甘肃庆城庄19井区上三叠统延长组长82亚油层组 低渗透储层的地质特征

长82亚油层组是甘肃庆城地区庄19井区上三叠统延长组中储集砂岩相对富集的层位,但砂岩低渗透性的特点显著,成为影响该区石油储产量增长的主要地质因素.结合前人的相关工作,通过钻井岩心观察、测井曲线分析、储层岩石实验测试等工作,详细地分析了庄19井区长82亚油层组低渗透储层的地质特征,认为沉积微相和压实作用、胶结作用是控制低渗透性储集砂岩发育和分布的主要地质因素,寻找以水下分流河道微相为代表的有利储集相带砂岩体是油气勘探的重要方向.     

鄂尔多斯盆地黄陵、东胜地区地温场对比

鄂尔多斯盆地黄陵、东胜铀矿区分别处于盆地南部渭北隆起的北侧边缘和盆地北部伊盟隆起的东部,赋矿层位都是中侏罗统直罗组。盆地南、北铀矿区在现今地温场及古地温场都存在明显差异,南部现今大地热值及热演化程度明显高于北部。对于下侏罗统延安组和石炭—二叠系煤层,黄陵地区镜质体反射率都高于东胜地区。通过镜质体反射率资料得出同一埋深的一套地层经历的最大古地温和对应的古地温梯度也有南部高于北部的现象。由于早白垩世后期盆地普遍整体抬升使得现今地温相对古地温降低,南部黄陵地区抬升剥蚀量大于北部东胜地区,导致古、今地温差异也大于后者。盆地南部庆阳—富县一带局部构造热运动,导致南部异常地温场的形成,使得南部热演化程度高于北部。     

幔源CO_2释出机理、脱气模式及成藏机制研究进展

针对幔源CO2如何从地幔岩浆中脱出并进入沉积地层中形成CO2气藏聚集这一关键问题,总结了国内外研究进展和前缘方向。研究表明,地幔深部的碱性玄武岩浆和碱性岩浆才是深部流体和CO2等挥发份大量赋存、渗滤和释出的场所。浅成侵入岩、次火山岩和火山通道等是CO2释放和聚集的有利位置,岩浆期后和岩浆衰弱期的热液活动阶段是CO2大量释放和聚集的有利时期。幔源CO2进入沉积盆地中具有3种脱气模式,即沿岩石圈断裂直接脱气模式、热流底辟体脱气模式和壳内岩浆房-基底断裂组合脱气模式。CO2的固有物化性质决定其运移相态多样,具有运移和聚集过程同步的特征。只有在满足大量的化学消耗及地层水或原油的溶解和耗散之后才能形成CO2有效聚集。幔源CO2成藏和分布主要受岩浆气源体和气源断裂体系的控制。今后,在超临界CO2及其对油气运移聚集的作用、CO2与深大断裂及火山岩的关系、CO2脱气运移机制、CO与常规烃类油气的耦合差异成藏机制等方面仍需要进一步的研究和探索。     

苏里格气田盒8气藏气水层测井识别研究

鄂尔多斯盆地苏里格气田盒8气藏为弹性驱动的河流相低效气藏,储层非均质性强,在开发中普遍见水。根据试采情况将气藏储层分为气层、水层、气水层三类,开展气、水层的测井识别研究。以区内26口井50个射孔层段各层的试油结果作为依据,选出了700个样本点作为判识建模的标本,以声波时差和中子孔隙度等8个原始测井值作为判识变量组合,采用模糊判别分析,建立了各层的判识模型。回判结果表明,模型对气层精度达到97％,水层为89％,气水层为94％。利用未参加建模的出水井射孔段对模型进行验证,判识结果基本符合实际情况,模型具有较高精度。     

焦作-郑州天然气输气管道工程地质灾害防治措施及防治建议

焦作-郑州天然气输气管道是较重要建设项目,输气管道起自焦作市博爱县磨头镇,南止郑州市惠济区古荥镇,该输气管道沿线地质环境条件复杂程度为简单-中等。地质灾害类型主要为崩塌、地裂缝、地面不均匀沉陷,黄土湿陷和沙土液化等地质灾害。工程建设有引发和加剧崩塌灾害的可能性,有遭受地质灾害的危险性。工程建设过程中应针对不同的灾害类型采取适当的预防或治理措施。     

高位瓦斯抽放钻孔在降低综采工作面瓦斯浓度中的应用

林南仓属于低瓦斯矿井,但存在高瓦斯区域。煤层和采空区是瓦斯的主要来源,尤以采空涌出量大,给煤矿生产和安全带来了极大隐患。通过在1129综采工作面风道施工高位瓦斯孔,把钻孔打到采空区一侧煤层顶板以上冒落裂隙带内,用钻孔进行瓦斯抽放,使采空内的瓦斯通过裂隙带沿钻孔抽出,有效降低综采工作面瓦斯浓度,保证综采工作面正常回采和安全生产。     

(ϖμ/ϖT) P of the lower mantle

We estimate (/T) _P of the lower mantle at seismic frequencies using two distinct approaches by combining ambient laboratory measurements on lower mantle minerals with seismic data. In the first approach, an upper bound is estimated for |(/T) _P | by comparing the shear modulus () profile of PREM with laboratory room-temperature data of extrapolated to high pressures. The second approach employs a seismic tomography constraint ( lnV _S / lnV _P ) _P =1.8–2, which directly relates (/T) _P with (K _S /T) _P . An average (K _S /T) _P can be obtained by comparing the well-established room-temperature compression data for lower mantle minerals with theK _S profile of PREM along several possible adiabats. Both (K _S /T) and (/T) depend on silicon content [or (Mg+Fe)/Sil of the model. For various compositions, the two approaches predict rather distinct (/T) _P vs. (K _S /T) _P curves, which intersect at a composition similar to pyrolite with (/T) _P =–0.02 to –0.035 and (K _S /T) _P =–0.015 to –0.020 GPa/K. The pure perovskite model, on the other hand, yields grossly inconsistent results using the two approaches. We conclude that both vertical and lateral variations in seismic velocities are consistent with variation due to pressure, temperature, and phase transformations of a uniform composition. Additional physical properties of a pyrolite lower mantle are further predicted. Lateral temperature variations are predicted to be about 100–250 K, and the ratio of ( lnp/ lnV _S ) _P around 0.13 and 0.26. All of these parameters increase slightly with depth if the ratio of ( lnV _S / lnV _P ) _P remains constant throughout the lower mantle. These predicted values are in excellent agreement with geodynamic analyses, in which the ratios ( ln / lnV _S ) _P and ( / lnV _S ) _P are free parameters arbitrarily adjusted to fit the tomography and geoid data.     

Evolution of cool skin and direct air-sea gas transfer coefficient during daytime

Absorption of solar radiation within the thermal molecular sublayer of the ocean can modify the temperature difference across the cool skin as well as the air-sea gas transfer. Our model of renewal type is based on the assumption that the thermal and diffusive molecular sublayers below the ocean surface undergo cyclic growth and destruction, the heat and gas transfer between the successive burst events are performed by molecular diffusion. The model has been upgraded to include heating due to solar radiation. The renewal time is parameterized as a function of the surface Richardson number and the Keulegan number. A Rayleigh number criterion characterizes the convective instability of the cool skin under solar heating. Under low wind speed conditions, the solar heating can damp the convective instability, strongly increasing the renewal time and correspondingly decreasing the interfacial gas exchange. In the ocean, an additional convective instability caused by salinity flux due to evaporation becomes of importance in such cases. The new parameterization is compared with the cool skin data obtained in the western equatorial Pacific during the Tropical Ocean Global Atmosphere Coupled Ocean Atmosphere Response Experiment in February 1993. In combination with a model of the diurnal thermocline it describes main features of the field data both in nighttime and daytime. Under low wind speed conditions (< 5 m s^-1) diurnal variations of the sea surface temperature due to the formation of a diurnal thermocline were substantially larger than those across the cool skin. Under wind speeds > 5 m s^-1, diurnal variations of the surface temperature due to the variations of the thermal molecular sublayer become more important.     

Oil and gas resource assessment: The linkage between geological analysis and discovery process model results

Two widely-used techniques to estimate the volume of remaining oil and gas resources are discovery process modeling and geologic assessment. Both were used in a recent national assessment of oil and gas resources of the United States. Parallel estimates were obtained for 27 provinces. Geological-based estimates can typically see into areas not available to discovery process models (that is areas with little or no exploration history) and thus, on average, yield higher estimates. However, a linear relation does exist between the mean estimates obtained from these two methods. In addition, other variables were found in a multiple regression model that explained much of the difference. Thus, it is possible to perform discovery process modeling and adjust the estimates to yield results that might be expected from geological-based assessments.