Particle size logging inversion method of deep complex clastic rock and its application in fine lithology identification
The Miocene strata in the L area of the Y Basin in the western part of the South China Sea are characterised by high temperature and ultra-high pressure,which makes drilling difficult and core data rare.In addition,the accuracy of rock chip logging in reflecting lithology is relatively low,making it diffi-cult to meet the requirements of fine identification of lithology.The deep clastic rocks in the second section of Huangliu Formation in the L area of Y basin are used in this study,firstly,using the limited data of core size analysis,rock chip logging and logging,we selected the particle size parameter characterizing li-thology:median Md and five logging curves of natural gamma,density,neutron,acoustic time difference and resistivity which are sensitive to changes in the particle size,and constructed the data set of five varia-bles of the median Md and logging,and then we used K-MEANS secondly,using K-MEANS clustering method,the dataset was divided into four classes according to the optimal relationship between"sum of error squares and the number of clusters"(referred to as"granularity classification"),which optimised the correlation between the median Md-granularity and the logging response,and obtained the logging re-sponse characteristics of the different classes and the corresponding lithological types.Then,in the actual well data processing process,Fisher's discriminant equation is applied to determine the type of particle size classification to which the unknown depth point belongs,and finally,the intelligent calculation model of median particle size logging based on XGBoost algorithm is established under the particle size classifica-tion,and based on the numerical range of median particle size corresponding to different lithologies,it realises the purpose of fine identification of lithology by inverting the median Md curve according to the log-ging on the wellbore profile.The purpose of fine identification of lithology is achieved by inverting the Md curve on the wellbore profile according to the logging.The results show that the sandstone lithology in the second section of Huangliu Formation in L area is divided into:siltstone,fine sandstone,medium sand-stone and coarse sandstone considering the difference of grain size,among which the fine sandstone and medium sandstone are the most dominantly developed lithologies,and the median Md of grain size has the closest relationship with the lithology of different grain sizes,and it is the most reflective of the different grain sizes of lithologies;the intelligent calculation of the median Md of grain size logging model based on XGBoost algorithm is better than that of multiple regression algorithm after the classification of the grain sizes.The prediction effect of the model is better than that of the multiple regression prediction model,and the correlation coefficient between the calculated median particle size and the measured value reaching 0.9397,the average absolute error(MAE)is 0.0195,and the average relative error MRE is 0.1752.The model is an effective method for the fine identification of the lithology of the deep complex elastic rocks,and it also lays a foundation for sedimentary grain sequence analysis and fine interpretation of the reservoir configuration,and the evaluation of the validity on the vertical profile.It also lays the foundation for sedimentary grain sequence analysis,fine interpretation of reservoir configuration and validity evalua-tion in longitudinal section.
South China SeaMiocenecomplex lithologymedian grain sizelogginginver-sionmachine learningXGBoost algorithm
万方数据
维普
