in this imaging modality. Method: Our study focused on developing and evaluating Ensemble Learning ML techniques along with clustering algorithms for predicting blood flow rates in MELSCI. The effectiveness of these techniques was assessed using performance parameters, including accuracy, F1-score, precision, recall, specificity, and classification error rate. Result: Notably, the study revealed that Ensemble Learning with clustering emerged as the most accurate technique, achieving an impressive accuracy rate of 98.5%. Furthermore, it demonstrated a high recall of more than 91%, F1-score, the precision of more than 90%, higher specificity of 99%, and least classification error of 1.5%, highlighting its suitability and sustainability for flow prediction in MELSCI.Conclusion: The study's findings imply that Ensemble Learning can significantly contribute to enhancing the accuracy of blood flow prediction in MELSCI. This advancement holds substantial promise for healthcare professionals and researchers, as it facilitates improved understanding and assessment of perfusion within biological tissues, which will contribute to the maintenance of good health and prevention of chronic diseases.

show abstract

Combining K-Means Clustering and Random Forest to Evaluate the Gas Content of Coalbed Bed Methane Reservoirs

Cited by 13 publications

References 16 publications

Reservoir classification and log prediction of gas hydrate occurrence in the Qiongdongnan Basin, South China Sea

Reservoir classification and log prediction of gas hydrate occurrence in the Qiongdongnan Basin, South China Sea

Multi-exposure Laser Speckle Contrast Imaging (MECI)-Based Prediction of Blood Flow Using Random Forest (RF) With K-Means (KM)

Enhancing blood flow prediction in multi-exposure laser speckle contrast imaging through ensemble learning with K-mean clustering

Contact Info

Product

Resources

About