论文详情
应用水平井H1,H2和H3的微地震、压裂及地震数据综合分析了四川盆地W地区下志留统龙马溪组页岩储层特征。由微地震监测数据分析可知,H1井的压裂主要以激活先期天然裂缝为主,H2井的压裂以形成人工压裂裂缝网络为主,H3井的压裂则以激活先期天然裂缝和人工压裂裂缝网络共同出现为主。分析了有无天然裂缝以及天然裂缝与井筒不同产状关系情况下不同的压裂效果,进而评价了3口水平井的压裂效果,H2井压裂效果最好,H3井次之,H1井较差。最后,利用神经网络技术建立了页岩四大主控因素(总含气量、孔隙度、脆性指数和裂缝密度)地质模型,获得页岩产能数据并将其与具有生产测井数据的H1水平井进行了对比,结合微地震压裂数据对H2井、H3井产能进行了预测,预测结果为H2井产能是H3井的2倍,实际生产测试为2.3倍,吻合性好,页岩产能剖面优化了水平井部署、井轨迹设计、压裂方案参数以及更好的完井设计。
The shale gas reservoir characterization in area W of the Sichuan Basin is analyzed using seismic,fracturing,and micro-seismic data from three horizontal wells.From the analysis of micro-seismic monitoring data,we know that after fracturing,pre-existing natural faults were reactivated in H1,an artificial fracture network was formed in H2,and both pre-existing faults and artificial fractures were reactivated in H3.The fracturing effect is mainly affected by natural fractures and the relationship between natural fractures and wellbore occurrence.The analysis of fracturing results for the three wells indicates that H2 had the best fracturing effect,H3 had a medium effect,and H1 had the worse effect.Finally,geological models based on four key driver factors,total gas content,porosity,brittleness index and fracture density,are built using a neural network to obtain the production capacity of shale reservoirs.The trend of the obtained production capacity data is coincident with H1s productive logging data.By integrating that result with the microseismic fracturing monitoring data,the predicted production capacity of the shale reservoir in H2 could be twice that of H3;the actual result is 2.3 times that of H3.The resulting production capacity profiles of the shale reservoir can be used to optimize well deployment,horizontal well track design,hydraulic fracturing geometry layout,and improve well completion programs.
国家科技重大专项“大型油气田及煤层气开发”(2016ZX05023004)资助。