基于模糊神经网络的致密砂岩储层反演——以长岭断陷1号气田登娄库组为例

doi:10.11743/ogg20130320

石油与天然气地质 ›› 2013, Vol. 34 ›› Issue (3): 413-420.doi: 10.11743/ogg20130320

• 技术方法 • 上一篇

基于模糊神经网络的致密砂岩储层反演——以长岭断陷1号气田登娄库组为例

艾宁^1,2, 唐永^3,4, 杨文龙⁵, 沈传波⁴, 王彦卿⁶, 黄文芳⁷, 尚婷⁸

1. 西北大学地质系, 陕西西安 710069;
2. 宁夏地质调查院, 宁夏银川 750021;
3. 浙江大学地球科学系, 浙江杭州 310027;
4. 中国地质大学构造与油气资源教育部重点实验室, 湖北武汉 430074;
5. 中国石油西部钻探工程公司苏里格第一项目经理部, 内蒙古乌审旗 173000;
6. 中国石油长庆油田分公司石油勘探开发研究院, 陕西西安 710021;
7. 中国石油长庆油田分公司第三采油厂, 宁夏银川 750006;
8. 延长石油有限责任公司石油勘探开发研究院, 陕西西安 710069

收稿日期:2013-03-22 修回日期:2013-05-08 出版日期:2013-06-28 发布日期:2013-07-17
作者简介:艾宁(1984- ),女,博士研究生,矿产普查与勘探。E-mail:ainin666@126.com。
基金资助:
中国博士后科学基金资助项目(2012M521160);中国地质大学构造与油气资源教育部重点实验室开放研究基金项目(TPR-2011-26)。

Fuzzy neural network-based tight sandstone reservoir inversion—a case study from the Denglouku Formation in Changling 1 gas field

Ai Ning^1,2, Tang Yong^3,4, Yang Wenlong⁵, Shen Chuanbo⁴, Wang Yanqing⁶, Huang Wenfang⁷, Shang Ting⁸

1. Department of Geology, Northwest University, Xi'an, Shaanxi 710069, China;
2. Geological Survey of Ningxia, Yinchuan, Ningxia 750021, China;
3. Department of Earth Sciences, Zhejiang University, Hangzhou, Zhejiang 310027, China;
4. Key Laboratory of Tectonics and Petroleum Resources of Ministry of Education, China University of Geosciences, Wuhan, Hubei 430074, China;
5. First Western Drilling Project Manager Department in Sulige, PetroChina West Drilling Engineering Company, Wushenqi, Inner Mongolia 173000, China;
6. Research Institute of Petroleum Exploration and Development, PetroChina Changqing Oilfield Company, Xi'an, Shaanxi 710021, China;
7. No.3 Oil Producing Plant, PetroChina Changqing Oilfield Company, Yinchuan, Ningxia 750006, China;
8. Research Institute of Petroleum Exploration and Development, Yanchang Petroleum (Group) Ltd.Company, Xi'an, Shaanxi 710069, China

Received:2013-03-22 Revised:2013-05-08 Online:2013-06-28 Published:2013-07-17

摘要/Abstract

摘要： 模糊神经网络储层反演,能够较好地摈弃不同类型、不同尺度数据的揉合,较好地保留了数据完整性,这既能反映整体的变化趋势,又能刻画局部细致变化特征。针对长岭1号气田登娄库组相变快、砂体横向连续性较差的特点,综合地震、测井、钻井测试分析资料,应用模糊神经网络原理,反演了研究区目的层段砂岩厚度与孔隙度特征。研究结果显示:①长岭1号气田登娄库组各段砂体平面展布变化较大,但厚度较大的区域均集中于长深103—长深1-3—长深1-1井和长深2井附近,向东北呈逐渐减薄趋势,D3岩层段长深103井周围岩石厚度可达33.2 m;②研究区登娄库组主力产油层D3和D4岩层段孔隙度较低,平均仅为5%左右,砂体孔隙度相对较高区域(>9%)仅集中于长深1井附近,整体显示登娄库组岩性较为致密;③反演结果与实际测试数据对比显示,砂体厚度误差控制在2.5 m以内,砂体孔隙度误差在0.49%以下,结果可靠性较好。利用模糊神经网络原理进行储层反演分析,能够很好地展现储集砂体分布规律及储集性能。

关键词: 模糊化, 神经网络, 地震反演, 登娄库组, 长岭断陷

Abstract: Fuzzy neural network reservoir inversion can avoid the integration of data of different types and scales and well preserve the data integrity,making it possible to reveal both the overall variation tendency and the local detailed features.In view of the rapid facies change and poor lateral continuity of the Denglouku Formation sandstone in the Changling1 gas field,we integrated various data including seismic,well logging,drilling and well testing and performed inversion of the sandstone thickness and porosity characters by using the fuzzy neural network theory.The following results were obtained.①The areal distributions of the Denglouku Formation sandstones in the Changling1 gas field varies greatly,with the thicker sandtone occurring mainly in areas along the Changshen 103,Changshen 1-3,Changshen 1-1 wells and the Changshen 2 well.The thickness of sandstone decreases progressively in northeast direction.The thickness of D3 layer near in the Changshen 103 well is up to 33.2m.②The major D3 and D4 pay zones in the Denglouku Formation have a relatively low porosity,averaging at only about 5%.The sandbodies with relatively high porosity(>9%)only occur in area around the Changshen 1 well.The Denglouku Formation is tight as a whole.③Comparison of the inversion results with the actual test data shows that the error of sandstone thickness is within 2.5 m and the error of the porosity is under 0.49%,indicating a high reliability of the inversion results.It is concluded that fuzzy neural network-based inversion of tight sandstone reservoirs can well reveal sandstone distribution and reservoir properties.

Key words: fuzzy, neural network, seismic inversion, Denglouku Formation, Changling faulted depression

中图分类号:

TE122.1

艾宁,唐永,杨文龙,等. 基于模糊神经网络的致密砂岩储层反演——以长岭断陷1号气田登娄库组为例[J]. 石油与天然气地质, 2013, 34(3): 413-420. doi: 10.11743/ogg20130320 Ai Ning,Tang Yong,Yang Wenlong,et al. Fuzzy neural network-based tight sandstone reservoir inversion—a case study from the Denglouku Formation in Changling 1 gas field[J]. Oil & Gas Geology, 2013, 34(3): 413-420. doi: 10.11743/ogg20130320

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基于模糊神经网络的致密砂岩储层反演——以长岭断陷1号气田登娄库组为例

Fuzzy neural network-based tight sandstone reservoir inversion—a case study from the Denglouku Formation in Changling 1 gas field

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