基于抗大倾角干扰的第三代相干体分析技术及应用

地震相干体技术已广泛应用于构造和沉积研究,能够对断层、裂缝以及地质体边缘等不连续性结构进行有效检测.基于乘幂法的第三代相干体分析技术没有考虑到地层倾角对相干计算的影响,尽管效率很高,但是在大倾角区域会受影响,不利于高分辨地识别小断层等.为此,本文提出一种抗大倾角干扰的相干体分析技术的高效实现方法.该方法利用平滑的层位信息对数据体进行拉平,并利用基于乘幂法的第三代相干体快速实现方法来快速计算相干值,然后对计算得到的相干值进行反拉平以减少大倾角对计算相干值的影响.最后将本文提出方法应用于渤海浅层以及南海深水的地震资料处理以说明本文方法的有效性,实际应用效果最好.因此,本文方法以抗噪性能强、分辨率高的第三代相干体算法为基础,结合了三代相干体算法的快速实现方法和体拉平技术,计算效率高,能够有效减少大倾角对计算相干体产生的干扰,具有很高的空间分辨率.     

基于振幅加权的拖缆与海底地震资料联合波形反演方法

南黄海新近系沉积层与其下方中-古生代海相地层之间速度差异大,存在强波阻抗界面,导致接收的浅部和中深部地层的反射波振幅差异极大.一般全波形反演方法未考虑不同界面反射波振幅的差异,对南黄海浅部沉积层速度的反演效果好,而对中深部地层速度的反演效果差.为此,本文提出一种基于振幅加权的拖缆与海底地震资料联合波形反演方法.该方法对中深部弱反射波数据施加较大的权重,对浅部强反射波数据施加较小的权重,从而均衡浅、中和深部地层反射波的振幅,使得自浅至深所有地层速度均能得到较好反演效果.同时,该方法利用海底地震资料和拖缆地震记录积分的低频成分反演低波数背景速度场,利用拖缆资料的高频成分提高反演模型的分辨率,充分发挥了拖缆和海底地震资料的优势互补作用,获得了比仅使用单一资料更好的反演结果.根据地震剖面和钻井资料,建立了南黄海中部隆起的速度模型.利用该理论模型的合成地震记录对本文方法进行了测试和分析,验证了本文方法的正确性和有效性.     

低勘探程度地区地层速度提取方法与应用——以琼东南盆地深水区为例

如何在低勘探程度地区提取地层速度一直是地球物理勘探面临的难题,由于低勘探程度地区钻井资料十分稀少,需要借助地震资料才能够实现全区地层速度提取,而地震资料具有一定的频带,直接通过地震资料反演获取的地层速度缺失低频分量,必须进行低频速度补偿.本文提出一种以地震资料为主、测井资料为辅的地层速度提取方法.首先,通过有色反演方法提取地震资料的相对速度分量;然后,利用地震速度谱资料构建低频速度模型,低频速度模型构建受层序地层格架约束;最后,将低频和相对速度分量合成为宽频带的地层速度.应用该方法提取琼东南盆地深水区地层速度,结果表明提取的地震绝对速度精度较高,可作为该地区地层速度提取的一种行之有效的方法.利用该方法获得的高精度地层速度剖面在中央峡谷水道砂体的速度-岩性识别和地层压力预测方面获得良好的应用效果.     

基于离心窗倾角扫描的曲率属性提取

体曲率属性作为三维地震资料构造解释的重要工具,在识别地层弯曲、地层不连续性,以及裂缝预测方面呈现了不可或缺的作用,因此如何高质量的提取该属性成为一个需要关注的问题.体曲率属性提取质量的关键在于倾角扫描的精度,本文提出了一种基于离心窗的倾角扫描方法,该离心窗是在空间为中心偏离分析点的四个旋转窗口,时间上为包含分析点的五个纵向滑动窗口,通过变换不同的窗口进行高精度倾角估计,并采用最小二乘拟合插值求取真实倾角.该方法避免了多窗扫描对小断层构造的忽略以及噪声的过大影响,同时在处理中对倾角数据进行中值滤波,去掉噪点影响.将由离心窗倾角扫描方法所计算的倾角数据体应用到曲率属性的计算中,在恢复构造细节和提高信噪比方面均展现了较好的效果.     

基于卷积神经网络和MPGA-LM算法的阵列侧向测井快速反演方法

在大斜度井/水平井环境下,阵列侧向测井受钻井液侵入、地层倾角和各向异性等多种因素影响,导致测井响应复杂,需借助反演手段提取地层真实电阻率.然而,阵列侧向测井三维正演效率低,难以满足测井资料快速反演和油气藏快速评价的需求.为此,本文基于深度学习并联合混合多种群遗传(MPGA)与列文伯格马奎特(LM)算法建立了一种快速反演方法.首先从地层参数敏感性出发,基于严格的三维有限元正演算法,依次确定侵入、各向异性和地层倾角的敏感性大小;其次,引入深度学习和模型可视化技术,实现斜井各向异性地层阵列侧向测井响应的快速正演;最后,基于数据集分解技术和混合MPGA-LM算法,实现斜井各向异性地层电阻率剖面快速精确重构.数值模拟结果表明:斜井各向异性地层中,阵列侧向测井响应对侵入深度敏感性最高,各向异性和地层倾角次之;相较于反向传播神经网络(BPNN),二维卷积神经网络(2D-CNN)能够实现阵列侧向测井响应的快速精确计算,计算一个测井点仅需0.36 ms,精度可达99％左右;基于三层反演模型的MPGA-LM混合算法稳定性强,电阻率参数反演精度高的优点,可用于阵列侧向测井资料的快速处理.     

非刚性匹配技术在地震资料一致性处理中的应用

受地震勘探资料的采集施工方法、地表激发接收条件、复杂的地下传播介质等因素的影响会导致同一区块同一地层反射波组的频率、波形不同,静校正不彻底、速度不准会导致剩余时差,动校正拉伸会导致波形畸变,通常在常规一致性处理后的CMP/CRP道集数据仍然存在一些不一致性问题,会影响资料潜力的发挥,资料的一致性处理对处理成果品质的提高起到至关重要的作用,尤其是在多块三维连片处理、时延地震处理、过渡带探区资料处理、叠前反演处理、高分辨率处理、高精度成像处理等方面均对资料的一致性提出了更高的要求.针对该现实问题,介绍了依赖于图像的相似和有局部光滑变形(或位移)场的非刚性匹配处理技术.该技术在常规一致性处理的基础上,首先将叠前数据分选成炮检距数据,优选部分炮检距数据形成模型,然后对每一炮检距数据逐一与模型进行非刚性匹配,实现数据的一致性处理.给出了实际资料非刚性匹配一致性处理的效果,证实了该技术在提高数据的一致性、改善数据处理成果品质、保持分辨率不降低和振幅保真方面具有一定的优势.因此,该技术可适用于时延地震匹配处理、叠前角道集拉平、叠前CMP/CRP道集拉平、振幅随角度变化(AVA)处理、PS波与PP波同相轴匹配处理等.     

基于压缩感知谱反演算法及其在随钻过程中的应用

在超高温高压环境下的深海钻井过程中,受关键层位、尤其是薄弱层(弱抗张力地层)影响,加上缺乏海上超高温高压薄弱层精准快速识别及关键层位精准卡层技术方法,以致钻井事故频发,是海上超高温高压油气勘探开发亟待解决的重要问题.随钻过程中高精度地震数据的应用,作为钻井过程实时监测的手段,能够有效提供钻头前方精细地质构造信息.本文在谱反演提高分辨率技术基础上,提出了基于压缩感知L0范数算法的谱反演算法,并形成完整的提高分辨率技术流程.该方法能够相对保幅保真地实现地震数据的有效拓频,识别薄弱层及实现随钻地震高精度关键层位综合预测.方法在南海西部油田深层的随钻实际数据分析中,有效识别了钻头前方薄弱层、关键地层、储层深度等关键信息,结果与钻后测井资料吻合,验证了方法有效性,为南海西部油田超高温高压钻井的风险评估提供依据.     

一种新的地层弹性参数直接反演方法

地震资料反演的目的是估计地层的物理参数（密度、弹性和粘弹性参数）的实际值．本文中将最佳估计值定义为一种参数集,由该参数集可以算出与地震实际资料最佳拟合的合成地震记录．本文提出一种新的地层弹性参数反演方法,主要适用于各向异性介质（横向各向同性介质）中地层弹性参数的反演．本方法不同于过去传统的反演方法,它是一种完全意义上的全波波动方程反演,利用地震勘探中的多波多分量记录（PP反射波记录和PSV转换波记录）联合进行地层弹性参数犆11和犆33反演,用以判断地层的属性．      

基于相似性重构低频数据的金属矿频域全波形反演

由于深部金属矿埋深和自身的复杂性,利用重、磁、电方法和一般的地震方法很难有效地对其进行高精度定位.全波形反演通过最小化模拟数据与观测数据的差异使深部金属矿的高精度探测成为可能,但全波形反演是一个局部优化过程,需要准确的低频数据作为起始,而这在一般的地震数据采集中难以做到.本文先在频域中使用伴随状态震源函数反演方法,通过震源附近的直达波能精确地反演出震源函数的形态.然后利用得到的高精度震源子波结合褶积与反褶积思想及相似性现象重构含有低频成分的自激自收数据.将该数据应用到全波形反演中,有效缓减了反演过程中出现的周波跳跃现象,并提高了模型反演的正确性.Marmousi模型和金属矿模型的数值模拟实验证明了新方法改善了在没有低频数据时的全波形反演结果,并有较好抗噪性.     

一种时间域全波形反演中的地震子波估计方法

全波形反演具有高精度成像能力,然而由理论走向实际应用还存在很多问题.全波形反演通过匹配波形来更新模型,数据的波形与地震子波有直接的关系.本文介绍了一种针对时间域波形反演的子波估计方法,并将其应用到全波形反演中.由于在频率域子波反演可表示为一个线性优化问题,因此本文先给定一个试探子波,在时间域通过有限差分法正演得到地震波场,并在频率域与观测数据比较分析,反演得到预测子波.此外,本文还简要地介绍了如何使用伴随状态法计算全波形反演的梯度.数值实验证明,本文方法反演得到地震子波与真实子波具有很好的吻合度,并在全波形反演中取得了不错的效果.相比不依赖于子波的方法,提前进行子波估计可提高反演效率.此外,本文方法适用于全波形反演中常使用的多尺度反演策略.     

Look-ahead vertical seismic profiling inversion approach for density and compressional wave velocity in Bayesian framework

To reduce drilling uncertainties, zero-offset vertical seismic profiles can be inverted to quantify acoustic properties ahead of the bit. In this work, we propose an approach to invert vertical seismic profile corridor stacks in Bayesian framework for look-ahead prediction. The implemented approach helps to successfully predict density and compressional wave velocity using prior knowledge from drilled interval. Hence, this information can be used to monitor reservoir depth as well as quantifying high-pressure zones, which enables taking the correct decision during drilling. The inversion algorithm uses Gauss–Newton as an optimization tool, which requires the calculation of the sensitivity matrix of trace samples with respect to model parameters. Gauss–Newton has quadratic rate of convergence, which can speed up the inversion process. Moreover, geo-statistical analysis has been used to efficiently utilize prior information supplied to the inversion process. The algorithm has been tested on synthetic and field cases. For the field case, a zero-offset vertical seismic profile data taken from an offshore well were used as input to the inversion algorithm. Well logs acquired after drilling the prediction section was used to validate the inversion results. The results from the synthetic case applications were encouraging to accurately predict compressional wave velocity and density from just a constant prior model. The field case application shows the strength of our proposed approach in inverting vertical seismic profile data to obtain density and compressional wave velocity ahead of a bit with reasonable accuracy. Unlike the commonly used vertical seismic profile inversion approach for acoustic impedance using simple error to represent the prior covariance matrix, this work shows the importance of inverting for both density and compressional wave velocity using geo-statistical knowledge of density and compressional wave velocity from the drilled section to quantify the prior covariance matrix required during Bayesian inversion.     

Stratigraphic coordinates: a coordinate system tailored to seismic interpretation

In certain seismic data processing and interpretation tasks such as spiking deconvolution, tuning analysis, impedance inversion, and spectral decomposition, it is commonly assumed that the vertical direction is normal to reflectors. This assumption is false in the case of dipping layers and may therefore lead to inaccurate results. To overcome this limitation, we propose a coordinate system in which geometry follows the shape of each reflector and the vertical direction corresponds to normal reflectivity. We call this coordinate system stratigraphic coordinates. We develop a constructive algorithm that transfers seismic images into the stratigraphic coordinate system. The algorithm consists of two steps. First, local slopes of seismic events are estimated by plane‐wave destruction; then structural information is spread along the estimated local slopes, and horizons are picked everywhere in the seismic volume by the predictive‐painting algorithm. These picked horizons represent level sets of the first axis of the stratigraphic coordinate system. Next, an upwind finite‐difference scheme is used to find the two other axes, which are perpendicular to the first axis, by solving the appropriate gradient equations. After seismic data are transformed into stratigraphic coordinates, seismic horizons should appear flat, and seismic traces should represent the direction normal to the reflectors. Immediate applications of the stratigraphic coordinate system are in seismic image flattening and spectral decomposition. Synthetic and real data examples demonstrate the effectiveness of stratigraphic coordinates.     

纵向和横向同时约束AVO反演

为了提高AVO(amplitude versus offset)反演结果的精度和横向连续性,本文提出了一种新的AVO反演约束方法,该方法结合贝叶斯原理和卡尔曼滤波算法实现了对反演参数纵向和横向的同时约束.文章首先结合反演参数的纵向贝叶斯先验概率约束和反演参数的横向连续性假设建立了与卡尔曼滤波算法对应的AVO反演系统的数学模型,然后将该数学模型代入卡尔曼滤波算法框架,利用卡尔曼滤波算法实现了双向约束AVO反演.二维模型测试和实际数据测试结果表明,相对于单纯的纵向贝叶斯先验概率约束,双向约束能更准确地刻画参数的横向变化,得到更准确、横向连续性更好的反演结果.     

多尺度快速匹配追踪多域联合地震反演方法

多尺度快速匹配追踪多域联合地震反演是一种通过地震数据多尺度分解的迭代反演方法.与此同时,在快速匹配追踪算法中引入低频模型约束,有效提高了收敛精度,使反演结果具有丰富的高低频信息.首先通过对大尺度地震资料进行反演得到低频背景.在此基础上,采用中尺度与小尺度地震数据进行逐级迭代用以获得高频数据,因而有效缓解了常规反演方法对于初始模型精度的依赖.最后利用理论模型与实际地震数据进行测试,通过与常规时间域反演方法的反演结果进行对比可以看出,本文方法在地层连续变化处依然可以对变化地层进行精确刻画,且在纵向分辨率提升的同时保持了较好的横向连续性.     

JOINT INVERSION OF SEISMIC AND GEOELECTRIC DATA RECORDED IN AN UNDERGROUND COAL MINE1

Until the present time the ‘ rock-coal-rock’ layer sequence and offsets in coal-seams in underground coal mines have been detected with the aid of seismic waves and geoelectric measurements. In order to determine the geometrical and petrophysical parameters of the coal-seam situation, the data recorded using seismic and geoelectric methods have been inverted independently. In consequence, the inversion of partially inaccurate data resulted in a certain degree of ambiguity. This paper presents the first results of a joint inversion scheme to process underground vertical seismic profiling data, geolectric resistivity and resistance data. The joint inversion algorithm makes use of the damped least-squares method and its weighted version to solve the linearized set of equations for the seismic and geolectric unknowns. In order to estimate the accuracy and reliability of the derived geometrical and petrophysical layer parameters, both a model covariance matrix and a correlation matrix are calculated. The weighted least-squares algorithm is based on the method of most frequent values (MFV). The weight factors depend on the difference between measured data and those calculated by an iteration process. The joint inversion algorithm is tested by means of synthetic data. Compared to the damped least-squares algorithm, the MFV inversion leads to smaller estimation errors as well as lower sensitivities due to the choice of the initial model. It is shown that, compared to an independent inversion, the correlation between the model parameters is definitely reduced, while the accuracy of the parameter estimation is appreciably increased by the joint inversion process. Thus the ambiguity is significantly reduced. Finally, the joint inversion algorithm using the MFV method is applied to underground field data. The model parameters can be derived with a sufficient degree of accuracy, even in the case of noisy data.     

Lithology and fluid prediction from prestack seismic data using a Bayesian model with Markov process prior

We invert prestack seismic amplitude data to find rock properties of a vertical profile of the earth. In particular we focus on lithology, porosity and fluid. Our model includes vertical dependencies of the rock properties. This allows us to compute quantities valid for the full profile such as the probability that the vertical profile contains hydrocarbons and volume distributions of hydrocarbons. In a standard point wise approach, these quantities can not be assessed. We formulate the problem in a Bayesian framework, and model the vertical dependency using spatial statistics. The relation between rock properties and elastic parameters is established through a stochastic rock model, and a convolutional model links the reflectivity to the seismic. A Markov chain Monte Carlo (MCMC) algorithm is used to generate multiple realizations that honours both the seismic data and the prior beliefs and respects the additional constraints imposed by the vertical dependencies. Convergence plots are used to provide quality check of the algorithm and to compare it with a similar method. The implementation has been tested on three different data sets offshore Norway, among these one profile has well control. For all test cases the MCMC algorithm provides reliable estimates with uncertainty quantification within three hours. The inversion result is consistent with the observed well data. In the case example we show that the seismic amplitudes make a significant impact on the inversion result even if the data have a moderate well tie, and that this is due to the vertical dependency imposed on the lithology fluid classes in our model. The vertical correlation in elastic parameters mainly influences the upside potential of the volume distribution. The approach is best suited to evaluate a few selected vertical profiles since the MCMC algorithm is computer demanding.     

Assessing uncertainty in refraction seismic traveltime inversion using a global inversion strategy

To analyse and invert refraction seismic travel time data, different approaches and techniques have been proposed. One common approach is to invert first‐break travel times employing local optimization approaches. However, these approaches result in a single velocity model, and it is difficult to assess the quality and to quantify uncertainties and non‐uniqueness of the found solution. To address these problems, we propose an inversion strategy relying on a global optimization approach known as particle swarm optimization. With this approach we generate an ensemble of acceptable velocity models, i.e., models explaining our data equally well. We test and evaluate our approach using synthetic seismic travel times and field data collected across a creeping hillslope in the Austrian Alps. Our synthetic study mimics a layered near‐surface environment, including a sharp velocity increase with depth and complex refractor topography. Analysing the generated ensemble of acceptable solutions using different statistical measures demonstrates that our inversion strategy is able to reconstruct the input velocity model, including reasonable, quantitative estimates of uncertainty. Our field data set is inverted, employing the same strategy, and we further compare our results with the velocity model obtained by a standard local optimization approach and the information from a nearby borehole. This comparison shows that both inversion strategies result in geologically reasonable models (in agreement with the borehole information). However, analysing the model variability of the ensemble generated using our global approach indicates that the result of the local optimization approach is part of this model ensemble. Our results show the benefit of employing a global inversion strategy to generate near‐surface velocity models from refraction seismic data sets, especially in cases where no detailed a priori information regarding subsurface structures and velocity variations is available.     

Markov chain Monte Carlo algorithms for target-oriented and interval-oriented amplitude versus angle inversions with non-parametric priors and non-linear forward modellings

In geophysical inverse problems, the posterior model can be analytically assessed only in case of linear forward operators, Gaussian, Gaussian mixture, or generalized Gaussian prior models, continuous model properties, and Gaussian-distributed noise contaminating the observed data. For this reason, one of the major challenges of seismic inversion is to derive reliable uncertainty appraisals in cases of complex prior models, non-linear forward operators and mixed discrete-continuous model parameters. We present two amplitude versus angle inversion strategies for the joint estimation of elastic properties and litho-fluid facies from pre-stack seismic data in case of non-parametric mixture prior distributions and non-linear forward modellings. The first strategy is a two-dimensional target-oriented inversion that inverts the amplitude versus angle responses of the target reflections by adopting the single-interface full Zoeppritz equations. The second is an interval-oriented approach that inverts the pre-stack seismic responses along a given time interval using a one-dimensional convolutional forward modelling still based on the Zoeppritz equations. In both approaches, the model vector includes the facies sequence and the elastic properties of P-wave velocity, S-wave velocity and density. The distribution of the elastic properties at each common-mid-point location (for the target-oriented approach) or at each time-sample position (for the time-interval approach) is assumed to be multimodal with as many modes as the number of litho-fluid facies considered. In this context, an analytical expression of the posterior model is no more available. For this reason, we adopt a Markov chain Monte Carlo algorithm to numerically evaluate the posterior uncertainties. With the aim of speeding up the convergence of the probabilistic sampling, we adopt a specific recipe that includes multiple chains, a parallel tempering strategy, a delayed rejection updating scheme and hybridizes the standard Metropolis–Hasting algorithm with the more advanced differential evolution Markov chain method. For the lack of available field seismic data, we validate the two implemented algorithms by inverting synthetic seismic data derived on the basis of realistic subsurface models and actual well log data. The two approaches are also benchmarked against two analytical inversion approaches that assume Gaussian-mixture-distributed elastic parameters. The final predictions and the convergence analysis of the two implemented methods proved that our approaches retrieve reliable estimations and accurate uncertainties quantifications with a reasonable computational effort.     

地层衰减对地震波速度逆散射反演的影响研究

在利用地震波数据进行地球物理反演时,地层对地震波的吸收衰减效应会对地层物性参数的准确反演产生较大的影响,因此利用黏弹性声波方程进行反演更符合实际情形.本文在考虑地层衰减效应进行频率空间域正演模拟的基础上,提出基于黏弹性声波方程的频率域逆散射反演算法并对地震波传播速度进行反演重建,在反演过程中分别用地震波传播复速度和实速度来表征是否考虑地层吸收衰减效应.基于反演参数总变差的正则化处理使反演更加稳定,在反演中将低频反演速度模型作为高频反演的背景模型进行逐频反演,由于单频反演过程中背景模型保持不变,故该方法不需要在每次迭代中重新构造正演算子,具有较高的反演效率;此外本文在反演过程中采用了基于MPI的并行计算策略,进一步提高了反演计算的效率.在二维算例中分别对是否考虑地层吸收衰减效应进行了地震波速度反演,反演结果表明考虑衰减效应可以得到与真实模型更加接近的速度分布结果,相反则无法得到正确的地震波速度重建结果.本文算法对复杂地质模型中浅层可以反演得到分辨率较高的速度模型,为其他地震数据处理提供比较准确的速度信息,在地层深部由于地震波能量衰减导致反演分辨率不太理想.     

Interpretation of shallow refraction seismic data by reflection/refraction tomography

A new algorithm for tomographic inversion of traveltimes of reflected and refracted seismic waves is developed. The inversion gives interface configurations and velocity distributions in layers. The important features of the algorithm are: (a) the inclusion of shot time delays in the list of unknown parameters; (b) the regularization is applied in such a way that the most probable model is characterized by the similarity of neighbouring interfaces. As the problem under consideration is non-linear, several iterations are necessary in order to obtain the final model. In the case of a very inexact initial model, a 'layer-by-layer' inversion strategy is recommended as a first inversion step. The inversion program is supplied with a user interface, thanks to which one can: (a) pick interactively and identify seismic traveltimes; (b) build and edit depth/velocity models; and (c) display calculated traveltime curves and compare them with picked traveltimes as well as with the original seismic sections. The efficiency of the inversion software developed is illustrated by a numerical example and a field example in which shallow seismic data are considered. Application to wide-aperture reflection/refraction profiling (WARRP) data is also possible.