Machine learning (ML) for fluvial lithofacies identification from well logs: A hybrid classification model integrating lithofacies characteristics, logging data distributions, and ML models applicability

Jiang, Shiyi; Sun, Panke; Lyu, Fengqing; Zhu, Sicheng; Zhou, Ruifeng; Li, Bin; He, Taihong; Lin, Yujian; Gao, Yining; Song, Wendan; Xu, Huaimin

doi:10.1016/j.geoen.2023.212587

Cited by 3 publications

(1 citation statement)

References 73 publications

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“…48,49 Consequently, these methods often achieve superior predictive accuracy compared to conventional multiple regression techniques. Currently, various machine learning approaches such as Back-propagation Neural Networks (BPNN), 50 Extreme Learning Machines (ELM), 51 and Support Vector Machines (SVM) 52,53 are employed to forecast CBM content. Empirical evidence suggests that these methods typically outperform traditional regression techniques.…”

Section: Introductionmentioning

confidence: 99%

Optimized Random Forest Method for 3D Evaluation of Coalbed Methane Content Using Geophysical Logging Data

Guo,

Zhang,

Guo

et al. 2024

ACS Omega

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Accurate evaluation of coalbed methane (CBM) content is crucial for effective exploration and development. Traditional gas content measurement methods based on laboratory analysis of drill core samples are costly, whereas geophysical logging methods offer a cost-effective alternative by providing continuous high-resolution profiles of rock layer physical properties. However, the relationship between CBM content and geophysical logging data is complex and nonlinear, necessitating an advanced prediction method. This study focuses on the No. 3 coal seam in the Shizhuang South Block of the Qinshui Basin, utilizing geophysical logging data and 148 sets of laboratory core samples. We employed the Random Forest (RF) method optimized with a simulated annealing-genetic algorithm (SA-GA) to develop the SA-GA-RF model for evaluating CBM content. The model's performance was validated using test data and new CBM well data, and it was applied to calculate the vertical gas content profiles of No. 3 coal seam across 128 wells. The SA-GA-RF model demonstrated an average relative error of 13.13% in the test data set, outperforming Backpropagation Neural Network (BPNN), Least Squares Support Vector Machine (LSSVM), Extreme Learning Machine (ELM), and multivariate regression (MR) methods. The model also exhibited strong generalizability in new wells and improved modelbuilding efficiency compared to traditional cross-validation grid search methods. The construction of a three-dimensional CBM content model, incorporating well coordinates and elevation data, allowed for detailed identification of high gas content areas and layers. This three-dimensional model offers a more precise characterization than traditional two-dimensional isopleth maps, providing valuable insights for CBM exploration, reserve evaluation, and production optimization.

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Section: Introductionmentioning

confidence: 99%

Optimized Random Forest Method for 3D Evaluation of Coalbed Methane Content Using Geophysical Logging Data

Guo,

Zhang,

Guo

et al. 2024

ACS Omega

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Logging Lithology Discrimination with Enhanced Sampling Methods for Imbalance Sample Conditions

Liu,

Tian,

Zhao

et al. 2024

Applied Sciences

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In the process of lithology discrimination from a conventional well logging dataset, the imbalance in sample distribution restricts the accuracy of log identification, especially in the fine-scale reservoir intervals. Enhanced sampling balances the distribution of well logging samples of multiple lithologies, which is of great significance to precise fine-scale reservoir characterization. This study employed data over-sampling and under-sampling algorithms represented by the synthetic minority over-sampling technique (SMOTE), adaptive synthetic sampling (ADASYN), and edited nearest neighbors (ENN) to process well logging dataset. To achieve automatic and precise lithology discrimination on enhanced sampled well logging dataset, support vector machine (SVM), random forest (RF), and gradient boosting decision tree (GBDT) models were trained using cross-validation and grid search methods. Aimed to objectively evaluate the performance of different models on different sampling results from multiple perspectives, the lithology discrimination results were evaluated and compared based on the Jaccard index and F1 score. By comparing the predictions of eighteen lithology discrimination workflows, a new discrimination process containing ADASYN, ENN, and RF has the most precise lithology discrimination result. This process improves the discrimination accuracy of fine-scale reservoir interval lithology, has great generalization ability, and is feasible in a variety of different geological environments.

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Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

Ali,

Fu,

Chen

et al. 2024

Energies

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Porosity assessment is a vital component for reservoir evaluation in the oil and gas sector, and with technological advancement, reliance on conventional methods has decreased. In this regard, this research aims to reduce reliance on well logging, purposing successive machine learning (ML) techniques for precise porosity measurement. So, this research examines the prediction of the porosity curves in the Sui main and Sui upper limestone reservoir, utilizing ML approaches such as an artificial neural networks (ANN) and fuzzy logic (FL). Thus, the input dataset of this research includes gamma ray (GR), neutron porosity (NPHI), density (RHOB), and sonic (DT) logs amongst five drilled wells located in the Qadirpur gas field. The ANN model was trained using the backpropagation algorithm. For the FL model, ten bins were utilized, and Gaussian-shaped membership functions were chosen for ideal correspondence with the geophysical log dataset. The closeness of fit (C-fit) values for the ANN ranged from 91% to 98%, while the FL model exhibited variability from 90% to 95% throughout the wells. In addition, a similar dataset was used to evaluate multiple linear regression (MLR) for comparative analysis. The ANN and FL models achieved robust performance as compared to MLR, with R2 values of 0.955 (FL) and 0.988 (ANN) compared to 0.94 (MLR). The outcomes indicate that FL and ANN exceed MLR in predicting the porosity curve. Moreover, the significant R2 values and lowest root mean square error (RMSE) values support the potency of these advanced approaches. This research emphasizes the authenticity of FL and ANN in predicting the porosity curve. Thus, these techniques not only enhance natural resource exploitation within the region but also hold broader potential for worldwide applications in reservoir assessment.

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Machine learning (ML) for fluvial lithofacies identification from well logs: A hybrid classification model integrating lithofacies characteristics, logging data distributions, and ML models applicability

Cited by 3 publications

References 73 publications

Optimized Random Forest Method for 3D Evaluation of Coalbed Methane Content Using Geophysical Logging Data

Optimized Random Forest Method for 3D Evaluation of Coalbed Methane Content Using Geophysical Logging Data

Logging Lithology Discrimination with Enhanced Sampling Methods for Imbalance Sample Conditions

Advancing Reservoir Evaluation: Machine Learning Approaches for Predicting Porosity Curves

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