Improved well log classification using semisupervised Gaussian mixture models and a new hyper-parameter selection strategy

Dunham, Michael W.; Malcolm, Alison; Welford, J. Kim

doi:10.1016/j.cageo.2020.104501

Cited by 20 publications

(9 citation statements)

References 31 publications

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“…In recent years, a large number of scholars have tried to apply data-driven log interpretation methods to lithofacies identification for different regions and types of reservoirs; these approaches are mainly divided into unsupervised learning methods and supervised learning methods. Unsupervised learning methods mainly include factor analysis methods (Asfahani, 2014), principal component analysis (PCA) (Li et al, 2022), cluster analysis methods (Chen and Hiscott, 1999), and Gaussian mixture model methods (Dunham et al, 2020), which focus more on the statistics of the logging response itself. Supervised learning methods are more concerned with the correlations between geological properties and logging responses.…”

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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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 accuracy of this identification process directly affects reservoir evaluation and development plan formulation [2,3]. The traditional approach to lithofacies identification uses core-and thin-section analysis from cored-well matched with well-logs [4][5][6]. However, the collection of core samples and thin sections is often constrained by time and cost.…”

Section: Introductionmentioning

confidence: 99%

“…To address these challenges, different AI-and non-AI-based computational techniques and algorithms have long been used. These approaches include support vector machines (SVMs) [9], k-nearest neighbors (k-NNs) [1], fuzzy logic [10,11], artificial neural networks (ANNs), and machine learning [4,[12][13][14]. The fundamental goal of these methods is to use quick, repetitive calculations with complex equations to find the spatial and mathematical correlations between wire-line log data and lithofacies.…”

Section: Introductionmentioning

confidence: 99%

“…With increasing computational power, these techniques have become popular for identifying lithofacies. Machine learning accuracies hinge on training phases, categorized into supervised, semi-supervised, and unsupervised techniques [4,17]. In supervised learning, where models are trained with annotated data, challenges arise due to limited annotated data for lithofacies identification [18,19].…”

Section: Introductionmentioning

confidence: 99%

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Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Hasan,

Shahidi

et al. 2023

Energies

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Stratigraphic identification from wire-line logs and core samples is a common method for lithology classification. This traditional approach is considered superior, despite its significant financial cost. Artificial neural networks and machine learning offer alternative, cost-effective means for automated data interpretation, allowing geoscientists to extract insights from data. At the same time, supervised and semi-supervised learning techniques are commonly employed, requiring a sufficient amount of labeled data to be generated through manual interpretation. Typically, there are abundant unlabeled geophysical data while labeled data are scarcer. Supervised and semi-supervised techniques partially address the cost issue. An underutilized class of machine-learning-based methods, unsupervised data clustering, can perform consonant classification by grouping similar data without requiring known results, presenting an even more cost-effective solution. In this study, we examine a state-of-the-art unsupervised data clustering algorithm called piecemeal clustering to identify lithofacies from wire-line logs, effectively addressing these challenges. The piecemeal clustering algorithm groups similar wire-log signatures into clusters, determines the number of clusters present in the data, and assigns each signature to one of the clusters, each of which represents a lithofacies. To evaluate the performance, we tested the algorithm on publicly released data from ten wells drilled in the Hugoton and Panoma fields of southwest Kansas and northwest Oklahoma, respectively. The data consist of two major groups: marine and non-marine facies. The study herein is centered around addressing two fundamental research questions regarding the accuracy and practicality of the piecemeal clustering algorithm. The algorithm successfully identified nine distinct clusters in our dataset, aligning with the cluster count observed in previously published works employing the same data. Regarding mapping accuracy, the results were notable, with success rates of 81.90% and 45.20% with and without considering adjacent facies, respectively. Further detailed analysis of the results was conducted for individual types of facies and independently for each well. These findings suggest the algorithm’s precision in characterizing the geological formations. To assess its performance, a comprehensive comparative analysis was conducted, encompassing other data clustering algorithms, as well as supervised and semi-supervised machine learning techniques. Notably, the piecemeal clustering algorithm outperformed alternative data clustering methods. Furthermore, despite its unsupervised nature, the algorithm demonstrated competitiveness by yielding results comparable to, or even surpassing, those obtained through supervised and semi-supervised techniques.

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“…Density based data clustering algorithms available in the literature may be able to cluster such data sets and identify the number of clusters in the data set, but they filter out many data points as noise. Therefore, only supervised and semisupervised classifiers are used to solve the problem [22], which does not eliminate the manual process. The proposed algorithm may solve this problem since it can identify the number of clusters on its own, and also works with noisy data.…”

Section: Introductionmentioning

confidence: 99%

Piecemeal Clustering: a Self-Driven Data Clustering Algorithm

et al. 2022

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Various approaches have been discussed in the literature for the clustering of data, such as partitioning, hierarchical, and machine learning methods. Most of the approaches require some prior knowledge about the clusters, such as their total number. Furthermore, some previous algorithms are not robust enough to process higher-dimensional data or require a large amount of memory for computations. We propose, herein, a data clustering algorithm, Piecemeal Clustering, that successfully clusters data without prior knowledge of the number of clusters. The proposed clustering algorithm uses the similarity and density of the data to identify the number of clusters in the data set and works with both low-and high-dimensional data. We demonstrate the power of the proposed Piecemeal Clustering algorithm with two real-world data sets. It is found that the proposed algorithm outperforms seven other state-of-the-art algorithms on both of these data sets.

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Improved well log classification using semisupervised Gaussian mixture models and a new hyper-parameter selection strategy

Cited by 20 publications

References 31 publications

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

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

Lithofacies Identification from Wire-Line Logs Using an Unsupervised Data Clustering Algorithm

Piecemeal Clustering: a Self-Driven Data Clustering Algorithm

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