基于集成测井数据的地层岩性自动识别研究数据集（2009-2013年）

岩性识别方法是基于机器学习分类算法，可以对测井数据和测井图进行分类和识别，有效地学习和记忆储层中岩层的特征。该方法可以比较吸附岩体、决策树、随机森林和SVM四种算法，选择出最高的岩性识别精度。为满足识别精度、效率等要求提供重要依据，对测井数据的自动解释和计算机的地层自识别具有重要意义...

Lithology identification methods, which fall under machine learning classification algorithms, can classify and identify logging data and well logging images, and effectively learn and memorize the characteristics of rock formations in reservoirs. This approach can compare four algorithms, namely AdaBoost, Decision Tree, Random Forest and Support Vector Machine (SVM), to select the optimal solution with the highest lithology identification accuracy. It provides a critical basis for meeting requirements such as recognition accuracy and efficiency, and is of great significance to automatic interpretation of logging data and computerized automatic formation identification...

搜集汇总

背景与挑战

背景概述

该数据集为2009-2013年塔里木盆地的测井数据集成，用于机器学习方法的地层岩性自动识别研究，包含多种测井数据类型，数据格式为Excel，旨在提升岩性识别的精度和效率。