Research Status and Prospects of Intelligent Logging Lithology Identification DOI
Huang Jin,

Ci Yutong,

Xuan Liu

и другие.

Measurement Science and Technology, Год журнала: 2024, Номер 36(1), С. 012010 - 012010

Опубликована: Дек. 10, 2024

Abstract With the increasing of petroleum exploration and development, accurate lithology identification is crucial. Machine learning (ML) plays a key role in logging identification. By introducing traditional methods, we review application ML from perspectives bibliometrics classification this paper. The applications supervised learning, semi-supervised unsupervised ensemble deep algorithms are introduced detail. Multiple have achieved remarkable results different scenarios. For example, support vector machine, random forest, eXtreme gradient boosting, convolutional neural network perform well obtain relatively high accuracy. However, for also faces challenges such as data quality, imbalance, model generalization, interpretability. Future research should focus on algorithm optimization innovation, improvements quality quantity, multidisciplinary integration practical to enhance accuracy reliability These findings provide strong oil gas development.

Язык: Английский

Optimizing Sensor Data Interpretation via Hybrid Parametric Bootstrapping DOI Creative Commons
V. V. Golovko

Sensors, Год журнала: 2025, Номер 25(4), С. 1183 - 1183

Опубликована: Фев. 14, 2025

The Chalk River Laboratories (CRL) site in Ontario, Canada, has long been a hub for nuclear research, which resulted the accumulation of legacy waste, including radioactive materials such as uranium, plutonium, and other radionuclides. Effective management this requires precise contamination risk assessments, with particular focus on concentration levels fissile U235. These assessments are essential maintaining criticality safety. This study estimates upper bounds U235 concentrations. We investigated use hybrid parametric bootstrapping method robust statistical techniques to analyze datasets outliers, then compared these outcomes those derived from nonparametric bootstrapping. underscores significance measuring ensuring safety, conducting environmental monitoring, adhering regulatory compliance requirements at sites. used publicly accessible data Eastern Desert Egypt demonstrate application methods small datasets, providing reliable limit that vital remediation decommissioning efforts. seeks enhance interpretation sensor data, ultimately supporting safer waste practices sites CRL.

Язык: Английский

Процитировано

3
An integrated workflow combining machine learning and wavelet transform for automated characterization of heterogeneous groundwater systems DOI Creative Commons
Musaab A. A. Mohammed, Norbert Péter Szabó, Abdelrhim Eltijani

и другие.

Scientific Reports, Год журнала: 2025, Номер 15(1)

Опубликована: Фев. 10, 2025

Groundwater aquifers are complex systems that require accurate lithological and hydrogeological characterization for effective development management. Traditional methods, such as core analysis pumping tests provide precise results but expensive, time-consuming, impractical large-scale investigations. Geophysical well logging data offers an efficient continuous alternative, though manual interpretation of logs can be challenging may result in ambiguous outcomes. This research introduces automated approach using machine learning signal processing techniques to enhance the aquifer characterization, focusing on Quaternary system Debrecen area, Eastern Hungary. The proposed methodology is initiated with imputation missing deep resistivity from spontaneous potential, natural gamma ray, medium utilizing a gated recurrent unit (GRU) neural network. preprocessing step significantly improved quality subsequent analyses. Self-organizing maps (SOMs) then applied preprocessed map distribution units across groundwater system. Considering mathematical geological aspects, SOMs delineated three primary units: shale, shaly sand, sand gravel which aligned closely drilling data. Continuous wavelet transform further refined mapping hydrostratigraphical boundaries. integrated methods effectively mapped subsurface generating 3D model simplifies into four major zones. lithology deterministically estimated shale volume permeability, revealing higher permeability lower sandy gravelly layers. provides robust foundation flow contaminant transport modeling extended other regions management development.

Язык: Английский

Процитировано

1
Developing an Automatic Gripping Learning System for a Robotic Arm by Integrating a Convolutional Neural Network and Optimization Algorithms DOI Creative Commons
Ping‐Huan Kuo, Li-Chia Yeh, Chen-Wen Chang

и другие.

Results in Engineering, Год журнала: 2025, Номер unknown, С. 105026 - 105026

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

1
Research Status and Prospects of Intelligent Logging Lithology Identification DOI
Huang Jin,

Ci Yutong,

Xuan Liu

и другие.

Measurement Science and Technology, Год журнала: 2024, Номер 36(1), С. 012010 - 012010

Опубликована: Дек. 10, 2024

Abstract With the increasing of petroleum exploration and development, accurate lithology identification is crucial. Machine learning (ML) plays a key role in logging identification. By introducing traditional methods, we review application ML from perspectives bibliometrics classification this paper. The applications supervised learning, semi-supervised unsupervised ensemble deep algorithms are introduced detail. Multiple have achieved remarkable results different scenarios. For example, support vector machine, random forest, eXtreme gradient boosting, convolutional neural network perform well obtain relatively high accuracy. However, for also faces challenges such as data quality, imbalance, model generalization, interpretability. Future research should focus on algorithm optimization innovation, improvements quality quantity, multidisciplinary integration practical to enhance accuracy reliability These findings provide strong oil gas development.

Язык: Английский

Процитировано

1