ROGII Pipeline A Trained Models

kaggle2026-07-05 更新2026-07-24 收录

井筒地质预测

油气勘探机器学习

数据链接：

https://www.kaggle.com/datasets/drmikholaehutchinson/rogii-pipeline-a-models 数据链接链接失效反馈

官方服务：

资源简介：

LightGBM + CatBoost models for wellbore geology prediction

面向井筒地质预测任务的LightGBM与CatBoost模型

创建时间：

2026-07-05

Data for: Optimization of Models for a Rapid Identification of Lithology While Drilling - A Win-Win Strategy Based on Machine Learning

随钻测井岩性识别

油气勘探机器学习

There are 3345 groups training data which come from conventional wireline logging of Yan'an gas field and 3345 groups test data which come from logging while drilling of Yan'an gas field in the data f

NIAID Data Ecosystem50

Statistical test of Kruskal-Wallis statistic H.

测井数据重建

油气勘探机器学习

The process of extracting oil and gas via borehole drilling is largely dependent on subsurface structures, and thus, well log analysis is a major concern for economic feasibility. Well logs are essent

Figshare2024-12-05 更新20

rogii-datasets-processed

井筒地质预测

油井分析

Creation of train_df.csv y test_csv de ROGII - Wellbore Geology Prediction

kaggle2026-06-05 更新00

The setting parameters and their initial values.

测井数据重建

油气勘探机器学习

NIAID Data Ecosystem40

Data for: Characterization of Kerogen Content and Activation Energy of Decomposition using Machine Learning Technologies in Combination with Numerical Simulations of Formation Heating

干酪根特性分析

油气勘探机器学习

Heater temperature transient data obtained with numerical simulation of formation heating. This is also the input data for inversion and classification by machine learning methods.

NIAID Data Ecosystem20

ROGII Pipeline A Trained Models

资源简介：

相关数据集