针对特高含水期油田常规井间储层预测方法多解性强,无法满足剩余油挖潜需求的难题,以大庆长垣老区为例,开展叠前反演储层预测研究,进一步提高特高含水阶段油田井间储层描述精度,实现储层描述的定量化和精准化。首先通过储层岩石物理模拟分析,确定砂岩与泥岩门槛值和界限;其次,开展地震角道集部分叠加、子波提取等关键环节处理,建立井震结合桥梁;最后,在Zoeppritz理论方程分析的基础上,以纵横波测井数据为出发点、井间基于不同角度叠加地震与子波为控制,采用蒙特卡洛算法实现纵、横波联合储层砂体预测。研究发现,与单一的纵波阻抗叠后反演预测结果相比,叠前反演薄层砂体预测结果与后验井符合精度更高,有效指导了潜力区的剩余油挖潜方案编制,单井压裂措施之后,增油3.5t/d,含水下降10%。该方法适用于特高含水期油田储层精准预测和剩余油挖潜。
Conventional methods for inter-well reservoir prediction in oilfields at the ultra-high water cut stage provide highly inconsistent results.Therefore,they are unhelpful for evaluating the tapping potential of the remaining oil.In this study,taking the old area of the Daqing Placanticline as a case study,research on reservoir prediction with prestack inversion was carried out to improve the accuracy of inter-well reservoir description in oilfields at the ultra-high water cut stage and achieve quantitative and accurate reservoir description.First,the threshold value and boundary of the sandstone and mudstone layers were determined through a physical simulation of the reservoir rock.Then,a well-seismic combination bridge was established after partial superposition of seismic angle gathers and wavelet extraction.Finally,on the basis of the theoretical Zoeppritz equations,by inputting P-S wave logging data and using inter-well seismic stacking data from different angles and wavelets for verification,a Monte Carlo simulation was performed to predict the reservoir sand bodies.A comparison between P-wave impedance post-stack inversion and pre-stack inversion results showed that the latter were more accurate and could effectively help in formulating an exploitation program for the remaining oil in the study area.In fact,after performing fracturing in a single well,the daily oil production increased by 3.5 tons and the water cut decreased by 10%.It is concluded that the proposed method can be useful for accurate reservoir prediction and evaluation of the tapping potential of the remaining oil in oilfields at the ultra-high water cut stage.
国家自然科学基金面上项目(51974247,51874241)、国家自然科学基金青年项目(41502311)以及陕西省教育厅重点实验室科研计划项目(15JS088)共同资助。