红河低渗透油田压裂水平井生产动态分析及预测研究

发布时间：2018-07-21 15:45

【摘要】：随着低渗透油气藏在我国油气产量中所占比重的持续增大，，我国未来油气产量稳产增产将更多地依靠低渗透油气藏，低渗透油气藏正成为中国油气产量增长的主体和储量接替的重要贡献者，而水平井技术结合大规模压裂技术已经成为开发低渗透油气藏的主要手段。水平井裂缝参数的优化，对指导水平井压裂改造，提高水平井产能和经济效益具有重要意义；生产动态分析是油藏治理挖潜，提高整体开发效果与经济效益的重要保障。本文在红河油田长8油藏储层特征研究的基础上，研究了该油藏的渗流规律；研究了影响低渗透油藏压裂水平井产能的主要因素，采用灰色关联分析法，结合红河油田196口水平井的压裂数据，研究确定了16个影响因素及其对压裂水平井产能影响的显著性；采用数值模拟法，研究了裂缝条数、裂缝长度和水平井长度等参数对压裂水平井的产能影响规律。研究表明：对于特定油藏，裂缝条数、裂缝长度和水平井长度等参数与水平井产能之间存在最优化关系；结合红河油田196口压裂水平井的资料，建立了基于BP神经网络方法的压裂水平井产能预测方法；采用传统和现代生产动态分析方法对红河长8油藏进行了动态分析，确定了储层特性参数。采用多种方法对油藏的产量及产水规律进行了研究，确定了适合该油田产量及含水率的预测方法，为该油田的综合治理和增产挖潜提供了依据。本文所建立的基于BP神经网络的压裂水平井产能预测方法，对于指导低渗透油藏进行压裂优化设计，提高压裂增产效果具有普遍指导意义和应用价值。采用对离散非对称性的生产数据进行规格化处理，进而采用现代生产动态分析方法进行分析，不仅提高了分析结果的可靠性，而且能够获取更多的油气藏信息，该方法对于大量缺少不稳定试井资料的低产机械采油井的动态分析提供了有效途径。本文研究成果对于其它低渗透油藏的开发具有一定指导意义。
[Abstract]:With the increasing proportion of low permeability reservoirs in China's oil and gas production, the future oil and gas production and steady production increase in China will depend more on low permeability oil and gas reservoirs. Low permeability reservoirs are becoming the main body of oil and gas production growth and important contributors of reserves replacement in China, and horizontal well technology combined with large-scale fracturing technology has become the main means of developing low permeability reservoirs. The optimization of fracture parameters in horizontal wells is of great significance for guiding the fracturing reconstruction of horizontal wells and improving the productivity and economic benefits of horizontal wells, and the analysis of production performance is an important guarantee for reservoir control and tapping potential, and for improving the overall development effect and economic benefits. Based on the study of reservoir characteristics of Chang 8 reservoir in Honghe Oilfield, the percolation law of this reservoir and the main factors affecting the productivity of fractured horizontal well in low permeability reservoir are studied in this paper. Based on the fracturing data of 196 horizontal wells in Honghe Oilfield, 16 influencing factors and their significance to the productivity of horizontal wells are studied, and the number of fractures is studied by numerical simulation. The effect of fracture length and horizontal well length on the productivity of fractured horizontal well is discussed. The results show that there is an optimal relationship between the number of fractures, the length of fractures and the length of horizontal wells and the productivity of horizontal wells for certain reservoirs, and based on the data of 196 fracturing horizontal wells in Honghe Oilfield, The productivity prediction method of fracturing horizontal well based on BP neural network is established, and the performance analysis of Honghe Chang 8 reservoir is carried out by using traditional and modern production dynamic analysis method, and the reservoir characteristic parameters are determined. Several methods are used to study the production and water production law of the reservoir, and the prediction method suitable for the production and water cut of the oilfield is determined, which provides the basis for the comprehensive control of the oil field and the tapping of the potential for increasing production and water cut. The productivity prediction method based on BP neural network for horizontal well fracturing is of general guiding significance and application value for guiding the optimization design of fracturing in low permeability reservoir and improving the effect of fracturing production. The production data of discrete asymmetry are normalized and analyzed by modern production dynamic analysis method, which not only improves the reliability of the analysis results, but also can obtain more oil and gas reservoir information. This method provides an effective way for the performance analysis of a large number of low-yield mechanical oil wells which lack of unstable well test data. The research results in this paper have certain guiding significance for the development of other low permeability reservoirs.
【学位授予单位】：重庆科技学院
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TE331

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