G─LOG技术在泌阳凹陷资料处理中的应用

The application of G-LOG technique for data processing in Biyang Depression

1. 地矿部石油物探研究所, 南京210014;2. 河南石油勘探局研究院, 南阳473132

Rcsearch Institutc of Gcophysical Prospccting for Pctrolcum, Nanjing 210014

泌阳凹陷是一个勘探程度很高的地区, 80年代后期开展了以隐蔽油藏为主要对象的深化勘探。G－LOG处理的目的就是要查清探区内的砂体展布变化情况, 为储展横向预测和寻找隐蔽油藏圈闭提供布井依据。G－LOG是一种发展比较完善的合成声波测井技术。本次处理工作是在三维偏移数据体的基础上, 抽取48条连井线进行。本文以实际处理效果为例, 对G－LOG处理的原理、方法和技术进行较为详细的讨论。处理中的技术关键是做好地震数据的精细处理, 为G－LOG提取相对速度(即高频分量)奠定基础;还要做好合成地震记录处理, 为拾取合理、可信的低频分量提供保证。交点闭合是本次处理的难点之一。在取得良好的G－LOG资料基础上, 进行了深入、细致的综合分析。我们在核三段Ⅲ砂组预测了三个岩性圈闭, 布探井一口。经钻探证实了三个砂组的预测结果, 并在其中一个砂层组所夹的小层内, 见到了工业油流。从而, 为G－LOG资料在储层横向预测及寻找岩性油藏方面的应用, 探索出了一个有效途径。

Biyang Depression has been a maturing exploratory area. During the latter part of 1980s detailed survey was the work in this area mainly to discover concealed reservoirs. G-LOG is utilized in data processing to find out sandstone distribution which can be used to transversely predict reservoirs and to arrange drilling positions. G-LOG has been developed to be a comparatively perfect syntetic acoustic logging technique.The processing has been done on the basis of 3-D migrated data volume with 48 well-connecting lines being selected. G-LOG is discussed in detail about its principle,method and technique with real examples. The tochnological crux of the processing is how to precisely process data for compution relative velocities (i. e. component of high frequency); and the processing of synthetic seismic record should be done properly to quarantee the pickup of reasonablc and reliable low frequency components. Intersection point closure is one of the difficulties in the processing. Synthhetic anaipeis is made thoroughly and meticulously based on the obtained good G-LOG data. Three lithologic closures are prcdicted at Sansha Group,Hesan Section. And one exploratory well is determined. The drilling result confermed the above prediction,with commcrcial oil being found in a sub-layer withhin one of the three lithologic closures. So an effective solution is found to the application of G-LOG data in transverse prediction of reservoirs as well as in the exploration of lithologic reservoirs.