视岩石结构数计算技术及其在碳酸盐岩岩相测井识别中的应用

Apparent rock fabric number technology and its application in carbonate lithofacies logging identification

利用测井资料识别碳酸盐岩岩石结构组分,对于碳酸盐岩沉积相研究具有重要的实用意义。碳酸盐岩地层通常经历强烈的成岩作用,缝洞发育,如何利用常规测井资料较精确地识别其基于岩石结构组分分类的岩相一直是个难题。目前有效测井参数与机器学习方法结合已经成为提高岩相识别精度的有效手段,其中Lucia的视岩石结构数参数在国外碳酸盐岩地层中取得了很好的应用效果。然而,ARFN技术建立的条件为非裂缝和非水层,这使得其应用受到较大限制。以四川盆地GM地区寒武系龙王庙组为例,提出了改进的方法。首先,参考Lucia的岩石类型分类方案,将岩相划分为3大类:颗粒白云岩(包括砂屑白云岩和细晶白云岩)、粉晶白云岩和泥晶白云岩(包括泥晶白云岩和泥质泥晶白云岩)。然后,通过岩-电关系分析优选出最敏感测井曲线,包括密度、声波时差和自然伽马。根据测井计算的孔隙度、含水饱和度及薄片鉴定资料,按照水层和非水层条件,对Lucia的视岩石结构数公式进行改进。计算结果表明,改进的ARFN曲线能够快速定量识别岩石结构组分,尤其对颗粒白云岩和泥晶白云岩的识别率很高。最后,将改进的ARFN作为输入测井参数之一,与K邻近分类算法(KNN)结合进一步提升岩相测井识别精度。取心井岩心资料验证表明,改进后的ARFN将平均识别符合率从74%提高到80%以上,有效推动了四川盆地GM地区龙王庙组碳酸盐岩沉积微相的精细研究工作。

The development of carbonate reservoirs is closely related to sedimentary facies.The identification of carbonate rock fabric components with logging data is of great practical significance for the study of carbonate sedimentary facies.However,due to the strong diagenesis and the development of fractures and vugs in the carbonate strata,it has always been a difficult problem to use conventional logging data to accurately identify lithofacies based on rock fabric classification.At present,the combination of effective logging parameters and machine learning methods has become an effective means to improve the identification accuracy.Among them,Lucia’s apparent rock fabric number(ARFN)parameter has achieved good application results in carbonate rocks.However,ARFN technology is established in non-fractured and non-water zones,which limits its application.Therefore,this paper takes the Cambrian Longwangmiao Formation in the GM area of Sichuan Basin as an example,and proposes an improved method.The study area is dominated by carbonate ramp deposition,which is controlled by late dolomitization and hypergene dissolution,with dissolution pores and multiphase fractures developing.The reservoir has good physical properties but strong heterogeneity.Firstly,with reference to the Lucia’s rock type classification scheme,the lithofacies are divided into three categories:granular dolomite(including sandy dolomite and finely crystalline dolomite),very finely crystalline dolomite and micrite dolomite(including micrite dolomite and argillaceous micrite dolomite).Then,through the relationship analysis of the rock types and log response,the most sensitive curves are selected as density,acoustic wave and natural gamma curve.Based on the logging porosity,logging water saturation and thin section identification data,Lucia’s ARFN formula is improved according to the conditions of water layer and non-water layer.The results show that ARFN curve can quickly and quantitatively identify rock fabric components,and the recognition coincidence rate is high for granular dolomite and micrite dolomite,but low for very finely crystalline dolomite.Finally,the improved ARFN is used as one of the logging input parameters,and combined with the K-neighbor classification algorithm(KNN)to further improve the accuracy of lithofacies logging identification.The core data verification shows that the average coincidence rate increases from 74%to more than 80%,which effectively promotes the fine study of carbonate sedimentary microfacies of the Longwangmiao Formation in the GM area of Sichuan Basin.The improved ARFN formula can be applied to any nonmicrobial carbonate strata,especially for dolomite strata with fewer rock types,to achieve rapid and quantitative identification of lithofacies,so this technology is suitable for other similar carbonate rocks.The lithofacies logging identification of the formation has reference and promotion value.However,ARFN technology is established in nonfractured and non-aqueous layers,which limits its application.