根据地震属性预测乌夏断裂带深层火山岩储集层
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摘要
准噶尔盆地乌夏断裂带深层二叠系储集层埋藏深、断裂发育、地震反射较杂乱、构造复杂,给深层储集层预测增加了难度。钻井资料显示,二叠系火山岩储集层比较发育,是油气聚集的有利区带。根据研究区火山岩的地质和地球物理特点,在层位精细标定和解释的基础上,沿层提取多种地震属性,并对优选出的平均绝对振幅、弧线长度和能量半衰时梯度三种属性,采用Kohonen神经网络方法实现综合分类,利用分类结果对二叠系风城组的火山岩储集层进行横向预测,综合考虑钻井、地质和地球物理的研究成果确定了钻探井位,取得了良好的应用效果。
The deep reservoir (Permian) of Wu-Xia fault belt in Junggar basin is characterized by the deep buried, developed faults, random seismic reflection and complex structures, etc. These make the prediction of the deep reservoir more difficult. The drilling data show that the developed volcanic reservoirs of Permian serve as the favorable zone for hydrocarbon accumulation. This paper presents the geologic and geophysical features of the volcanic rocks in Wu-Xia fault belt and their fine horizon calibration and interpretation, based on which several seismic attributes are picked up along the horizons, of which three attributes are selected such as average absolute amplitude, arc length and energy gradient at half (life) period. And the comprehensive classification given by means of Kohonen neural network is put forward, by which the lateral prediction of the volcanic reservoirs in Fengcheng formation of Permian is conducted and the next drilling well sites are determined, considering previous drilling, geologic and geophysical research results. The study shows that the seismic attributes are of good effects on the deep volcanic reservoir predictions.
The deep reservoir (Permian) of Wu-Xia fault belt in Junggar basin is characterized by the deep buried, developed faults, random seismic reflection and complex structures, etc. These make the prediction of the deep reservoir more difficult. The drilling data show that the developed volcanic reservoirs of Permian serve as the favorable zone for hydrocarbon accumulation. This paper presents the geologic and geophysical features of the volcanic rocks in Wu-Xia fault belt and their fine horizon calibration and interpretation, based on which several seismic attributes are picked up along the horizons, of which three attributes are selected such as average absolute amplitude, arc length and energy gradient at half (life) period. And the comprehensive classification given by means of Kohonen neural network is put forward, by which the lateral prediction of the volcanic reservoirs in Fengcheng formation of Permian is conducted and the next drilling well sites are determined, considering previous drilling, geologic and geophysical research results. The study shows that the seismic attributes are of good effects on the deep volcanic reservoir predictions.
引文
[1]刘传虎.地震属性与非构造油气藏勘探[J].新疆石油地质,2005,26(5):485-488.
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[9]李艳丽,王国军.针对火山岩预测的目标处理与火山岩储层预测新技术[J].石油地球物理勘探,2002,37(5):541-546.
[10]傅强,王家林,周祖翼.自组织特征映射网络在储层识别中的应用[J].同济大学学报,1999,27(3):371-374.
[2]李庆忠.走向精确勘探的道路[M].北京:石油工业出版社,1993:1-35.
[3]钟俊义,贾曙光.地震属性参数在安棚深层系储层预测中的应用[J].石油物探,2003,42(1):82-85.
[4]王永刚,谢东,乐友喜,等.地震属性分析技术在储层预测中的应用[J].石油大学学报(自然科学版),2003,27(3):30-32.
[5]吕复苏,黄小平.地震属性信息在砂砾岩油藏开发中的应用[J].新疆石油地质,2003,24(4):310-312.
[6]BrownA R.Seismic attributes and their classification[J].The LeadingEdge,1996,15(10):1090.
[7]Quincy Chen,Steve Sidney.Seismic attribute technology for reservoir forecasting and monitoring[J].The Leading Edge,1997,16(5):445-456.
[8]Walls J D,Taner MT,Taylor G.Seismic reservoir characteri-zation of a U.S.Midcontinent fluvial system using rock physics,poststack seismic attributes,and neural networks[J].The LeadingEdge,2002,21(5):428-436.
[9]李艳丽,王国军.针对火山岩预测的目标处理与火山岩储层预测新技术[J].石油地球物理勘探,2002,37(5):541-546.
[10]傅强,王家林,周祖翼.自组织特征映射网络在储层识别中的应用[J].同济大学学报,1999,27(3):371-374.
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