沁水盆地南部煤岩储层天然裂缝普遍发育,天然裂缝有效性是决定煤岩储层渗流能力、影响煤层气井能否高产的重要因素.综合利用野外露头、岩心和扫描电镜等资料,在分析沁水盆地南部上古生界煤岩储层天然裂缝类型和发育特征的基础上,对裂缝有效性及其主控因素开展研究,并结合单井生产动态资料,探讨了裂缝有效性对煤层气开发的影响.结果表明:沁水盆地南部煤岩储层主要发育割理和构造裂缝,其中割理包括面割理和端割理,构造裂缝包括剪切裂缝和张性裂缝.受多种地质因素影响,不同类型裂缝的有效性存在明显差异.割理形成于煤化作用阶段,多被方解石和粘土矿物等全充填或半充填,整体上其裂缝有效性较差.构造裂缝多未被矿物充填,其有效性主要受控于裂缝规模、倾角、储层埋深及与现今地应力最大主应力方向夹角.构造裂缝开度大、延伸较远,裂缝有效性好;高角度构造裂缝有效性好于斜交缝;受地应力状态变化影响,随储层埋深的增加,裂缝有效性依次表现为较差、较好、较差;NE-SW向裂缝有效性最好,其次为近EW向和近NS向裂缝,NW-SE向裂缝有效性最差.相比于割理,构造裂缝在规模和裂缝有效性方面均好于前者,可作为煤层主要的渗流和产出通道,对煤层气的开发具有明显积极作用.然而,过大规模尺度的构造裂缝,尤其当构造裂缝穿过煤层在顶底板发育时,不仅会造成煤岩储层内煤层气散失和储层压力降低,同时还将导致外来地层水对煤层的补充,不利于煤层气的保存和有效开采.

Natural fractures, as the effective storage space and fluid flow pathway of hydrocarbons, play a critical role in the exploration and development of unconventional gas resources in the coal bearing strata. In the present study, to reveal the effects of natural fractures on coalbed methane (CBM) preservation and development, the types, origins and development characteristics of natural fractures in the upper Paleozoic Permian Shanxi Formation in the southern Qinshui Basin were systematically described and analyzed based on outcrops, cores, borehole image logs, thin sections, and SEM. Four types of natural fractures have been identified in the Shanxi Formation according to their geologic origins, including the endogenic fractures (cleats) and exogenous fractures in the No.3 coal seam, and tectonic fractures and diagenetic fractures in its roof and floor. Tectonic fractures can be further divided into small faults, intraformational shear fractures, intraformational open fractures and slip fractures, while diagenetic fractures are mainly bed-parallel fractures. The statistical results show that the development and distribution characteristics of those types of fractures are significantly different, which are mainly influenced by lithology, layer thickness and geological structure. Further, natural fractures with different types and distributions show different influences on CBM preservation and well productivity. Exogenous fractures can effectively improve the seepage capability of coal reservoirs and are conducive to CBM wells productivity, whereas endogenic fractures have less contributions to coal permeability due to their poor effectiveness. The intraformational open fractures in the fold axis zones, and intraformational shear fractures and small faults in the fault zones are generally excessive-developed. Those fractures may result in gas dissipation and external water supplement, which have negative effects on CBM preservation and production. However, moderate-developed intraformational shear fractures in the sandstone roof can favorably affect the pressure reduction of coal reservoir and improve the recovery potential of unconventional gas resources. These investigations may serve as a geological basis for unconventional gas exploration and development of the upper Paleozoic coal bearing strata in the southern Qinshui Basin.

Coal structure is closely related to the porosity and permeability of coal reservoirs, which not only affects the enrichment of coalbed methane (CBM), but also influences the hydraulic fracturing and efficient development of CBM. The accurate identification of the coal structure would be a critical issue in CBM exploration and development, and is always a challenge. Compared with traditional methods for identifying coal structure base on borehole cores or mining seam observation, geophysical logging has become the most economic and efficient technique. Several linear correlations have been established to describe the relationships between the coal structures and well logs. However, those correlations cannot accurately reflect nonlinear relations between them. Therefore, a reliable and efficient method to identify coal structure is needed. As a powerful nonlinear classifier, the kernel Fisher discriminant analysis (KFD) method has been widely used due to its strong generalization ability. In this paper, a new quantitative coal structure identification model was developed based on the KFD method by using geophysical logging data. The model was trained, tested and optimized using 178 logging data sets from 15 CBM wells in Qinshui Basin, China. The approach accounted for all the available well logging attributes, and the training data sizes and kernel parameters were analyzed to get the most appropriate model in practice. In addition, the built model was validated individually by employing logging data of a new CBM well. The results indicate that the KFD based identification model has high prediction accuracy, which can be used as a reliable method for coal structure identification. The KFD method exhibits strong capability during the modeling and generalization in the determination of nonlinear relationships, which provides an efficient way for the coal structure prediction in basic research of coal reservoirs.