Underpressure System and Forming Mechanism in the Shiwu Depression of Songliao Basin
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摘要: 松辽盆地东南隆起区十屋断陷发育异常低压体系, 实测的地层压力梯度介于0.005~0.008 MPa/m.研究结果表明异常低压体系的形成既与地温梯度的降低有关, 又与新生代该区的地层抬升和剥蚀有关.此外, 天然气扩散作用对局部异常低压体系的形成具有一定的影响.研究区地层压力系统演化经历了从异常高压向异常低压演化过程, 这一过程决定了盆地不同演化时期的水流系统及其油气运聚特征, 从而使得十屋断陷具有石油成藏早且主要分布于断陷边缘、天然气成藏晚且主要分布于断陷中央的成藏特点.Abstract: Underpressure system developed in the Shiwu depression of Songliao basin with measured formation pressure gradient ranging between 0.005 MPa/m to 0.008 MPa/m. This research result shows that the genetic mechanism of abnormal low pressure attributes to reduction of paleothermal gradients, uplift and erosion during Eocene period. Moreover, gas diffusions also affect the formation of underpressure system at local part. This system at present indicates the evolution process from overpressure to underpressure, which controls fluid flow and hydrocarbon migration in different stages of the depression. Hence, oil pools generating at early stage occur at the basin margin, and gas pools generating at late stage at central part of the Shiwu depression, Songliao basin.
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Key words:
- underpressure system /
- Shiwu depression /
- Songliao basin
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图 2 十屋断陷主要储层DST实测压力与深度关系
Fig. 2. Measured formation pressure versus depth profile in main reservoirs of the Shiwu depression
图 3 十屋断陷SN10井泥岩声波时差随深度的变化
Fig. 3. Transit time versus depth profile for the mudstones in well SN10
图 4 假想模型中模拟剩余压力与热流变化关系
Fig. 4. Relationship of calculated excess pressure versus heat flow in a model with two layers
图 5 假想模型中模拟剩余压力与剥蚀速率关系
a.剥蚀速率为5 m/Ma; b.剥蚀速率为15 m/Ma; c.剥蚀速率为50 m/Ma
Fig. 5. Relationship of calculated excess pressure versus erosion rate in a model with two layers
图 6 十屋断陷A-A′剖面剩余压力随时间变化
a, b为异常超压; c为异常低压.剖面位置见图 1
Fig. 6. Evolutionary processes of excess pressure in the Shiwu depression
图 7 十屋断陷SN28井不同时期地层压力与深度关系
Fig. 7. Pore pressure versus depth profile at well SN28 of the Shiwu depression, showing the variation of excess pressure in different periods
表 1 模拟中采用孔隙度和渗透率参数
Table 1. Parameters of porosity and permeability in the model
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