测井沉积学

将人工神经网络（ANN）模式识别技术应用于测井沉积学解释中，可以实现井剖面沉积环境的连续自动解释，大大提高解释精度和工作效率。

Application of the Artificial Neural Network ( ANN ) to identify sedimentary microfacies from well logging data can complete the series auto interpreting . The application can improve the auto interpreting accuracy and make us get more satisfied results .

测井沉积学方法和应用概述

The Outline of Methodology and Application of Log Sedimentology

应用神经网络模式识别技术进行测井沉积学研究

Application of artificial neural network pattern recognition technology to the study of well-logging sedimentology

整理和发展测井沉积学已经有了理论和方法基础。

There has been a theoretical and practical base for sorting out and developing the log sedimentology .

为了进行测井沉积学和测井层序地层学的研究，需要对测井曲线形态进行识别。

Well logging curve shape identification is needed in study of logging sedimentology and logging sequence stratigraphy . Firstly , the first and second derivatives of logging curve are solved .

测井沉积学是近些年来发展起来的一门新的边缘学科，它是以测井资料为主，在油区沉积学研究覆盖下，并且与其它学科和技术紧密结合的一种专门的多井测井评价技术。

Measure-Well Sedimentology is a new subject recently , which is a special evaluation technology on multi-well , based on metrical data of oil wells , covered by oil-section sedimentology and combined with other subjects and technology .

其核心是把测井资料用于油区沉积学研究，以获取进一步描述油气储集层的基础信息。

It uses metrical data of oil wells to research oil-section sedimentology and gather basic information on deposited layer of oil and gas .

利用油、水基泥浆测井资料，根据沉积学原理、声波传播机理系统分析了砂泥岩剖面各类岩性测井声阻抗、真实波阻抗变化特征及其控制因素。

Based on the sedimentologic theory and acoustic transit mechanism and by using logging data of wells drilled with oil-based and water-based mud , this paper systematically analyzes features and control factors of various acoustic impedances and actual wave impedances met in sandstone-mudstone section while logging .

测井沉积微相模式为陆相盆地沉积学和定量测井沉积学的深入研究可提供一个参考模式。

The logging sedimentary microfacies may provide a reference model for further studying the sedimentation and logging sedimentation in continental basins .