基于BP神经网络的底水油藏控水压裂选段新方法

发布时间：2019-02-17 19:52

【摘要】：随着水平井压裂技术的普遍应用,压裂段的选择成为影响增产效果的主要因素之一,但针对底水油气藏,在压裂时同时考虑控水同样重要。影响底水油气藏压裂效果的因素很多,因素之间具有高度不确定的非线性关系,给控水压裂选段分析带来了诸多不便。利用传统的单因素回归等方法,显然不能满足底水油藏多种影响因素下水平压裂段的选择。针对此问题,通过油藏工程理论和现场经验,确定了避水高度、地层各向异性、天然裂缝发育程度、地应力差值、水平井曲直偏差、储层与水层之间隔层发育程度六个参数作为影响控水压裂段选择的主要因素,建立定性分析方法。通过底水脊进模型确定了参数界限,结合BP神经网络系统的原理,建立定量分析。利用计算机语言编制底水油气藏水平井压裂段控水选择软件,通过实际应用,表明其对现场施工具有一定的指导作用。
[Abstract]:With the widespread application of horizontal well fracturing technology, the selection of fracturing section becomes one of the main factors that affect the effect of increasing production. However, it is also important to consider water control when fracturing for bottom water reservoirs. There are many factors that affect the fracturing effect of bottom water reservoir, and there is a highly uncertain nonlinear relationship between the factors, which brings much inconvenience to the analysis of fracturing section selection for water control fracturing. The traditional method of single factor regression obviously can not satisfy the choice of horizontal fracturing section under many influence factors in bottom water reservoir. In order to solve this problem, through reservoir engineering theory and field experience, the water escape height, formation anisotropy, natural fracture development degree, ground stress difference, horizontal well deviation are determined. The qualitative analysis method was established by using six parameters of the interval development degree between the reservoir and the water layer as the main factors affecting the selection of the water-controlled fracturing section. The parameter limits are determined by the bottom water ridge model, and the quantitative analysis is established based on the principle of BP neural network system. The computer language is used to compile the water control selection software for horizontal well fracturing section of bottom water reservoir. The practical application shows that it has a certain guiding effect on field operation.
【作者单位】：中国石化集团新星石油有限责任公司;中国地质大学(北京)能源学院;
【分类号】：TE357.1;TP183

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