岩石物理模型在定量化预测页岩油气储层有机质含量中的应用

Application of Petrophysical Models in Quantitative Prediction of Kerogen Content in Shale Oil and Gas Reservoirs

有机质含量是页岩储层测录井评价的重要参数,准确获取有机质含量是进行页岩储层综合地质评价的基础。根据页岩储层特征,基于自适应(SCA模型)、Kuster-ToksÖz模型和Backus平均模型开发了适用于测录井尺度的页岩岩石物理模型的有机质含量反演算法,该方法可弥补烃源岩取心少、实测样品分布不连续的不足。通过对两口实际井录测井资料的处理分析,将实测有机质含量与预测有机质含量进行比较,结果表明,两者拟合程度较高,相关性好,相对误差在0.02%~0.05%,充分证明了岩石物理建模方法与有机质含量反演算法的可靠性。该页岩有机质含量反演方法有效提高了页岩有机质含量的预测精度,为录测井油气层解释评价提供了较为可靠的依据。

Kerogen content is an important parameter for shale reservoir logging evaluation,and accurate acquisition of kerogen content is the basis for comprehensive geological evaluation of shale reservoirs.Based on the shale reservoir characteristics,an organic matter content inversion algorithm based on SCA model,Kuster-ToksÖz model and Backus average model is summarized for shale petrophysical models at the logging well scale,which can compensate for the shortage of hydrocarbon source rock coring and discontinuous distribution of measured samples.By processing and analyzing the logging and logging data of two actual wells and com-paring the measured kerogen content with the predicted kerogen content,the results show that the two fit well and correlate well with relative errors in the range of 0.02%~0.05%,which fully proves the reliability of the petrophysical modeling method and the kerogen content inversion algorithm.Kerogen content inversion method of shale effectively improves the prediction accuracy of shale kerogen content,and provides a more reliable basis for the interpretation and evaluation of oil and gas formations.