A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification

Zhou, Kaibo; Zhang, Jianyu; Ren, Yusong; Huang, Zhen; Zhao, Luanxiao

doi:10.1190/geo2019-0429.1

Cited by 85 publications

(16 citation statements)

References 45 publications

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“…The following are the steps in the GBDT technique process 63 : Suppose that is a constant Evaluate the and training function Obtain parameter and modify the function: …”

Section: Models’ Implementationmentioning

confidence: 99%

Modeling solubility of CO2–N2 gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state

Nakhaei-Kohani

Taslimi-Renani

Hadavimoghaddam

et al. 2022

Sci Rep

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Determining the solubility of non-hydrocarbon gases such as carbon dioxide (CO2) and nitrogen (N2) in water and brine is one of the most controversial challenges in the oil and chemical industries. Although many researches have been conducted on solubility of gases in brine and water, very few researches investigated the solubility of power plant flue gases (CO2–N2 mixtures) in aqueous solutions. In this study, using six intelligent models, including Random Forest, Decision Tree (DT), Gradient Boosting-Decision Tree (GB-DT), Adaptive Boosting-Decision Tree (AdaBoost-DT), Adaptive Boosting-Support Vector Regression (AdaBoost-SVR), and Gradient Boosting-Support Vector Regression (GB-SVR), the solubility of CO2–N2 mixtures in water and brine solutions was predicted, and the results were compared with four equations of state (EOSs), including Peng–Robinson (PR), Soave–Redlich–Kwong (SRK), Valderrama–Patel–Teja (VPT), and Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT). The results indicate that the Random Forest model with an average absolute percent relative error (AAPRE) value of 2.8% has the best predictions. The GB-SVR and DT models also have good precision with AAPRE values of 6.43% and 7.41%, respectively. For solubility of CO2 present in gaseous mixtures in aqueous systems, the PC-SAFT model, and for solubility of N2, the VPT EOS had the best results among the EOSs. Also, the sensitivity analysis of input parameters showed that increasing the mole percent of CO2 in gaseous phase, temperature, pressure, and decreasing the ionic strength increase the solubility of CO2–N2 mixture in water and brine solutions. Another significant issue is that increasing the salinity of brine also has a subtractive effect on the solubility of CO2–N2 mixture. Finally, the Leverage method proved that the actual data are of excellent quality and the Random Forest approach is quite reliable for determining the solubility of the CO2–N2 gas mixtures in aqueous systems.

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“…The following are the steps in the GBDT technique process 63 : Suppose that is a constant Evaluate the and training function Obtain parameter and modify the function: …”

Section: Models’ Implementationmentioning

confidence: 99%

Modeling solubility of CO2–N2 gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state

Nakhaei-Kohani

Taslimi-Renani

Hadavimoghaddam

et al. 2022

Sci Rep

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“…Notably, these minority lithofacies should not be ignored. Therefore, there is a need to mitigate the data imbalance problem in logging datasets (Hu and Sun, 2020;Kim and Byun, 2020;Zhou et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China

Zheng

Zhang²,

Cheng³

et al. 2023

Front. Earth Sci.

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The strong heterogeneity characteristics of deep-buried clastic low-permeability reservoirs may lead to great risks in hydrocarbon exploration and development, which makes the accurate identification of reservoir lithofacies crucial for improving the obtained exploration results. Due to the very limited core data acquired from deep drilling, lithofacies logging identification has become the most important method for comprehensively obtaining the rock information of deep-buried reservoirs and is a fundamental task for carrying out reservoir characterization and geological modeling. In this study, a machine learning method is introduced to lithofacies logging identification, to explore an accurate lithofacies identification method for deep fluvial-delta sandstone reservoirs with frequent lithofacies changes. Here Sangonghe Formation in the Central Junggar Basin of China is taken as an example. The K-means-based synthetic minority oversampling technique (K-means SMOTE) is employed to solve the problem regarding the imbalanced lithofacies data categories used to calibrate logging data, and a probabilistic calibration method is introduced to correct the likelihood function. To address the situation in which traditional machine learning methods ignore the geological deposition process, we introduce a depositional prior for controlling the vertical spreading process based on a Markov chain and propose an improved Bayesian inversion process for training on the log data to identify lithofacies. The results of a series of experiments show that, compared with the traditional machine learning method, the new method improves the recognition accuracy by 20%, and the predicted petrographic vertical distribution results are consistent with geological constraints. In addition, SMOTE and probabilistic calibration can effectively handle data imbalance problems so that different categories can be adequately learned. Also the introduction of geological prior has a positive impact on the overall distribution, which significantly improves the accuracy and recall rate of the method. According to this comprehensive analysis, the proposed method greatly enhanced the identification of the lithofacies distributions in the Sangonghe Formation. Therefore, this method can provide a tool for logging lithofacies interpretation of deep and strongly heterogeneous clastic reservoirs in fluvial-delta and other depositional environments.

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“…To address this issue, researchers developed the concept of ensemble learning, which allows for results that are more accurate with lower variance and errors. Thus, commonly, ensemble learning relies on bagging, stacking, or boosting approaches [31] to combine predictions from base models by voting and averaging them to improve the result. Although the latter has recently succeeded in many fields, no application of DL ensemble as a seismic inversion method has been reported, and in contrast to the common ensemble learningrelated techniques, no other approach has been performed.…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

et al. 2023

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Underground porosity is important in many earth sciences and engineering fields, including hydrocarbon reservoir characterization and geothermal energy production. Popular methods largely rely on the analysis of lithological core data, well logs, and seismic inversion methods. While these methods are reliable, they are also time-consuming, expensive, and difficult to implement. In addition, seismic inversion has nonlinearity, data dimensionality, and non-uniqueness issues. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability by mapping directly from seismic attributes to underground porosity. Therefore, we trained several DL models with different optimization functions. In the training steps, we labelled every seismic attribute data point with its corresponding porosity derived from the welllogs. In contrast to popular ensemble techniques, we proposed a weighted prediction approach based on the strengths of each model. Testing results showed a coefficient of determination (R 2 ) of 0.94345 and a Pearson's correlation coefficient of 0.9725 between the actual model and the model of the proposed approach, versus 0.9681 and 0.9716 for the best single and popular ensemble models, respectively. Further, we tested the effectiveness of our method using real seismic data from the North Sea. With a Pearson's correlation value of 0.9743, the inverted model ranges from 27 to 35%, compared to the reference model, which has an overall range of 20 to 33%. These results provide insights into the potential of the proposed method and its applicability to any other seismic volume to determine spatially varying underground porosity from seismic attributes directly.INDEX TERMS Deep neural networks, ensemble methods, seismic inversion, reservoir properties.

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A gradient boosting decision tree algorithm combining synthetic minority oversampling technique for lithology identification

Cited by 85 publications

References 45 publications

Modeling solubility of CO2–N2 gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state

Modeling solubility of CO2–N2 gas mixtures in aqueous electrolyte systems using artificial intelligence techniques and equations of state

Lithofacies logging identification for strongly heterogeneous deep-buried reservoirs based on improved Bayesian inversion: The Lower Jurassic sandstone, Central Junggar Basin, China

Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

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