碳酸盐岩储层UniQ三维地震勘探技术应用及效果

塔里木盆地哈拉哈塘油田碳酸盐岩有效裂缝及大型缝洞体储层空间连通性预测难度大,现有地震资料不能完全满足缝洞体系精细刻画、油气富集区域准确预测的需求,制约了碳酸盐岩油藏的勘探开发。针对此难题,以哈13—热普6井区为试验区,开展了UniQ单点高密度三维地震采集技术研究。小面元、高覆盖、宽方位、高炮道密度的UniQ采集方案保证了空间采集密度,采样分布均匀,基本没有假频信息,有利于信噪分离和保幅去噪处理。根据数据特点,采用了有针对性的高密度地震数据去噪技术、稳健地表一致性反褶积技术、OVT域数据规则化技术、深度域精细速度建模技术等数据处理技术,提高成果质量。结果显示,UniQ三维地震数据的地层成像产状合理,碳酸盐岩缝洞串珠更加聚焦,成像更加清晰,裂缝刻画更加细致,为缝洞型碳酸盐岩储层的勘探开发奠定了良好的数据基础。     

塔里木油田实用产量预测方法

乌什凹陷神木地区位于天山造山带南部山前复杂地区, 地震资料品质较差, 储层埋藏深, 岩相横向变化快, 叠后反演、地震属性等常规储层预测方法由于具有局限性和多解性而不适用。为准确地预测神木地区储层分布规律, 引进了叠前同时反演技术, 从叠前道集资料品质评价及优化处理、岩石物理建模、地震子波提取、低频模型建立及岩相解释分析等技术环节层层质控, 对研究区储层进行精细研究, 刻画出研究区储层展布规律, 取得了较好的应用效果。研究发现, 神木地区白垩系舒善河组油气藏为一受构造控制的岩性油气藏, 储层为呈北东向展布的河道砂体, 砂岩物性较好。SM2井位于河道砂体构造高部位, 砂体较连续; SM201井构造位置相对较低, 砂体位于油水界面之下；WC1井钻遇河道边缘的一透镜状砂体, 规模较小。SM2井区岩性油气藏河道砂体的构造高部位可作为下一步勘探的有利目标。     

利用多方法综合预测碳酸盐岩储层小断裂及微裂缝

由于塔里木盆地碳酸盐岩储层段地震资料低信噪比、低分辨率等因素的影响,造成了裂缝预测难度大,如何提高预测精度是长期以来的技术难题之一。以解释性处理的三维地震数据为平台,通过大量预测参数实验并结合钻井资料、构造地质分析,形成了提高碳酸盐岩储层小断裂及微裂缝预测精度的一套系统、有效的技术系列,根据勘探开发精度要求的不同,采用相干、曲率、蚂蚁体等属性进行单一的或综合的叠后裂缝预测。此技术系列在塔中碳酸盐岩储层勘探开发中有效地指导了高产井点的优选,尤其是水平井的钻探,应用效果明显。     

F—X域复数经验模态分解去噪方法

常规的随机噪声衰减方法不能很好地适应非平稳地震信号的 处理,为此提出了一种基于F—X域投影法复数经验模态分解的预测滤波方法。采用基于空间投影的复数经验模态分解方法将F—X域地震数据分解为不同的模态分量;对不同模态的地震数据分别进行F—X域预测滤波。该方法克服了常规经验模态分解方法需要对复数信号的实部和虚部分别进行处理的缺陷,也避免了因剔除第一模态分量对有效信号的损失,具有较高的去噪能力和保幅性能。     

逆时偏移在碳酸盐岩缝洞型储层地震成像中的应用

逆时偏移采用双程波动方程，对地层倾角没有限制，适应纵横向速度变化，具有提高碳酸盐岩缝洞型储层成像精度的技术潜力。依托哈拉哈塘地区的实际地震数据，就逆时偏移的成像效果进行了实验分析，实验结果表明，逆时偏移较单程波偏移能够更好地聚焦缝洞体的散射能量，提高成像精度，但其对速度场精度的依赖性更高，当使用地震属性对裂缝系统进行刻画时，由于逆时偏移地震数据的频带较窄，降低了裂缝系统的刻画精度。     

塔中西部碳酸盐岩高效井串珠状地震相储层要素探讨

为了提高碳酸盐岩高效井的预测成功率,综合利用常规地震剖面、钻井、录井资料及开发动态资料等,研究塔里木盆地塔中西部地区高效井与储层发育指数、串珠有无断裂伴生、串珠位于缝洞系统的空间位置及串珠状地震相储层顶部标定的关系。研究认为,储层发育指数越大,强串珠发育伴生断裂,储层易发育且规模往往较大。但钻遇强串珠的油井高效与否还与其在缝洞系统中的空间匹配关系和井轨迹是否位于储层顶面密切相关。油井钻遇的串珠位于缝洞系统的相对高部位,油柱高度大,产水的可能较小,易高产稳产。对于强串珠状,水平井轨迹设计在最大波谷处较为合适;对于弱串珠状,水平井轨迹设计在最大波谷之下的零相位之上较为合适。在塔中西部中古15井区实际应用结果表明,钻遇具备此四大要素的强串珠状地震相储层的油井易高产稳产,提高了碳酸盐岩高效井的预测成功率。     

井控Q补偿在哈拉哈塘三维地震拓频处理中的应用

哈拉哈塘地区奥陶系油气勘探目的层埋深大,构造复杂,储层非均质性差。针对这一问题,利用能同时进行振幅校正和相位畸变校正的井控Q补偿方法对该区的三维地震资料进行了提高分辨率处理。该方法是地震资料处理中提高分辨率的重要方法,利用哈拉哈塘地区的VSP资料提取地层的Q值,在获得的Q值附近进行扫描,将不同Q补偿的偏移剖面与井资料进行匹配分析,最终获得精确的Q值。该方法有效地补偿了地震资料的中频、高频成分,拓宽了地震信号的频带宽度,提高了地震剖面的分辨率,改善了地震资料的质量。     

地震储层厚度预测精度分析及在塔里木地区的应用

为了分析储层厚度预测精度,首先根据实际数据建立正演模型并进行正演模拟,然后采用地震剖面识别、宽带约束反演、矢量属性3种方法对正演模拟数据进行分析,最终得到3种方法下的储层厚度预测精度。同时,在理论正演数据中加入噪声,研究这3种方法对噪声的敏感度,研究结果表明地震剖面识别和宽带约束反演方法在预测储层厚度时抗噪性较好,而矢量属性方法则对地震资料的信噪比要求较高。将理论研究成果应用于塔里木哈得地区,得到基于宽带约束反演和矢量属性方法预测的研究区内储层厚度平面图,并给出了储层段尖灭线,效果明显。     

塔里木盆地哈拉哈塘地区碳酸盐岩裂缝预测

采用基于成因分析的间接方法或地震资料直接预测方法预测不同尺度的裂缝,裂缝预测缺乏层次性及针对性,预测结果多解性强、精度低。针对不同尺度裂缝,以裂缝地质成因为指导,有针对性选取不同地质、地震信息逐级识别大(千米级别)、中(百米级别)、小(十米级别)不同尺度裂缝。大尺度裂缝派生中尺度裂缝,中尺度裂缝控制小尺度裂缝发育,逐级约束、分层次预测裂缝能够提高裂缝预测精度。大尺度裂缝受主断裂控制,通过构造地质建模刻画断裂期次、级别及类型,结合断控裂缝的地质成因规律定性地预测大尺度裂缝分布范围。大尺度裂缝派生中尺度发育,在大尺度裂缝预测成果约束下,选取叠后地震资料及精细相干技术识别中尺度裂缝。小尺度裂缝受大尺度、中尺度裂缝控制。以大尺度、中尺度裂缝为约束条件,利用叠前方位各向异性反演技术识别小尺度的裂缝密度及方位,最终实现缝洞单元划分。通过对裂缝进行逐级识别,提高裂缝识别精度,有利于缝洞连通性分析和缝洞单元的合理划分。     

塔北西部大深度薄储层碎屑岩地震采集技术

针对地震资料分辨率低、薄砂体无法有效分辨的难题, 通过理论分析和模型正演认为识别薄储层面元的属性比面元的大小更为重要,在勘探成本有限的前提下,宁可面元和线距大一点,也应该选择覆盖次数较高、方位较宽的观测系统,提高地质目标成像质量和精度,依靠数据体的属性去识别和检测薄储层。该技术在塔里木盆地玉东地区应用后,地震资料品质改善明显。     

《护理与康复》杂志社网站开通公告

<正>为使《护理与康复》杂志跟上网络化步伐,缩短读者、作者、编者的距离,为国内外临床护士、研究人员以及医学院校从事护理教育工作者提供快捷的学术交流平台,《护理与康复》网站于2014年10月1日正式开通,网址:www.zjhlykf.com。网站依托杂志本身,体现"传递护理学术信息,提高理论与技术水平,促进护理学科发展"的办刊宗旨,通过期刊简介、期刊荣誉、投稿指南、审稿指南、期刊订阅、在线留言、期     

Modelling and correcting azimuthal anisotropy in Sentinel-1 backscatter data

Sentinel-1 backscatter data – acquired in dual-polarized (VV/VH) Interferometric Wide (IW) swath mode – show an azimuth angle dependency. The orientation of the tangential plane of the surface observed can alter the backscatter differently depending on the azimuth angle of the viewing geometry. In this study, two years of Sentinel-1 backscatter data over Western Europe have been investigated to show that the azimuthal anisotropy of backscatter is mainly caused by the orientation of the topography’s slope. The SRTM-3 digital elevation model (DEM) is used to describe the azimuthal anisotropy in the Tyrolean Alps and in addition, an algorithm is presented to take into account the azimuthal dependency by calculating correction parameters for each relative orbit of Sentinel-1.     

Mitigation of ionospheric phase delay error for SAR interferometry: an application of FR-based and azimuth offset methods

Synthetic aperture radar (SAR) signals interact with the ionosphere layer when they propagate through the atmosphere, leading to the phase delay error for SAR interferometry (InSAR). Although several methods have been proposed to mitigate the ionospheric phase delay, the application of these methods has not been fully investigated. In this study, an application of two ionospheric mitigating methods, azimuth offset and Faraday Rotation (FR), is investigated and analysed through processing two Phased Array type L-band Synthetic Aperture Radar onboard the Advanced Land Observing Satellite-1 full-polarimetric SAR images over Alaska. The result indicates that both methods can effectively mitigate the ionospheric phase delay error for SAR interferometry, where the standard deviations of the ionosphere-corrected result have decreased to 3.2 cm for FR-based method and 2.0 cm for azimuth offset method compared to those before correction (7.3 cm). However, it is also found that the FR-based method is easily contaminated by the noise, which may be a limitation in practical applications. Moreover, analysis of the residual ionospheric phase from azimuth offset method suggests that the residual phase contribution in the azimuth offset and interferometric phase images is a limitation when using the azimuth offset to mitigate the ionospheric effect on SAR interferometry. This study may provide useful information when using these two methods to mitigate the ionospheric phase delay error on InSAR.     

Ovarian vein thrombosis: a rare cause of abdominal pain outside the peripartum period

Ovarian vein thrombosis (OVT) is a rare cause of abdominal pain that may mimic a surgical abdomen. The differential diagnosis of OVT includes acute appendicitis, endometritis, pelvic inflammatory disease, pyelonephritis, nephrolithiasis, tubo-ovarian abscess, and ovarian torsion. The complications of OVT, including sepsis and pulmonary embolism, are significant. Diagnosis relies on a careful examination of the radiographic findings. This diagnosis should be considered not only in postpartum patients but also in women with pelvic inflammatory disease, recent abdominal surgery, malignancy, or known hypercoagulable state. In this report we present a case of OVT in a 29-year-old woman presenting with 3 days of sharp left-sided abdominal pain, nausea, and vomiting after bilateral salpingectomy. We then discuss the epidemiology, pathophysiology, and clinical management of OVT.     

The reliability of a deep learning model in clinical out-of-distribution MRI data: A multicohort study

Deep learning (DL) methods have in recent years yielded impressive results in medical imaging, with the potential to function as clinical aid to radiologists. However, DL models in medical imaging are often trained on public research cohorts with images acquired with a single scanner or with strict protocol harmonization, which is not representative of a clinical setting. The aim of this study was to investigate how well a DL model performs in unseen clinical datasets–collected with different scanners, protocols and disease populations–and whether more heterogeneous training data improves generalization. In total, 3117 MRI scans of brains from multiple dementia research cohorts and memory clinics, that had been visually rated by a neuroradiologist according to Scheltens’ scale of medial temporal atrophy (MTA), were included in this study. By training multiple versions of a convolutional neural network on different subsets of this data to predict MTA ratings, we assessed the impact of including images from a wider distribution during training had on performance in external memory clinic data. Our results showed that our model generalized well to datasets acquired with similar protocols as the training data, but substantially worse in clinical cohorts with visibly different tissue contrasts in the images. This implies that future DL studies investigating performance in out-of-distribution (OOD) MRI data need to assess multiple external cohorts for reliable results. Further, by including data from a wider range of scanners and protocols the performance improved in OOD data, which suggests that more heterogeneous training data makes the model generalize better. To conclude, this is the most comprehensive study to date investigating the domain shift in deep learning on MRI data, and we advocate rigorous evaluation of DL models on clinical data prior to being certified for deployment.