五百梯气田石炭系储层裂缝测井识别研究

Study on Fracture Logging Identification of Carboniferous Reservoir in Wubaiti Gas Field

对于致密的碳酸盐岩地层,裂缝的发育程度影响着油气藏的经济开采价值。本文基于五百梯气田40口井的测井数据及岩心等资料,运用神经网络技术分析储层孔隙度及渗透率与测井值之间的相关性,利用对裂缝响应特征明显的深侧向电阻率(RLLD)、声波(AC)及密度(DEN)测井值对石炭系储层裂缝进行识别并定量反映裂缝的发育程度。研究表明:孔隙度模型的输入曲线为RLLD、浅侧向电阻率(RXO)、DEN等6条,渗透率模型中的输入曲线为RLLD、AC、补偿中子(CNL)等6条。裂缝的测井响应特征参数区间为:RLLD<1000Ω·m,AC>46μs/ft,DEN<2.7 g/cm3,石炭系储层裂缝张开度约在20~40μm左右,裂缝孔隙度一般在0.14%左右,裂缝渗透率一般在3×10-3 m2左右。可知,裂缝对储层孔隙度的影响不明显,但对储层渗透率的贡献非常明显。

For dense formation of carbonate rock, the development of fractures affects the economic exploitation of oil and gas accumulation. Based on well logging data and core data of 40 wells in the Wubaiti Gas Field, this paper uses neural network technology to analyze the correlation between reservoir porosity and permeability and logging values, using the deep lateral resistivity（RLLD）, acoustics（AC）, and density（DEN） logging values that have significant fracture response characteristics, the fractures of the Carboniferous reservoirs were identified and the fracture development degree was quantitatively reflected. The results show that the input curves of the porosity model are 6 items such as RLLD, shallow lateral resistivity（RXO） and DEN. The input curves in the permeability model are 6 items such as RLLD, AC and compensated neutron（CNL）. Logging response characteristic parameters of fractures are： RLLD1000 Ω·m, AC46 μs/ft, DEN2.7 g/cm3, fracture opening of Carboniferous reservoirs is approximately between 20~40 μm, fracture porosity is generally about 0.14%, the fracture permeability is generally about 3×10-3 m2. It can be seen that the effect of fractures on reservoir porosity is not obvious, but the contribution to reservoir permeability is very obvious.