摘要
通过挖掘多源异构多尺度数据中的裂缝信息降低裂缝预测的不确定性,在单井裂缝识别裂缝指示参数法的基础上,改进3种人工智能方法,从小样本分类预测、多尺度非线性特征提取、预测模型方差减小提升裂缝识别精度。井间裂缝发育趋势预测方法是通过人工智能地震属性裂缝预测获取井间裂缝带细节,与地质力学数值模拟获得的断层相关裂缝信息互补,提高裂缝预测的可靠性。最后通过协同序贯模拟耦合单井与井间裂缝信息,生成裂缝网络建模所需的裂缝密度体。以中东扎格罗斯盆地A油田渐新统—中新统AS组致密碳酸盐岩储集层为例,对该方法进行了应用和检验。结果表明,单井裂缝识别准确率相比常规裂缝指示参数法提高15个百分点以上,井间裂缝发育趋势预测法相比复合地震属性预测提高25个百分点以上,所建裂缝网络模型与产液指数具有较好一致性。
An intelligent prediction method for fractures in tight carbonate reservoir has been established by upgrading single-well fracture identification and interwell fracture trend prediction with artificial intelligence,modifying construction of interwell fracture density model,and modeling fracture network and making fracture property equivalence.This method deeply mines fracture information in multi-source isomerous data of different scales to reduce uncertainties of fracture prediction.Based on conventional fracture indicating parameter method,a prediction method of single-well fractures has been worked out by using 3 kinds of artificial intelligence methods to improve fracture identification accuracy from 3 aspects,small sample classification,multi-scale nonlinear feature extraction,and decreasing variance of the prediction model.Fracture prediction by artificial intelligence using seismic attributes provides many details of inter-well fractures.It is combined with fault-related fracture information predicted by numerical simulation of reservoir geomechanics to improve inter-well fracture trend prediction.An interwell fracture density model for fracture network modeling is built by coupling single well fracture identification and interwell fracture trend through co-sequential simulation.By taking the tight carbonate reservoir of Oligocene-Miocene AS Formation of A Oilfield in Zagros Basin of the Middle East as an example,the proposed prediction method was applied and verified.The single-well fracture identification improves over 15%compared with the conventional fracture indication parameter method in accuracy rate,and the inter-well fracture prediction improves over 25%compared with the composite seismic attribute prediction.The established fracture network model is well consistent with the fluid production index.
作者
董少群
曾联波
杜相仪
鲍明阳
吕文雅
冀春秋
郝静茹
DONG Shaoqun;ZENG Lianbo;DU Xiangyi;BAO Mingyang;LYU Wenya;JI Chunqiu;HAO Jingru(State Key Laboratory of Petroleum Resources and Prospecting,China University of Petroleum,Beijing 102249,China;College of Science,China University of Petroleum,Beijing 102249,China;College of Geoscience,China University of Petroleum,Beijing 102249,China)
出处
《石油勘探与开发》
SCIE
EI
CAS
CSCD
北大核心
2022年第6期1179-1189,共11页
Petroleum Exploration and Development
基金
国家自然科学基金青年资助项目(42002134)
中国博士后科学基金第14批特别资助项目(2021T140735)。
关键词
裂缝测井识别
井间裂缝预测
裂缝密度体
裂缝网络模型
人工智能
致密碳酸盐岩
扎格罗斯盆地
fracture identification by well logs
interwell fracture trend prediction
interwell fracture density model
fracture network model
artificial intelligence
tight carbonate reservoir
Zagros Basin
