利用地震属性钻前预测井壁稳定性
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摘要
钻前准确预测井壁稳定性是防止钻进过程中井壁失稳的有效手段,声波时差和地层密度是井壁稳定分析的两个关键参数。根据地震记录与地层声波时差及密度之间存在的非线性关系,提出了利用地震属性钻前预测井壁稳定性的方法。从井旁地震记录中提取地震属性,通过RBF神经网络在已钻井段的地震属性与声波时差及密度之间建立起映射模型。以此为基础预测待钻地层的声波时差和密度。运用预测结果结合井壁稳定力学模型,确定岩石力学参数和地应力状态,计算井壁坍塌压力和破裂压力,确定安全钻井液密度窗口,实现钻前井壁稳定预测。该方法在塔里木油田的应用中取得了良好的预测效果。
Predicting borehole stability accurately before drilling is an effective way to control borehole instability in drilling. The interval transit time and density of formation are key parameters in borehole stability analysis. The method of predicting borehole stability before drilling by using seismic attributes is presented based on the nonlinear relationship between seismic data and interval transit time and density. The seismic attributes are extracted from borehole-side seismic trace firstly. Through radial basis function neural network the mapping model is constructed between seismic attributes and interval transit time and density in drilled formation, based on which the interval transit time and density under borehole bottom is predicted. The prediction results and the borehole stability mechanics model are used to determine rock mechanical parameters and in-situ stress state, and to calculate the collapse pressure and fracture pressure of the borehole, thus obtaining the safe drilling fluid density window to achieve the prediction of borehole stability before drilling. This method has yielded excellent predicting results in practical application.
Predicting borehole stability accurately before drilling is an effective way to control borehole instability in drilling. The interval transit time and density of formation are key parameters in borehole stability analysis. The method of predicting borehole stability before drilling by using seismic attributes is presented based on the nonlinear relationship between seismic data and interval transit time and density. The seismic attributes are extracted from borehole-side seismic trace firstly. Through radial basis function neural network the mapping model is constructed between seismic attributes and interval transit time and density in drilled formation, based on which the interval transit time and density under borehole bottom is predicted. The prediction results and the borehole stability mechanics model are used to determine rock mechanical parameters and in-situ stress state, and to calculate the collapse pressure and fracture pressure of the borehole, thus obtaining the safe drilling fluid density window to achieve the prediction of borehole stability before drilling. This method has yielded excellent predicting results in practical application.
引文
[1] 金衍,陈勉,杨小奇．利用层速度钻前预测安全泥浆密度窗口 研究[J]．岩石力学与工程学报,2004,23(14):2430-2433．
[2] 金衍,陈勉,张旭东．钻前井壁稳定预测方法的研究[J]．石 油学报,2001,22(3):96-99．
[3] 郭凯俊．陈勉,梁红军,等．利用地震记录钻前预测却勒1片 的安全钻井液密度窗口[J]．石油钻探技术,2004,32(4): 26-28．
[4] 王永刚,乐友喜,刘伟,等．地震属性与储层特征的相关性研 究[J]．石油大学学报(自然科学版),2004,28(1):26-30．
[5] Quincy C, Steve S. Seismic attribute technology for forecasting and monitoring [J]. The Leading Edge, 1997, 16 (5), 445-456.
[6] 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．
[7] 杜世通,王永刚．地震参数综合处理方法在储层横向预测中的 应用[J]．石油大学学报(自然科学版),1993,17(1):8 15．
[8] 陈勉,金衍．深井井壁稳定技术研究进展与发展趋势[J]．石 油钻探技术,2005,33(5):28-34．
[9] Robert J, Schilling J, Caroll J. Approximation of nonlinear systems with radial basis function neural network [J]. IEEE Transactions on Neural Network, 2001, 12 (1): 21-28.
[10] Bors A G, Pitas I. Median radial basis function neural network [J]. IEEE Transactions on Neural Network, 1996, 7 (6): 1351-1364.
[11]Elanayar S, Shin Y C. Radial basis function neural network for approximation and estimate on of nonlinear stochastic dynamic system [J]. IEEE Transactions on Neural Network, 1994, 4 (5); 594-604.
[12] 黄荣樽,邓金根,陈勉．井壁坍塌压力和破裂压力的计算模 型[A]．／／徐同台,崔茂荣,王允良,等．钻井工程井壁稳 定新技术[C]．北京:石油工业出版社,1999:104-112．
[2] 金衍,陈勉,张旭东．钻前井壁稳定预测方法的研究[J]．石 油学报,2001,22(3):96-99．
[3] 郭凯俊．陈勉,梁红军,等．利用地震记录钻前预测却勒1片 的安全钻井液密度窗口[J]．石油钻探技术,2004,32(4): 26-28．
[4] 王永刚,乐友喜,刘伟,等．地震属性与储层特征的相关性研 究[J]．石油大学学报(自然科学版),2004,28(1):26-30．
[5] Quincy C, Steve S. Seismic attribute technology for forecasting and monitoring [J]. The Leading Edge, 1997, 16 (5), 445-456.
[6] 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．
[7] 杜世通,王永刚．地震参数综合处理方法在储层横向预测中的 应用[J]．石油大学学报(自然科学版),1993,17(1):8 15．
[8] 陈勉,金衍．深井井壁稳定技术研究进展与发展趋势[J]．石 油钻探技术,2005,33(5):28-34．
[9] Robert J, Schilling J, Caroll J. Approximation of nonlinear systems with radial basis function neural network [J]. IEEE Transactions on Neural Network, 2001, 12 (1): 21-28.
[10] Bors A G, Pitas I. Median radial basis function neural network [J]. IEEE Transactions on Neural Network, 1996, 7 (6): 1351-1364.
[11]Elanayar S, Shin Y C. Radial basis function neural network for approximation and estimate on of nonlinear stochastic dynamic system [J]. IEEE Transactions on Neural Network, 1994, 4 (5); 594-604.
[12] 黄荣樽,邓金根,陈勉．井壁坍塌压力和破裂压力的计算模 型[A]．／／徐同台,崔茂荣,王允良,等．钻井工程井壁稳 定新技术[C]．北京:石油工业出版社,1999:104-112．
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