多源测井数据预测煤层工业组分和发热量模型研究

A prediction model of the industrial components and calorific values of coal seams based on multi-source log data

煤层工业组分和发热量是评价煤质的基本依据,利用测井资料预测煤层工业组分和发热量可以弥补煤芯样试验分析的不足。利用宁夏某井田详查、勘探等不同阶段的数字测井和煤质化验数据,在研究煤质特征、测井响应特征和统计分析的基础上,建立了测井响应特征提取、样本集建立和数据处理方法与深度神经网络模型,通过对测试数据的预测结果和试验分析结果对比,验证了预测模型有效性。

The industrial components and calorific values of coal seams serve as an important basis for the evaluation of coal quality,and the prediction of them based on log data allows for overcoming the deficiency in the experimental analysis of coal core samples.This study collected data from digital logs and coal quality analysis at different stages(e.g.,detailed survey and exploration)of a coal field in Ningxia.Based on the investigation of the coal quality and log responses,as well as statistical analysis,this study developed the methods for extracting log response characteristics,establishing sample sets,and processing data and established a deep neural net-work-based prediction model.Then,it confirmed the validity of the prediction model by comparing the predicted results of testing data with the results from the experimental analysis.