The Permian Shihezi Formation is located at the LX block at the eastern margin of the Ordos Basin, and it develops tight sandstone reservoirs with fluvial facies. Reservoirs with high gas production feature a porosity of larger than 12%, a permeability of higher than 1 mD, and a gas saturation of more than 50%, and the quantitative evaluation of reservoir parameters shall be urgently carried out to find sweet spots with high production. However, the accuracy of indirectly predicting porosity and other parameters by traditional seismic inversion is low. In addition, the seismic data and well-logging curves of the LX block have inconsistent corresponding relations, and a lot of conflict samples exist, which makes conventional convolutional neural networks difficult to be applied. Therefore, a fully connected network architecture is added to the conventional convolutional neural network, and the seismic data and well-logging data are connected through local Toeplitz network architecture, so as to deal with the indirect correlation between reservoir parameters and seismic data. The fully connected network architecture can address the conflict samples by introducing prior information including the line/channel number, horizon, and seismic facies. Furthermore, a deep learning network model suitable for tight reservoirs is established by introducing prior constraint information such as stratigraphic framework and seismic facies, and a geo-oriented method for selecting the best sample well is developed, so as to quantitatively predict reservoir parameters and describe the plane distribution of the sweet spots in reservoirs with high gas production. The actual application results show that the predicted results of porosity, permeability, and gas saturation are in good agreement with the well-logging data, and the newly deployed five wells are tested and achieve an open-flow capacity of more than 10,000 m~3/d during drilling, which effectively promotes the efficient development of tight gas.

鄂尔多斯盆地A区块的地质、地震特征表现为:地表条件复杂,致密气储层非均质强、厚度小,不同井的气层测试产量差异很大,断层、裂缝影响油气富集并具有后期调整、再分配气层分布的作用;地震资料分辨率低,常规叠后数据难以满足薄储层中断层、裂缝体系识别的需求.为此,首先利用基于L0范数稀疏反演的压缩感知地震资料处理方法提高数据分辨率,为后续薄储层中断裂体系识别创造良好的资料条件.在此基础上探索基于中值滤波技术的绕射信息提取方法,在保幅偏移地震数据体上提取绕射信息,得到清晰的断点和断层信息;根据绕射波地震数据振幅及相位在空间上的不连续性,通过蚂蚁体追踪刻画断层和裂缝分布.结果表明:处理后的数据分辨率得到有效提高,并且断点清晰,更易识别断层;与常规叠后属性相比,基于绕射信息提取技术的断裂识别方法能够识别更小尺度的断裂;对比钻井气层解释结果、产能资料与绕射断裂识别结果发现,断裂和气层同时存在时产能较高,该认识可为井位部署提供重要依据.

针对致密砂岩气储层复杂的微观孔隙结构进行岩石物理建模,在模型中比较了单一孔隙纵横比、双孔隙模型两种表征孔隙结构的表征方式.岩石物理正演分析表明,两种孔隙结构模型均可解释致密砂岩复杂的速度-孔隙度关系.岩石物理反演结果表明,双孔隙模型对测井横波速度的预测精度更高,说明该模型更适用于表征研究区致密砂岩的孔隙结构,反演的软孔比例参数能够反映地层中孔隙结构的非均匀分布.应用双孔隙模型计算致密砂岩地层岩石骨架的弹性模量,与Krief及Pride等传统经验公式相比,该方法考虑了岩石骨架模量与矿物基质、孔隙度和孔隙结构等微观物性因素的关系,理论上更具有严谨性.对致密砂岩骨架模量计算结果的分析表明,少量微裂隙的存在即能够显著影响致密砂岩骨架的弹性性质,同时孔隙空间中的球形孔隙是致密气的主要赋存空间.并且,通过致密砂岩骨架弹性模量,进一步计算了可用于地层评价的Biot系数等岩石物理参数.致密砂岩骨架模量的预测结果可为Gassmann流体替换理论、BISQ孔隙弹性介质理论等岩石物理方法提供关键参数.

西非尼日尔三角洲盆地发育深水扇沉积体系,钻井资料揭示,油层和水层均表现为"亮点"和远道增强的Ⅱ、Ⅲ类AVO异常,利用常规振幅和AVO方法检测烃类精度低.为此,首先分析了地震响应特征及其影响因素,通过正演模拟明确了孔隙度是引起振幅及AVO多解性的主要原因;然后,通过交会分析认识到,截距—梯度属性随孔隙度的变化规律与随含水饱和度的变化规律存在差异,进而提出了基于坐标旋转的扩展AVO属性,以降低常规AVO属性的多解性,提高油气预测精度;最后,开展面向实际储层的敏感流体因子优选,提出一种新的流体因子敏感性定量分析方法,能够优选出对流体敏感、对孔隙不敏感的流体因子,预测结果能够压制孔隙度造成的流体识别假象.分析结果表明,λ/(λ、μ为拉梅系数)具有对流体性质敏感性高、对孔隙度敏感性低的特征,是研究区开展烃类检测的最佳敏感流体因子.实际应用结果表明,利用扩展AVO属性和λ/μ流体因子能够有效区分真"亮点"油层和假"亮点"水层,预测结果与钻井数据更吻合,有效提升了烃类检测成功率.

致密储层具有地层薄、孔隙度低、横向非均质性强等特点.现有储层预测技术在解决此类问题时,主要依靠人工从反演属性体中寻找可能的甜点区域.由于地层的砂岩含量、孔隙度值与地震反射特征并无直接关系,导致甜点识别准确率低.为此,根据测井数据和地震数据的空间分布特征和数据分布特征,将全局和局部连接网络相结合,有针对性地创建了适用于致密储层甜点预测的混合深度学习网络结构,其中局部连接网络负责学习数据分布特征,全局连接网络负责学习空间分布特征.在甜点预测时,先预测砂岩储层,在此基础上预测孔隙度值.为解决孔隙度数据分布不均匀、有效值与背景值比例不均衡的问题,以砂岩含量曲线为约束条件设置阈值,筛选高于阈值的对应层段的孔隙度值,建立了砂岩含量遮挡的孔隙度训练样本集构建方法.鄂尔多斯盆地东北部的致密砂岩甜点识别结果表明,孔隙度预测结果准确度高,能有效识别本区的致密储层甜点发育区.

在过去十几年时间里,海洋宽频地震采集和配套处理技术取得了长足进展,在我国南海深水区已采集地震资料的频带宽度已达5个倍频程,低频信息可拓展至3 Hz,高频信息可达120 Hz.与常规地震数据相比,宽频地震数据中子波的波形特征有了明显变化,除了可以大幅提升地震资料的信噪比和改善中深层地震成像精度外,在地震属性计算过程中还有很多明显的优势.本文从瞬时振幅属性计算原理出发,通过模型对比分析了常规子波与宽频子波在瞬时振幅属性计算方面的差异及产生原因.分析表明,宽频地震数据可以有效降低瞬时振幅属性计算结果的误差,有效提高基于振幅属性进行储层刻画和储层物性参数预测的精度.最后,将该方法应用于南海深水区实际数据的对比中,进一步阐明了宽频地震数据在瞬时振幅属性计算方面的优势,有效支持了深水区勘探进程,取得了良好的应用效果.

鄂尔多斯盆地东缘LX地区二叠系石盒子组发育河流相致密砂岩储层, 具有低孔、低渗且非均质性强的特点, 优选“甜点”是取得产能突破的关键。勘探结果表明, 基于叠后振幅属性预测气层存在多解性。对比分析井旁地震道集发现, 气层和干层的叠前AVO响应特征存在差异, 且与储层参数具有一定相关性。为此, 从研究区沉积模式入手, 建立泥-砂-泥3层介质模型, 通过开展变参数AVO正演模拟, 分析孔隙度、厚度以及含气饱和度变化对截距属性(P)和梯度属性(G)的影响。模拟结果表明, 孔隙度和厚度是决定AVO响应特征的主控因素。通过构建AVO定量解释模版, 并拟合截距和梯度属性与孔隙度和厚度之间的数学函数关系, 实现孔隙度和厚度的半定量-定量预测。应用结果表明, “甜点”预测结果与已钻井吻合率较高, 孔隙度和厚度定量预测误差控制在20%以内, 依据预测结果部署的新钻井L6井获得了高产天然气, 取得了良好的应用效果。

目前基于字典学习的三维地震数据重建方法通常采取二维逐切片重建的策略,这种重建方式忽略了切片间的相互联系,未能充分运用地震数据各个方向上的连续性约束.为此,提出了一种三维联合重建方法——快速结构字典学习三维数据重建方法.该方法在压缩感知理论框架下,利用快速结构字典学习算法训练训练集,产生三维自适应字典;然后利用三维自适应字典、观测矩阵以及正则化正交匹配追踪算法对数据进行高精度重建.模型数据和实际数据的重建结果表明,该方法能够恢复地震数据的细节特征,具有重建精度高、保幅性良好的优点.

西湖凹陷深层致密砂岩储层具有良好的勘探开发前景,受埋深影响,目的层地震资料品质较差.A构造通过斜缆宽频采集和处理获取宽频地震数据,提升了资料品质,然而应用常规子波提取方法对宽频数据进行子波提取并反演计算纵波阻抗,结果与井上实测数值差异较大,影响储层的定量解释.针对这一问题,提出统计性子波和确定性子波相结合的长短子波合并宽频子波提取方法,提取的宽频子波比常规子波低频丰富、旁瓣小,能更真实地反映地震信息,约束稀疏脉冲反演的纵波阻抗结果与测井曲线吻合度更高.基于宽频数据和常规数据分别进行约束稀疏脉冲弹性波阻抗反演,预测A构造优质储层分布,经已钻井证实,宽频数据比常规数据储层预测精度高,预测的储层展布特征与研究区地质沉积认识一致.结果表明:这种基于宽频子波提取的宽频资料应用方法有效降低了致密砂岩储层预测的多解性.

南海白云凹陷区域位于南海北部珠江组盆地,其沉积环境属于砂岩岩性的复合圈闭构造,具有振幅刻画弱,非均质性强,构造复杂的特殊之处,是制约南海白云凹陷区域勘探进展的主要原因之一.为此,依据物性参数(孔隙度、渗透率)对于在白云凹陷区域高孔高渗的油气藏认识,对物性参数敏感性孔隙度及渗透率作了定量分析,即含气层孔隙度大于15％,渗透率大于40×10-3 μm2.将AVF作为独立信息,结合广义S变换以及支持分频数据体非线性算法PSO-SA训练在井约束下对孔隙度和渗透率进行了分频反演,反演结果与实际测井资料吻合程度较好(反演物性参数误差在18％以内),获得了孔隙度和渗透率反演剖面,准确有效地刻画了含气储层,提高了深水区油气勘探的精度.

通过建立等效加权模型并进行单步向上延拓反Q滤波,减弱目前常规Q值模型对反Q滤波效果的限制,达到进一步提高地震记录分辨率的目的.目前,常规Q值模型包括常Q模型和层状模型,常Q模型与复杂的地下情况相差较大,导致地震波能量衰减补偿效果较差;而基于层状模型进行反Q滤波则会导致噪声逐层放大,降低地震记录信噪比,基于等效加权模型进行单步向上延拓反Q滤波,可以有效补偿地震波振幅衰减并校正相位畸变,在保持信噪比的同时提高地震记录分辨率.通过模型测试,基于等效加权模型进行单步向上延拓反Q滤波,可以在有效压制噪声的同时增强能量衰减补偿效果;实际资料应用的结果表明:该方法不仅能有效补偿深层能量衰减,并且能较好的压制环境噪声,在提高地震资料分辨率的同时保持了信噪比.

常规波阻抗反演一般使用全频带叠后数据进行反演,大多数反演结果受地震波主频控制,数据中有效频带的高频与低频部分的潜力没有得到充分利用,很难达到理想效果.本文提出了一种基于稀疏窗S变换的分频-重构波阻抗反演方法,首先对窗参数进行基于振幅谱的稀疏优化,根据不同信号的振幅谱作不同适应性窗参数优化,然后利用稀疏窗S变换对井数据进行处理,最后将分频井曲线反演与分频地震反演方法相结合,建立新的分频重构波阻抗反演流程.模型试算、方法验证及实例应用表明,基于稀疏窗S变换的时频分析方法具有高分辨率和能量聚集的特性,分频-重构波阻抗反演结果与井吻合度更高,能够有效提高对断层及薄层的识别精度,具有良好的应用潜力.

Zero offset data has many advantages and applications in marine seismic data processing. However, limited by the marine streamer seismic acquisition method, the minimum offset of received data is generally about 200 meters, and the data with offset smaller than it cannot be obtained directly. The usual approach to get the zero offset data (including small offset data) is by extrapolating larger offset data after NMO correction. Unfortunately, the accuracy of this extrapolation is affected by the signal-to-noise ratio of seismic data and the accuracy of velocity for NMO correction, and this method is difficult to preserve the true amplitude. In this paper, based on the Kirchhoff true-amplitude migration and demigration series technique, in which the observation system is changed during the process of demigration, the zero-offset (including small-offset) data are converted from the large offset seismic data effectively while the amplitude characteristics data are preserved. In addition, after the series processing of migration and demigration, the random noise in seismic data is effectively suppressed and the signal-to-noise ratio and the imaging precision of seismic data are significantly improved.

建立准确的低频模型是波阻抗反演中的重要环节,它直接影响着波阻抗反演结果的准确性.但是,常规模型建立方法的准确性受钻井数量影响明显,钻井数量越多,模型的准确性越高.在海洋深水油气勘探过程中,由于勘探费用昂贵,钻井数量非常少,很难通过常规方法建立准确的低频模型.特别是在沉积体横向特征变化较大时,地震反演的可靠性受到巨大影响.本文首先介绍了立体层析速度反演的基本理论和数据域、成像域立体层析速度反演的计算过程,综合数据域立体层析与成像域立体层析的优势获得了高精度速度模型;然后结合有限的钻井进行标定,构建出地震反演所需的低频模型,有效提升了低频模型的精度,使其达到可真实反映较大规模地质异常体的尺度;最后,将其应用于南海深水区W构造的勘探实践,提升了反演结果横向预测的准确性.

A novel approach to construct a amperometric biosensor for determination of H 2 O 2 is described. Horseradish peroxidase (HRP) as a base enzyme was immobilized into the mixture of multiwalled carbon nanotubes (MWNTs) and polyvinyl butyral (PVB). Taking the classical hydroquinone as mediator, cyclic voltammetry and amperometric measurements were used to study and optimize the performance of the resulting H 2 O 2 biosensor. The effect of the concentration of MWNTs, HRP, hydroquinone, solution pH, and the working potential of amperometry on the electrochemical biosensor was systematically studied. The results showed that the fabricated biosensor demonstrated significant electrocatalytic activity for the reduction of hydrogen peroxide with wide linear range from 0.000832 to 0.6 mM, and low detection limit 0.000167 mM (S/N = 3) with fast response time less than 8 s. The apparent Michaelis–Menten constant was determined to be 0.049 mM. Additionally, the biosensor exhibited high sensitivity, rapid response and good long-term stability.

Spectral decomposition techniques have been widely used in seismic interpretation. However, the traditional methods have two limitations, low time resolution and uncertainty in predicting fluid types. In this paper, a seismic complex spectral decomposition technique is developed to achieve a much higher resolution in time-frequency distributions via inversion strategy and to reduce the uncertainty in hydrocarbon detection with the extracted wavelet phase information. The time-varying wavelet frequency and phase information are obtained by decomposing the seismic trace using a wavelet library composed of a number of complex Ricker wavelets with different dominant frequencies and zero phase. This process is referred to as the seismic complex decomposition. The Synthetic examples show the ability of the proposed method to distinguish closely positioned wavelets and the accuracy to extract the wavelet frequency and phase information from the seismic data.The causes of wavelet phase change due to attenuation are studied by using the viscoelastic wave equation modelling which is based on the Kelvin-Voigt model. The result demonstrates that the phase change not only occurs in the process of wave propagating, but also occurs at the interface where existing impedance or attenuation contrast. The impedance contrast only changes the polarity of the wave, while the attenuation contrast is the key reason that rotates the wavelet phase. The gas reservoir has high attenuation property, so that it could be predicted via the wavelet phase change information.The real data example demonstrates the high time resolution of the proposed method in reservoir prediction and the successful application of the phase information in distinguishing gas saturated layers and water saturated layers.

深水区受钻井资料少、中深层地震资料品质差和速度场求取困难等客观因素影响,常规的烃源岩预测方法受到限制.文章研究了白云深水区利用纵波阻抗与烃源岩TOC关系进行海相烃源岩预测的方法,提出了一种少井条件下井震联合预测烃源岩TOC的技术流程.首先,通过井曲线多属性融合技术预测连续的TOC曲线(TOC测井预测);其次,将预测的TOC曲线与弹性参数交会分析,得到烃源岩TOC与纵波阻抗的拟合关系(烃源岩岩石物理分析);然后引入无需井约束的高精度网格层析速度反演求取速度谱,用于波阻抗反演(地震反演);最后基于拟合关系和波阻抗反演结果预测烃源岩品质.实际数据无井约束波阻抗反演结果与井上TOC曲线关系匹配较好,验证了井震联合预测方法的有效性.

深水区勘探钻井稀少,由于缺乏测井约束,地震反演等常规技术油气检测的多解性很强.基于地震波衰减的频散现象,在地震谱分解基础上,提出应用频率依赖的AVO方程计算地震波的频散属性的方法,并设计理论模型验证了这种频散属性对含气性识别的高敏感度,进而将AVO属性与频散属性相结合构建了一种烃类指示因子(DvP×DG).应用频散AVO分析技术对南海深水区Y21-1构造区目的层系含气性进行了钻前预测,结果与实钻结果吻合较好,证实了频散AVO分析技术可以增强气层识别的敏感度,降低水层等背景带来的影响,可以作为烃类检测指标进行含气性识别.

This paper is aiming to solve the problems of weak seismic energy and poor imaging quality in the deep water ar-ea with big steep slope of Qiong Dong Nan basin.Firstly,the typical velocity model of BaoDao sag is established with the mix-ing grid modeling technology.Then,the wavefield propagation directions is simulated with ray tracing in different source posi-tions.The research shows that,in complex terrain of steep slope area,the relative position relation of source and hydrophone is one of the important factors,which has the influence on the energy propagating and signal receiving.This research will play important guiding significance for future seismic acquisition scheme design in big steep or rugged sea areas,and also for impro-ving the energy of deep seismic data and imaging quality.

随着石油勘探开发的深入,地震资料的应用越来越重要,对于地震资料的品质要求越来越高.实际应用中,地震道集往往存在动校不平,有剩余时差,对叠加成像、构造解释、属性分析等存在影响.为解决地震道集不平,提出了一种与剩余速度无关的动态道集拉平方法,该方法具有很好的适应性,通过互相关方法的选取和参数的调整,能适应各个地区资料地震道集的剩余时差情况.应用实例表明,该方法能较好地对叠前地震道集进行剩余时差校正,改善道集的品质和成像质量,提高断层成像.