Prediction of Total Organic Carbon Content in Continental Shale Reservoirs Based on Multiple Logging Parameters:A Case Study from Member 7 of Yanchang Formation in Heshangyuan Area
TOC is a key parameter to evaluate shale oil and gas potential,TOC prediction by using logging data can depict the change of TOC in the whole section of the reservoir,which is of great significance for clarifying the geological-engineering sweet spot.Due to the frequent alternation of depositional environments,a large number of siltstone mudstone bands are developed in the shale of member 7 in Yanchang formation in Heshangyuan area.In view of the characteristics of strong lithologic heterogeneity,TOC content of the shale reservoir was predicted by distinguishing the two lithologies of shale and siltstone.Based on the comparison of the existing methods and the actual well logging data in the study area,the improved method of △lgR and the machine stepwise regression learning method were selected to establish the prediction model by lithology.After testing the accuracy of the measured data,the results show that the machine stepwise regression learning method has higher prediction accuracy.The error analysis between the predicted and measured values in a single well also confirms the reliability of the machine stepwise regression learning method by subdivision lithology,proving that the method is effective in predicting TOC content of mud shale,which is mainly based on conventional logging.On this basis,TOC content and its spatial distribution is obtained by the logging interpretation of 101 wells'shale of member 7 in Yanchang formation.
total organic carbon content predictionlogging datacontinental shalemachine learning
万方数据
维普
