Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm

Ao, Yile; Li, Hongqi; Zhu, Lei; Ali, Sikandar; Yang, Zhongguo

doi:10.1016/j.petrol.2018.10.048

Cited by 49 publications

(12 citation statements)

References 52 publications

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“…This method was introduced by Breiman in 2001 utilizing ensemble trees to predict the target variable which is calculated as the average of the predictions of the individual regression trees in the ensemble . Also, the part of the training data that is not taken by the bootstrap sampling to build the tree is defined as the out-of-bag (OOB) sample which is used for incorporating a validation step within the fitting procedure. − These OOB errors are the estimation errors when the tuned RF network is employed in the OOB samples . RF is based on the randomness of various kinds of decision trees generated from different data subpools.…”

Section: Development Of Intelligent Modelsmentioning

confidence: 99%

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Zheng¹,

Mahmoudzadeh

Amiri-Ramsheh

et al. 2023

ACS Omega

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Carbon dioxide (CO 2 ) has an essential role in most enhanced oil recovery (EOR) methods in the oil industry. Oil swelling and viscosity reduction are the dominant mechanisms in an immiscible CO 2 -EOR process. Besides numerous CO 2 applications in EOR, most oil reservoirs do not have access to natural CO 2 , and capturing it from flue gas and other sources is costly. Flue gases are available in huge quantities at a significantly lower price and can be considered economically viable agents for EOR operations. In this work, four powerful machine learning algorithms, namely, extra tree (ET), random forest (RF), gradient boosting (GBoost), and light gradient boosted machine (LightGBM) were utilized to accurately estimate the viscosity of CO 2 −N 2 mixtures. To this aim, a databank was employed, containing 3036 data points over wide ranges of pressures and temperatures. Temperature, pressure, and CO 2 mole fraction were applied as input parameters, and the viscosity of the CO 2 −N 2 mixture was the output. The RF smart model had the highest precision with the lowest average absolute percent relative error (AAPRE) of 1.58%, root mean square error (RMSE) of 2.221, and determination coefficient (R 2 ) of 0.9993. The trend analysis showed that the RF model could precisely predict the real physical behavior of the CO 2 −N 2 viscosity variation. Finally, the outlier detection was performed using the leverage approach to demonstrate the validity of the utilized databank and the applicability area of the developed RF model. Accordingly, nearly 96% of the data points seemed to be dependable and valid, and the rest of them were located in the suspected and out-of-leverage data zones. 22

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Section: Development Of Intelligent Modelsmentioning

confidence: 99%

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Zheng¹,

Mahmoudzadeh

Amiri-Ramsheh

et al. 2023

ACS Omega

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“…Random Forests is now successfully used in seismology for automated source classification (Provost et al, 2017;Hibert et al, 2017c;Maggi et al, 2017;Malfante et al, 2018;Hibert et al, 2019;Ao et al, 2019;Pérez et al, 2020;Wenner et al, 2021;Chmiel et al, 2021). However the Random Forests algorithm can also be used to estimate continuous values and thus perform regression analyses.…”

Section: Machine Learning: Using Random Forests As a Regression Toolmentioning

confidence: 99%

Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate

Noël¹,

Toe

Talib

et al. 2022

Preprint

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Abstract. Understanding the dynamics of slope instabilities is critical to mitigate the associated hazards but their direct observation is often difficult due to their remote locations and their spontaneous nature. Seismology allows to get unique information on these events, including on their dynamics. However, the link between the properties of these events (mass and kinematics) and the seismic signals generated are still poorly understood. we conducted a controlled rockfall experiment in the Riou-Bourdoux torrent (South French Alps) to try to better decipher those links. We deployed a dense seismic network and inferred the dynamics of the block from the reconstruction of the 3D trajectory from terrestrial and airborne high-resolution stereo-photogrammetry. We propose a new approach based on machine learning to predict the mass and the velocity of each block. Our results show that we can predict those quantities with average errors of approximately 10 % for the velocity and 25 % for the mass. These accuracies are as good as or better than those obtained by other approaches, but our approach has the advantage of not requiring to localize the source and an a priori knowledge of the environment, nor of making a strong assumption on the seismic wave attenuation model. Finally, the machine learning approach allows us to explore more widely the correlations between the features of the seismic signal generated by the rockfalls and their physical properties, and might eventually lead to better constrain the physical models in the future.

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“…Our work proposes to use outlier detection to exclude data errors. Ao et al (2019a) also proposed a pruning random forest (PRF) to identify sand-body from seismic attributes in the western Bohai Sea of China. Results show that PRF has better predictive performance and robustness.…”

Section: Related Workmentioning

confidence: 99%

A Coarse-to-Fine Approach for Intelligent Logging Lithology Identification with Extremely Randomized Trees

Xie

Zhu

et al. 2020

Math Geosci

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Lithology identification is vital for reservoir exploration and petroleum engineering. Recently, there has been growing interest in using an intelligent logging approach for lithology classification. Machine learning has emerged as a powerful tool in inferring lithology types with the logging curves. However, well logs are susceptible to logging parameter manual entry, borehole conditions and tool calibrations. Most studies in the field of lithology classification with machine learning approaches have focused only on improving the prediction accuracy of classifiers. Also, a model trained in one location is not reusable in a new location due to different data distributions. In this paper, a unified framework is provided for training a multi-class lithology classification model for a data set with outlier data. In this paper, a coarse-to-fine framework that combines outlier detection, multi-class classification with an extremely randomized tree-based classifier is proposed to solve these issues. An unsupervised learning approach is used to detect the outliers in the data set. Then a coarse-to-fine inference procedure is used to infer the lithology class with an extremely randomized tree classifier. Two real-world data sets of well-logging are used to demonstrate the effectiveness of the proposed framework. Comparisons are conducted with some baseline machine learning classifiers, namely random forest, gradient tree boosting, and xgboosting. Results show that the proposed framework has higher prediction accuracy in sandstones compared with other approaches.

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Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm

Cited by 49 publications

References 52 publications

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate

A Coarse-to-Fine Approach for Intelligent Logging Lithology Identification with Extremely Randomized Trees

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Identifying channel sand-body from multiple seismic attributes with an improved random forest algorithm

Cited by 49 publications

References 52 publications

Modeling Viscosity of CO2–N2 Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Modeling Viscosity of CO2–N2 Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Machine learning prediction of the mass and the velocity of controlled single-block rockfalls from the seismic waves they generate

A Coarse-to-Fine Approach for Intelligent Logging Lithology Identification with Extremely Randomized Trees

Contact Info

Product

Resources

About

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM

Modeling Viscosity of CO₂–N₂ Gaseous Mixtures Using Robust Tree-Based Techniques: Extra Tree, Random Forest, GBoost, and LightGBM