一种新的TOC含量拟合方法研究与应用

Study and Application of a New Overlay Method of the TOC Content

由于取样的限制,通常在烃源岩评价中往往只能获得十分有限的实测烃源岩的TOC含量数据。但随着我国多数盆地精细勘探的深入,需要更加精细的烃源岩评价,因此,人们对测井数据拟合烃源岩的TOC含量变化越来越重视。目前该技术最先进的是人工神经网络拟合方法,但多种神经网络在拟合高成熟度碳酸盐岩烃源岩地层的TOC含量时相关性均不够高。针对研究区内烃源岩的特点,尝试了一种用图版分类—模糊排队—BP神经网络联合拟合TOC含量的新方法。利用该方法对鄂尔多斯盆地马家沟组的测井资料和实测总有机碳资料进行数据处理,将结果与ΔlogR方法、模糊神经网络方法的结果相比较,证实该方法的结果与实测值有更好的相关性,为系统了解鄂尔多斯盆地下古生界烃源岩发育提供了一定的科学依据。

Because of the sampling limitation in the assessment of source rock, we could obtain very limited TOC content data from actually measuring the source rock. With the fine exploration for most basins in China, the finer assessment of source rock is needed. Much attention is paid to the logging data overlaying the TOC content＇s variation of the source rock. The most advanced overlay method of this technique is artificial neural network. But several neural networks do not make adequate high correlativity when they overlay the TOC content of the carbonate source rock with high degree of maturity. In view of the characteristics of the source rock in the region of interest, this paper attempts a new method to overlay the TOC content, which assembles the crossplot／｜sorting, fuzzy-ranking and BP neural network. Using this way to process the logging data and the data of actually measured total organic carbon in Majiagou Formation of Ordos basin, and comparing the results with the results of the ΔlogR method and the fuzzy neural network, we demonstrated that the results of this method have more correlativity with the measured value, and it is helpful for systematically knowing the developing conditions about the Low Palaeozoic source rock in Ordos basin.