Translation of machine learning approaches into gas hydrate saturation proxy: a case study from Krishna-Godavari (KG) offshore basin

IF 1.6 4区 地球科学 Q3 GEOCHEMISTRY & GEOPHYSICS
Bappa Mukherjee, Kalachand Sain, Rahul Ghosh, Suman Konar
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Abstract

Empirical methods often fail to accurately depict in-situ gas hydrate saturation distributions, despite their relationships with petrophysical and elastic properties remaining partially unclear. We proposed a data-driven approach to estimate gas hydrate saturation employing several machine learning techniques, including radial basis function neural network (RBFNN), random forest (RF), extreme gradient boosting (XGBoost), Adaptive Boosting (AdaBoost), support vector machines (SVM), and k-nearest neighbors (kNN). This study involved pre-processing data from laterolog deep resistivity and p-wave velocity logs, defining their increments as differences from the lowest values in gas hydrate zones. We identified data-driven patterns between pairs of laterolog deep resistivity and p-wave velocity increments, as well as core information corroborated with the traditionally predicted gas hydrate saturations, by adopting machine learning (ML) approaches. The approach tested on four wells in the Krishna-Godavari (KG) offshore basin (India) is extremely feasible. During the training and test phases, the minimum correlation coefficient between the true and predicted responses exceeds 0.94 and 0.88, respectively. The model accuracy hierarchy was RBFNN > AdaBoost > RF > XGBoost > KNN > SVM during training, and AdaBoost > XGBoost > RF > RBFNN > KNN > SVM during testing. This approach allows interpreters to select the most accurate ML model based on training phase performance. The proposed ML-based method is efficient, synergising p-wave and resistivity data increment, significantly improving gas hydrate saturation predictions, and avoiding the complexities of traditional calculations. The study indicates that gas hydrate saturation in the Krishna-Godavari region ranges from 0.17 to 86.84%.

Abstract Image

将机器学习方法转化为天然气水合物饱和度代理:克里希纳-戈达瓦里(KG)近海盆地的案例研究
经验方法往往无法准确描述原位天然气水合物饱和度分布,尽管它们与岩石物理和弹性特性的关系仍有部分不明确。我们提出了一种数据驱动的方法,利用径向基函数神经网络 (RBFNN)、随机森林 (RF)、极梯度提升 (XGBoost)、自适应提升 (AdaBoost)、支持向量机 (SVM) 和 k 近邻 (kNN) 等机器学习技术来估算天然气水合物饱和度。这项研究包括预处理来自 laterolog 深电阻率和 p 波速度测井的数据,将其增量定义为与天然气水合物区最低值的差异。通过采用机器学习 (ML) 方法,我们确定了红外深电阻率和 p 波速度增量对之间的数据驱动模式,以及与传统预测的天然气水合物饱和度相印证的岩心信息。在印度克里希纳-戈达瓦里(KG)近海盆地的四口油井上测试的方法非常可行。在训练和测试阶段,真实响应和预测响应之间的最小相关系数分别超过 0.94 和 0.88。在训练阶段,模型精度等级为 RBFNN > AdaBoost > RF > XGBoost > KNN > SVM;在测试阶段,模型精度等级为 AdaBoost > XGBoost > RF > RBFNN > KNN > SVM。这种方法允许解释人员根据训练阶段的表现选择最准确的 ML 模型。所提出的基于 ML 的方法效率很高,能协同 p 波和电阻率数据增量,显著提高天然气水合物饱和度预测,并避免了传统计算的复杂性。研究表明,克里希纳-戈达瓦里地区的天然气水合物饱和度在 0.17% 到 86.84% 之间。
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来源期刊
Marine Geophysical Research
Marine Geophysical Research 地学-地球化学与地球物理
CiteScore
2.80
自引率
14.30%
发文量
41
审稿时长
>12 weeks
期刊介绍: Well-established international journal presenting marine geophysical experiments on the geology of continental margins, deep ocean basins and the global mid-ocean ridge system. The journal publishes the state-of-the-art in marine geophysical research including innovative geophysical data analysis, new deep sea floor imaging techniques and tools for measuring rock and sediment properties. Marine Geophysical Research reaches a large and growing community of readers worldwide. Rooted on early international interests in researching the global mid-ocean ridge system, its focus has expanded to include studies of continental margin tectonics, sediment deposition processes and resulting geohazards as well as their structure and stratigraphic record. The editors of MGR predict a rising rate of advances and development in this sphere in coming years, reflecting the diversity and complexity of marine geological processes.
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