Abstract: Deep volcanic reservoir in Xushen Gas Field is featured with complex and multivariant lithology and fluid composition, low crystallization degree and similar acidic rock composition. Effect of rock framework on resistivity exceeds that of reservoir fluids. Therefore, there is lots of difficulty in fine logging evaluation of volcanic reservoir, while precise identification of volcanic lithology is the basis and keystone for further research. Aiming at the difficulty of lithology identification in this area, it makes full use of advantages of Elemental Capture Spectroscopy (ECS), electric imaging, nuclear magnetic logging data on lithology identification, produces the knowledge of lithology identification by combining composition with structure, and determines the classification system of volcanic rock. Through utilizing TAS graph, graph mode and neural network methods, well logging lithology identification of volcanic rocks is realized, and it lays a firm foundation for fined logging evaluation of volcanic reservoir.

Key words: volcanic reservoir, lithology identification, Elemental Capture Spectroscopy, electric imaging, neural network

摘要： 徐深气田深层火山岩储层岩性、流体成分复杂多变,结晶程度较差,酸性岩类成分比较接近,岩石骨架对电阻率的影响超过储层流体的影响,因而在火山岩储层精细测井评价方面存在较大困难。其中准确确定火山岩岩性是开展进一步研究工作的基础和关键。针对该区岩性识别难点,充分发挥元素俘获谱(ECS)、电成像、核磁等测井资料在岩性识别上的优势,制定了组分与结构相结合的岩性识别思路,确定了火山岩岩石分类系统,应用TAS图、图像模式、神经网络等3种方法,实现了对火山岩的测井岩性识别,为火山岩储层精细测井评价打下了坚实的基础。

关键词: 火山岩, 岩性识别, 元素俘获谱测井, 电成像, 神经网络