Sonic well logs provide critical information to calibrate seismic data and support geomechanical characterization. Advanced subsurface data analytics and machine learning enable new methods and workflows for property estimation, regression, and classification for geoscience and subsurface engineering applications. However, current applications for imputation of well-logging values rely only on model accuracy and low error predictions. T raditional model validation techniques are not enough to validate models and account for the substantial uncertainty in the subsurface. Well-logging imputation estimates and their associated uncertainty models are essential to the field development planning and decision-making workflows, such as reservoir modeling, volumetric resource assessment, predrill prediction with uncertainty, remaining resource mapping, and production allocation. When performing subsurface feature imputation with machine learning, we must expand our machine-learning model training and complexity tuning workflows to check the entire uncertainty model to ensure uncertainty distributions are precise and accurate. We propose a workflow that integrates the goodness metric to calculate accurate and precise uncertainty models of sonic well-log predictions based on ensembles of the machine-learning estimates. Our workflow combines model evaluation and visualization of the estimates and the uncertainty model with respect to measured depth. Our proposed method provides intuitive diagnostics and metrics to evaluate estimation accuracy and uncertainty model goodness.

Eduardo Maldonado-Cruz

John T. Foster

Michael J. Pyrcz

Petrophysics – The SPWLA Journal of Formation Evaluation and Reservoir Description

PETROPHYSICS 包含对地层评价的理论和应用方面的原创性贡献，包括裸眼和套管井测井、岩心分析和地层测试。

The journal "Petrophysics" publishes original research contributions on the theory and application of formation evaluation, including open-hole and cased-hole logging, core analysis, and formation testing.

《Petrophysics》期刊发表关于地层评价的原创性研究，包括裸眼和套管井测井、岩心分析及地层测试等理论与应用方面的贡献。

Category	Quartile
工程技术	4
工程技术, 工程石油	4
工程技术, 地球化学与地球物理	4

Sonic Well-Log Imputation Through Machine-Learning-Based Uncertainty Models

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Journal Info

Category	Quartile
GEOCHEMISTRY & GEOPHYSICS	4