{"title":"利用机器学习技术从常规测井中预测盆地尺度的 S 波声波测井结果","authors":"Jaewook Lee, Yangkang Chen, Robin Dommisse, Guo-chin Dino Huang, Alexandros Savvaidis","doi":"10.1111/1365-2478.13527","DOIUrl":null,"url":null,"abstract":"<p>S-wave velocity plays a crucial role in various applications but often remains unavailable in vintage wells. To address this practical challenge, we propose a machine learning framework utilizing an enhanced bidirectional long short-term memory algorithm for estimating S-wave sonic logs from conventional logs, including P-wave sonic, gamma ray, total porosity, and bulk density. These input logs are selected based on traditional rock physics models, integrating geological and geophysical relations existing in the data. Our study, encompassing 34 wells across diverse formations in the Delaware Basin, Texas, demonstrates the superiority of machine learning models over traditional methods like Greenberg–Castagna equations, without prior geological and geophysical information. Among these machine learning models, the enhanced bidirectional long short-term memory model with self-attention yields the highest performance, achieving an <i>R</i>-squared value of 0.81. Blind tests on five wells without prior geologic information validate the reliability of our approach. The estimated S-wave velocity values enable the creation of a basin-scale S-wave velocity model through interpolation and extrapolation of these prediction models. Additionally, the bidirectional long short-term memory model excels not only in predicting S-wave velocity but also in estimating S-wave reflectivity for seismic amplitude variation with offset applications in exploration seismology. In conclusion, these S-wave velocity estimates facilitate the prediction of further elastic properties, aiding in the comprehension of petrophysical and geomechanical property variations within the basin and enhancing earthquake hypocentral depth estimation. </p>","PeriodicalId":12793,"journal":{"name":"Geophysical Prospecting","volume":null,"pages":null},"PeriodicalIF":1.8000,"publicationDate":"2024-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/1365-2478.13527","citationCount":"0","resultStr":"{\"title\":\"Basin-scale prediction of S-wave Sonic Logs using Machine Learning techniques from conventional logs\",\"authors\":\"Jaewook Lee, Yangkang Chen, Robin Dommisse, Guo-chin Dino Huang, Alexandros Savvaidis\",\"doi\":\"10.1111/1365-2478.13527\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>S-wave velocity plays a crucial role in various applications but often remains unavailable in vintage wells. To address this practical challenge, we propose a machine learning framework utilizing an enhanced bidirectional long short-term memory algorithm for estimating S-wave sonic logs from conventional logs, including P-wave sonic, gamma ray, total porosity, and bulk density. These input logs are selected based on traditional rock physics models, integrating geological and geophysical relations existing in the data. Our study, encompassing 34 wells across diverse formations in the Delaware Basin, Texas, demonstrates the superiority of machine learning models over traditional methods like Greenberg–Castagna equations, without prior geological and geophysical information. Among these machine learning models, the enhanced bidirectional long short-term memory model with self-attention yields the highest performance, achieving an <i>R</i>-squared value of 0.81. Blind tests on five wells without prior geologic information validate the reliability of our approach. The estimated S-wave velocity values enable the creation of a basin-scale S-wave velocity model through interpolation and extrapolation of these prediction models. Additionally, the bidirectional long short-term memory model excels not only in predicting S-wave velocity but also in estimating S-wave reflectivity for seismic amplitude variation with offset applications in exploration seismology. 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引用次数: 0
摘要
S 波速度在各种应用中起着至关重要的作用,但在老井中往往无法获得。为了应对这一实际挑战,我们提出了一种机器学习框架,利用增强型双向长短期记忆算法,从传统测井资料(包括 P 波声波、伽马射线、总孔隙度和体积密度)中估算 S 波声波测井资料。这些输入测井曲线是根据传统的岩石物理模型,结合数据中存在的地质和地球物理关系选择的。我们的研究涵盖德克萨斯州特拉华盆地不同地层的 34 口油井,研究结果表明,在没有地质和地球物理信息的情况下,机器学习模型优于格林伯格-卡斯塔格纳方程等传统方法。在这些机器学习模型中,具有自我关注功能的增强型双向长短期记忆模型性能最高,R 方值达到 0.81。对五口无地质信息的油井进行的盲测验证了我们方法的可靠性。通过这些预测模型的内插和外推法,估计的 S 波速度值能够创建一个盆地尺度的 S 波速度模型。此外,双向长短期记忆模型不仅在预测 S 波速度方面表现出色,而且在估算地震振幅变化的 S 波反射率方面也很出色,在勘探地震学中具有偏移应用价值。总之,这些 S 波速度估算有助于预测进一步的弹性性质,帮助理解盆地内岩石物理和地质力学性质的变化,并加强地震次中心深度估算。
Basin-scale prediction of S-wave Sonic Logs using Machine Learning techniques from conventional logs
S-wave velocity plays a crucial role in various applications but often remains unavailable in vintage wells. To address this practical challenge, we propose a machine learning framework utilizing an enhanced bidirectional long short-term memory algorithm for estimating S-wave sonic logs from conventional logs, including P-wave sonic, gamma ray, total porosity, and bulk density. These input logs are selected based on traditional rock physics models, integrating geological and geophysical relations existing in the data. Our study, encompassing 34 wells across diverse formations in the Delaware Basin, Texas, demonstrates the superiority of machine learning models over traditional methods like Greenberg–Castagna equations, without prior geological and geophysical information. Among these machine learning models, the enhanced bidirectional long short-term memory model with self-attention yields the highest performance, achieving an R-squared value of 0.81. Blind tests on five wells without prior geologic information validate the reliability of our approach. The estimated S-wave velocity values enable the creation of a basin-scale S-wave velocity model through interpolation and extrapolation of these prediction models. Additionally, the bidirectional long short-term memory model excels not only in predicting S-wave velocity but also in estimating S-wave reflectivity for seismic amplitude variation with offset applications in exploration seismology. In conclusion, these S-wave velocity estimates facilitate the prediction of further elastic properties, aiding in the comprehension of petrophysical and geomechanical property variations within the basin and enhancing earthquake hypocentral depth estimation.
期刊介绍:
Geophysical Prospecting publishes the best in primary research on the science of geophysics as it applies to the exploration, evaluation and extraction of earth resources. Drawing heavily on contributions from researchers in the oil and mineral exploration industries, the journal has a very practical slant. Although the journal provides a valuable forum for communication among workers in these fields, it is also ideally suited to researchers in academic geophysics.