砂岩在西湖凹陷花港组交互式盖层中的作用初探

西湖凹陷花港组盖层具有砂泥岩交互式发育特征，而与泥岩伴生的砂岩类盖层的油气封闭作用研究较少。采用岩芯、镜下观察及压汞等分析方法，对砂泥岩交互式盖层中的砂岩特征进行了分析。结果表明，盖层中的砂岩可作为有效盖层，砂岩类盖层早期受沉积环境控制，含有较多的泥质而导致早期压实作用较强，加之晚期的黏土矿物转换及胶结物的发育，具备形成良好盖层的条件。在不同地区进行砂岩盖层有效性研究时应以沉积环境为基础，针对不同类型的砂岩进行成岩作用研究，并针对不同类型的砂岩样品进行实验分析以明确砂岩盖层的有效性。     

Antidepressant-like effect of triterpenoids extracts from Poria cocos on the CUMS rats by 16S rRNA gene sequencing and LC–MS metabolomics

Abstract

Poria cocos is an edible medicinal mushroom with the effects of inducing diuresis, excreting dampness, invigorating the spleen and tranquilizing the mind. The triterpenoids of Poria cocos as the main active ingredients have shown health benefits of the central nervous system of the human body. Accordingly, this study aimed at further understanding the antidepressant-like effect and a potential mechanism of the triterpenoids extracts from Poria cocos (TPC). As a result, the TPC significantly ameliorated depression-like behaviors on chronic unpredictable mild stress (CUMS) rats, and restored the levels of brain-derived neurotrophic factor and nerve growth factor in the hippocampus of rats. Gut microiota analysis revealed that TPC could increase the biodiversity and markedly regulate the relative abundance of [Prevotella], Allobaculum and Ochrobactrum of CUMS rats. The cecal contents metabolomics pointed to thirteen biomarkers associated with TPC antidepressant effect, which involved in primary bile acid biosynthesis, taurine and hypotaurine metabolism, arginine and proline metabolism. Correlation analysis further showed that there was a strong correlation relationship between the gut microbiota and the cecal contents metabolites, especially compounds involved in energy metabolism, inflammation and immunity. In conclusion, TPC showed a potential antidepressant effect, which was possibly mediated the gut microbiota and cecal contents metabolism.     

济阳坳陷车西洼陷漫湖洪水—风暴沉积与砂体横向预测

渐新统沙二段是济阳坳陷车西地区的主力油层,为了弄清其油气分布和控制因素,综合利用大量的地质和地球物理资料,首次在该段识别出漫湖洪水—风暴重力流沉积.该沉积具有明显的重力流沉积特征,砂岩的成分和结构成熟度均较低,粒度概率图多由悬浮次总体组成,沉积序列可用Bouma序列来描述.在明确了砂体的沉积层位和沉积边界的基础上,描述了漫湖洪水—风暴的沉积特征并建立了沉积模式,系统分析了这个环境下储集性砂体的分布及其油气聚集特征,为车西洼陷的进一步勘探指明了方向.     

阳信洼陷火山活动在层序发育晚期对可容空间的影响

以阳信洼陷下第三系火山层序为例,探讨了火山活动等事件对层序发育晚期可容空间产生与分布的影响.阳信洼陷下第三系火山层序发育的特征及湖退时期的可容空间变化特征表明,火山锥在断陷湖盆中的堆积使得盆地中可容空间演化特征与常见断陷湖盆不同,局部可容空间的分配出现非均一性特征.研究表明,火山锥体使湖退时期可容空间分布复杂化,是可容空间分布呈现非均一性的直接原因.进而丰富了层序地层学的内容.     

Magnetostratigraphic study of Cretaceous depositional succession in the northern Kuqa Depression, Northwest China

Stratigraphic studies on the depositional succession in the Kuqa Depression can be traced back to as early as 1950s[1], however, there are still some disputes about chronostratigraphic division of the Bashenjiqike For- mation because of the paucity of fos…     

溶解气回注提高致密油藏采收率效果及敏感性

针对致密油藏水平井产量递减快,衰竭开发采收率低等问题,提出了衰竭开发后期回注溶解气提高采收率的方法。基于新疆玛湖凹陷百口泉组地质油藏特征,建立了致密油藏多级压裂双水平井机理模型,系统研究了上述方法在致密油藏中的生产特征及敏感性。结果表明,溶解气回注可以有效提高致密油藏采收率,缓解水平井产量递减的速度。采出程度随注入量、注入速度及吞吐轮次逐渐增加;气体分子的扩散作用可增加基质的受效范围,扩大气体的作用半径;弱非均质性储层（变异系数0.2）采用溶解气吞吐提高采收率效果最佳。敏感性分析结果表明,吞吐轮次对注溶解气提高采收率的影响最大,其次是注入时间、注入速度、扩散系数、焖井时间。另外,建立的代理模型可准确预测和优化致密油藏注溶解气提高采收率效果。     

变质岩潜山地震资料处理方法探讨

辽河油田潜山经过四十余年的勘探,成果丰硕。在不断获得勘探重大发现,获取规模储量的同时,也不断完善了潜山勘探理论及相应的配套勘探技术系列。特别是近年来辽河油田提出了变质岩潜山内幕成藏勘探理论,并以此为指导在兴隆台~马圈子潜山带深层潜山和潜山深部获得重要突破,整体上报探明石油地质储量1.27亿吨,为辽河油田的增储稳产做出了巨大的贡献。辽河坳陷中央凸起潜山带具有较好的石油地质条件,钻探的赵古1已获重大发现,赵古2井显示存在变质岩内幕油藏,预示中央凸起变质岩潜山内幕油藏勘探拥有很好的前景。但变质岩潜山内幕结构、构造识别划分是关键点也是难点,加之中央凸起潜山内幕地震资料品质较差,这就需要在地震资料处理上有相应的配套处理方法,本文以中央潜山带为例针对变质岩潜山进行地震资料处理并且研究出配套处理方法。辽河坳陷中央潜山带地质情况复杂,潜山带及其两侧断裂附近横向速度突变,潜山内幕信噪比低,成像较差。为了理清断层位置,搞清潜山与东西两侧凹陷接触关系,提高潜山内幕成像质量,我们对辽河坳陷中央潜山带资料进行了叠前深度偏移处理和研究。本文首先介绍了克希霍夫叠前深度偏移的方法原理,进而阐述了建立精确速度-深度模型的思路和实施方法,并利用该速度模型进行了克希霍夫深度偏移和逆时偏移两种方法的成像运算,最后将叠前深度偏移成果与叠前时间偏移成果进行比较。深度偏移技术可以有效解决潜山及两侧断裂附近速度横向突变问题,使地下构造成像更加合理,同时信噪比和保真保幅方面也有所提高。     

海拉尔盆地贝尔凹陷埋藏沉降史

为揭示贝尔凹陷内的沉降抬升差异,利用该地区钻井取得的地层厚度、时代、岩性、密度、孔隙度等信息,根据其次级凹陷构造特征,选取五口代表井进行回剥分析,绘制贝尔凹陷砂岩孔隙度与深度关系曲线,并给出沉降史。结果表明：贝尔凹陷砂岩原始孔隙度为50%,压实系数为0.78 m-1。五口井共性显示,贝尔凹陷沉降史整体可分为n1、n2、d1和d2的快速沉降期（140.0～125.0 Ma）、尹敏组的持续沉降期（125.0～105.0 Ma）、抬升剥蚀期（105.0～99.0 Ma）和青元岗组的缓慢沉降期（70.6～65.0 Ma）四个阶段。贝尔凹陷整体底界的古构造演化具有相似性和继承性。五口井差异性显示,贝尔凹陷中西部含油气多、沉降速率大,其他地区含油气少、沉降速率小,沉降速率是影响油气富集的因素之一。该研究为进一步分析凹陷内油气富集条件提供了依据。     

Submicron carbon-based hybrid nano-pour-point depressant with outstanding pour point depressant and excellent viscosity depressant

We have investigated the effective utilization potential of carbon nanomaterials in the field of pour point depressants, and reported three kind of carbon-based hybrid nano-pour-point depressants with different dimensions. In this paper, poly-α-olefins-acrylate high-carbon ester pour point depressant (PAA-18) was prepared by esterification and polymerization as the basic pour point depressant. Then, the basic pour point depressant PAA18 was modified by solvothermal method with graphene oxide (GO), carbon nanospheres (Cna) and carbon nanotubes (OCNTs). The morphology and structure of the composites were analyzed by SEM, FTIR and XRD. The results showed that PAA18 was successfully in situ polymerized on GO, Cna and OCNTs. We took the simulated oil as the experimental object, and evaluated its pour point, rheological properties and wax crystal morphology, and achieved excellent results. In the three carbon-based hybrid nano-pour-point depressants with different carbon contents, the oxidation carbon nanotubes composite pour point depressant (PAA18-1 % OCNTs) with carbon content of 1 % had the best pour point and viscosity reduction effect when the dosage was 1250 ppm, which could make the pour point of the simulated oil containing wax decrease by 16 °C. PAA18-1 % OCNTs reduced the pour point by 5 °C more than PAA18. This paper provides reference for the application of carbon nanomaterials in the field of pour point depressant.     

Prediction of 5-hydroxytryptamine transporter inhibitors based on machine learning

In patients with depression, the use of 5-HT reuptake inhibitors can improve the condition. Machine learning methods can be used in ligand-based activity prediction processes. In order to predict SERT inhibitors, the SERT inhibitor data from the ChEMBL database was screened and pre-processed. Then 4 machine learning methods (LR, SVM, RF, and KNN) and 4 molecular fingerprints (CDK, Graph, MACCS, and PubChem) were used to build 16 prediction models. The top 5 models of accuracy (Q) in the cross-validation of training set were used to build three different ensemble learning models. In the test1 set, the VOT_CLF3 model had the largest SP (0.871), Q (0.869), AUC (0.919), and MCC (0.728). In the unbalanced test2 set, VOT_CLF3 had the largest SE (0.857), SP (0.867), Q (0.865) and MCC (0.639). VOT_CLF3 was recommended for the virtual screening process of SERT inhibitors. In addition, 12 molecular structural alerts that frequently appear in SERT inhibitors were found (P < 0.05), which provided important reference value for the design work of SERT inhibitors.