摘要
辽河油田陆家堡凹陷主要发育中生代储集层 ,含油气资源比较丰富 ,地质情况非常复杂 ,测井解释困难。针对本地区低孔隙度低渗透率的特点 ,采用人工神经网络等新的理论 ,并结合常规方法提出行之有效的储层泥质含量、孔隙度、粒度中值、饱和度以及渗透率的计算评价方法。通过对 2 0余口井的数据处理发现 ,解释精度得到了很大的提高 ,其中包1井的 940～ 990m测井解释结果和试油结论完全吻合。
Lujiapu depression in Liaohe Oilfield is a key exploration area which is composed of Mesozosic reservoir and bears rich oil and gas. The complication of geological condition causes many difficulties in logging data interpretation. Considering the characteristics of low porosity and low permeability in the depression, effective evaluation methods for shale volume, porosity, medium diameter of particle, oil saturation and permeability are presented using Artificial Neural Network technique combined with conventional methods. Logging data processing of more than 20 wells proved that the interpretation accuracy of the above methods is improved,and the interpretation result of Well Bao 1 is in good agreement with the oil test conclusion.
出处
《测井技术》
CAS
CSCD
2004年第2期132-134,共3页
Well Logging Technology
