The role of visual saliency in the automation of seismic interpretation

In this paper, we propose a workflow based on SalSi for the detection and delineation of geological structures such as salt domes. SalSi is a seismic attribute designed based on the modelling of human visual system that detects the salient features and captures the spatial correlation within seismic volumes for delineating seismic structures. Using this attribute we cannot only highlight the neighbouring regions of salt domes to assist a seismic interpreter but also delineate such structures using a region growing method and post‐processing. The proposed delineation workflow detects the salt‐dome boundary with very good precision and accuracy. Experimental results show the effectiveness of the proposed workflow on a real seismic dataset acquired from the North Sea, F3 block. For the subjective evaluation of the results of different salt‐dome delineation algorithms, we have used a reference salt‐dome boundary interpreted by a geophysicist. For the objective evaluation of results, we have used five different metrics based on pixels, shape, and curvedness to establish the effectiveness of the proposed workflow. The proposed workflow is not only fast but also yields better results as compared with other salt‐dome delineation algorithms and shows a promising potential in seismic interpretation.     

Wavelet transform-based seismic facies classification and modelling: application to a geothermal target horizon in the NE German Basin

At the geothermal test site near Groß Schönebeck (NE German Basin), a new 3D seismic reflection survey was conducted to study geothermal target layers at around 4 km depth and 150°C. We present a workflow for seismic facies classification and modelling which is applied to a prospective sandstone horizon within the Rotliegend formation. Signal attributes are calculated along the horizon using the continuous Morlet wavelet transform. We use a short mother wavelet to allow for the temporal resolution of the relatively short reflection signals to be analysed. Time-frequency domain data patterns form the input of a neural network clustering using self-organizing maps. Neural model patterns are adopted during iterative learning to simulate the information inherent in the input data. After training we determine a gradient function across the self-organizing maps and apply an image processing technique called watershed segmentation. The result is a pattern clustering based on similarities in wavelet transform characteristics. Three different types of wavelet transform patterns were found for the sandstone horizon. We apply seismic waveform modelling to improve the understanding of the classification results. The modelling tests indicate that thickness variations have a much stronger influence on the wavelet transform response of the sandstone horizon compared with reasonable variations of seismic attenuation. In our interpretation, the assumed thickness variations could be a result of variable paleo-topography during deposition of predominantly fluvial sediments. A distinct seismic facies distribution is interpreted as a system of thicker paleo-channels deposited within a deepened landscape. The results provide constraints for the ongoing development of the geothermal test site.     

南海北部新生界地层的远偏移距折射波速度特征

南海北部为张裂大陆边缘,在新生代时期经历了复杂的地质演化,具有良好油气勘探前景.本文首次利用南海北部某区域长排列地震数据的远偏移距折射波速度计算方法获得南海北部地层层速度,并进行了分析,此外,还提出了非初至折射波的概念.本方法在共偏移距剖面上识别、拾取折射波层位,得到折射波走时的横向连续变化数据,利用不同共偏移距同一折射波层位的到达时差计算该折射层位的速度结构.该方法在南海北部陆坡某区域地震资料的应用,揭示了新生界地层层速度的整体结构,反映了新生代不同时代地层的速度变化特征,为进一步利用折射层速度资料探讨南海新生代地质演化提供了新的视角.研究表明,与常规初至折射波法相比,远偏移距折射波能够提供更多的地质信息;该方法的初步应用能够对远偏移距折射波的研究提供进一步的视角.     

Seismic facies analysis through musical attributes

Seismic facies analysis is a well‐established technique in the workflow followed by seismic interpreters. Typically, huge volumes of seismic data are scanned to derive maps of interesting features and find particular patterns, correlating them with the subsurface lithology and the lateral changes in the reservoir. In this paper, we show how seismic facies analysis can be accomplished in an effective and complementary way to the usual one. Our idea is to translate the seismic data in the musical domain through a process called sonification, mainly based on a very accurate time–frequency analysis of the original seismic signals. From these sonified seismic data, we extract several original musical attributes for seismic facies analysis, and we show that they can capture and explain underlying stratigraphic and structural features. Moreover, we introduce a complete workflow for seismic facies analysis starting exclusively from musical attributes, based on state‐of‐the‐art machine learning computational techniques applied to the classification of the aforementioned musical attributes. We apply this workflow to two case studies: a sub‐salt two‐dimensional seismic section and a three‐dimensional seismic cube. Seismic facies analysis through musical attributes proves to be very useful in enhancing the interpretation of complicated structural features and in anticipating the presence of hydrocarbon‐bearing layers.     

A new energy density calculation method for reservoir delineation using seismic low-frequency signal

In comparison to high-frequency signals, low-frequency seismic signals suffer less from scattering and intrinsic attenuation during wave propagation, penetrate deeper strata and thus can provide more energy information related to the hydrocarbon reservoirs. Based on the asymptotic representation for the frequency-dependent reflections in the fluid-saturated pore-elastic media, we first derive a novel equation of the reservoir energy density and present an efficient workflow to calculate the reservoir energy density using low-frequency seismic data. Then, within a low-frequency range (from 1 to 30 Hz), we construct an objective function to determine the optimal frequency, using the energy densities calculated from the post-stack seismic traces close to the wells. Next, we can calculate the reservoir energy density using the instantaneous spectra of optimal frequency at the low-frequency end of the seismic spectrum. Tests on examples for synthetic and field data demonstrate that the proposed reservoir energy density can produce high-quality images for the fluid-saturated reservoirs, and it produces less background artefacts caused by elastic layers. This method provides a new way to detect the location of hydrocarbon reservoirs and characterize their spatial distribution.     

Noncausal f–x–y regularized nonstationary prediction filtering for random noise attenuation on 3D seismic data

Seismic noise attenuation is very important for seismic data analysis and interpretation, especially for 3D seismic data. In this paper, we propose a novel method for 3D seismic random noise attenuation by applying noncausal regularized nonstationary autoregression (NRNA) in f–x–y domain. The proposed method, 3D NRNA (f–x–y domain) is the extended version of 2D NRNA (f–x domain). f–x–y NRNA can adaptively estimate seismic events of which slopes vary in 3D space. The key idea of this paper is to consider that the central trace can be predicted by all around this trace from all directions in 3D seismic cube, while the 2D f–x NRNA just considers that the middle trace can be predicted by adjacent traces along one space direction. 3D f–x–y NRNA uses more information from circumjacent traces than 2D f–x NRNA to estimate signals. Shaping regularization technology guarantees that the nonstationary autoregression problem can be realizable in mathematics with high computational efficiency. Synthetic and field data examples demonstrate that, compared with f–x NRNA method, f–x–y NRNA can be more effective in suppressing random noise and improve trace-by-trace consistency, which are useful in conjunction with interactive interpretation and auto-picking tools such as automatic event tracking.     

Three-dimensional curvature analysis of seismic waveforms and its interpretational implications

The idea of curvature analysis has been widely used in subsurface structure interpretation from three-dimensional seismic data (e.g., fault/fracture detection and geomorphology delineation) by measuring the lateral changes in the geometry of seismic events. However, such geometric curvature utilizes only the kinematic information (two-way traveltime) of the available seismic signals. While analysing the dynamic information (waveform), the traditional approaches (e.g., complex trace analysis) are often trace-wise and thereby fail to take into account the seismic reflector continuity and deviate from the true direction of geologic deposition, especially for steeply dipping formations. This study proposes extending the three-dimensional curvature analysis to the waveforms in a seismic profile, here denoted as the waveform curvature, and investigates the associated implications for assisting seismic interpretation. Applications to the F3 seismic dataset over the Netherlands North Sea demonstrate the added values of the proposed waveform curvature analysis in four aspects. First, the capability of the curvature operator in differentiating convex and concave bending allows automatic decomposition of a seismic image by the reflector types (peaks, troughs and zero crossings), which can greatly facilitate computer-aided horizon interpretation and modelling from three-dimensional seismic data. Second, the signed minimum curvature offers a new analytical approach for estimating the fundamental and important reflector dip attribute by searching the orientation associated with least waveform variation. Third, the signed maximum curvature makes it possible to analyse the seismic signals along the normal direction of the reflection events. Finally, the curvature analysis promotes the frequency bands of the seismic signals and thereby enhances the apparent resolution on identifying and interpreting subtle seismic features.     

Uniform fragility spectra for the performance‐based seismic design of structures considering variabilities in structural properties

This paper presents a new methodology based on structural performance to determine uniform fragility design spectra, i.e., spectra with the same probability of exceedance of a performance level for a given seismic intensity. The design spectra calculated with this methodology provide directly the lateral strength, in terms of yield‐ pseudo‐accelerations, associated with the rate of exceedance of a specific ductility characterizing the performance level for which the structures will be designed. This procedure involves the assessment of the seismic hazard using a large enough number of seismic records of several magnitudes; these records are simulated with an improved empirical Green function method. The statistics of the performance of a single degree of freedom system are obtained using Monte Carlo simulation considering the seismic demand, the fundamental period, and the strength of the structure as uncertain variables. With these results, the conditional probability that a structure exceeds a specific performance level is obtained. The authors consider that the proposed procedure is a significant improvement to others considered in the literature and a useful research tool for the further development of uniform fragility spectra that can be used for the performance‐based seismic design and retrofit of structures.     

OpendTect系统在层序地层研究中的应用

在等时层序格架建立的基础上,利用地震资料研究各体系域内部沉积体的空间展布、叠置样式和沉积演化过程,进而预测各体系域中砂体的分布,是层序地层学应用于勘探实践的重要内容.OpendTect系统在三维地震倾角扫描的基础上,①以地震采样点空间信息为导向,实现了层序界面控制下的地震小层自动追踪,可以精细刻画出体系域内部沉积体地震反射结构和空间展布;②通过对各层序单元内部地震小层追踪结果进行层拉平,实现了时间域到Wheeler域的自动变换,可以更加直观地分析地层沉积演化历史;③通过沿沉积体内部小层面提取地震属性,实现了等时地层切片的提取,减少了地震属性多解性,提高了预测精度.OpendTect系统提供了一种新的基于层序地层的地震综合解释技术和思路.     

复杂断块砂砾岩油藏地震解释

地震构造层位的解释精度对砂砾岩油藏的预测起关键性作用.本文以夏子街油田夏18-36区块砂砾岩扇体油藏的构造和层位解释为例,研究砂砾岩油藏地震精细解释技术.研究中采用快速地震扫描、相干体分析、精细层位标定、构造样式及断裂组合分析相结合的工作思路,在地震资料品质中偏差先天不足条件下,实现了全工区200余口井的井震统一.运用多种解释方法综合印证来提高砂砾岩油藏的解释精度,为油藏参数的反演和储层预测及注水生产奠定了坚实的基础.     

基于自适应阈值RCSST变换的金属矿山地地区地震信号随机噪声消减

随着陆地地震勘探工作的加深,勘探环境变得越来越复杂,获得的地震信号信噪比越来越低,这给地震成像和数据解释带来了巨大的困难.为了解决这一技术难题,本文针对云南山地金属矿区的勘探环境提出了一种基于自适应阈值递归循环平移的Shearlet变换去噪算法（Recursive Cycle Spinning Shearlet Transform,RCSST）.首次将递归循环平移与Shearlet变换相结合,利用Shearlet变换的多尺度多方向特性对平移后的地震资料进行分解变换,之后,我们又提出了一种全新的自适应阈值,避免了信号系数被过度扼杀,同时也保护了有效信号.实验表明基于自适应阈值的RCSST算法克服了传统Shearlet变换去噪算法在低信噪比下易出现假轴的弊端并且能够有效地保护信号的幅度.在处理较低信噪比的模拟和实际云南山地地区地震资料的过程中,本文方法能够较好的压制随机噪声和保护有效信号.     

利用叠前Kirchhoff积分偏移识别小断裂与低幅度构造

利用常规地震剖面精细解释小断层和低幅度构造存在较大困难.本文利用纵波和横波速度模型对采集得到的二维地震数据进行叠前Kirchhoff积分偏移得到纵波和转换横波深度和时间剖面,根据不同分量的深度和时间剖面联合解释小断层与低构造.深度剖面克服了时间剖面受地层厚度和地层速度共同制约的缺点,有利于识别低幅度构造.纵波（P）剖面与转换横波（PS）剖面相比有利于识别层间小断裂.我们对准噶尔盆地实际地震资料的处理和解释证实了这些优点.     

大港油田三维可视化解释技术的应用实例分析

三维可视化解释技术是一项全新的地震解释技术,与传统的地震资料解释有着本质的不同,它是从三维可视化显示出发,以地质体或三维研究区块为单元,采用点、线、面和体相结合的数据体空间可视化解释。该技术结合相干体技术可进行复杂断层的解释与组合;应用空间域层位自动追踪技术能够准确落实构造形态;采用目标雕刻技术,利用反演数据体可实现立体透视岩性体的空间展布及厚度趋势。将该技术应用在大港油田官西地区复杂断块构造解释和歧南西斜坡隐蔽油气藏储层预测两项实际工作中,取得了良好效果,表明可视化解释技术在石油勘探中具有广阔的应用前景,是提高勘探效率和勘探成功率的有效技术手段。     

Application examples of 3D visual interpretation technology in Dagang Oilfield

3D visualization technology is a new seismic interpretation technology that is completely different from traditional seismic data interpretation technologies. This method takes geologic bodies or any 3D region of interest as a unit and integrates the points, lines, and planes of the bodies to make a 3D visual interpretation. This technology can be integrated with the coherence technique to interpret complex faults. Spatial domain automatic horizon tracing can accurately determine structural features. Target sculpting can determine the spatial dimensions and depth extent of perspective 3D lithology bodies using inversion data. I applied this technology to the interpretation of the complex block structures in the Guanxi area of Dagang Oilfield and the prediction of subtle reservoirs in the western slope of the Qinan sag with good results. This study indicated that the visual interpretation technology can be widely and effectively applied in oil field development and to improve the exploration success ratio.     

基于深度学习的接收函数自动挑选方法

接收函数作为地震学研究中的重要方法之一,在间断面成像、S波速度结构反演方面应用广泛.然而,接收函数方法需要耗费大量的人工成本挑选可用的数据,这不利于我们快速准确地获得地下结构,因此发展快速准确的数据自动处理方法具有十分重要的意义.本文针对这一问题,提出利用深度学习方法自动挑选接收函数,并使用中国地震局的牡丹江地震台(MDJ)和北京地震台(BJT)于2000年至2019年记录的波形数据提取的接收函数进行试验.结果表明,应用深度学习方法挑选接收函数是可行的,使用自动挑选的接收函数对台站下方地壳结构进行估计,结果与使用人工挑选的接收函数估计的结果具有较高的一致性.通过使用不同样本比例的训练集和测试集进行分析,本文提出的方法具有对训练集的数据量要求较低、利用率高、适合多台数据联合训练等特点.该方法在建立台网接收函数自动挑选模型字典、区域接收函数自动挑选模型等方面具有巨大应用潜力.     

火山岩地层地震反射特征和地震-地质联合解释:以徐家围子断陷为例

火山岩地层的地震反射特征可通过地震参数、反射结构与地震相、地震地层结构进行地质解释.徐家围子断陷营城组是一套以火山岩占优势的火山岩地层.钻遇营城组钻井与地震资料匹配给出了各种地震属性、反射结构和地震相的地质解释.在徐家围子断陷地震三维工区开展的火山岩地层的地震-地质联合解释包含三个层次1:地震参数、反射结构与地震相、地...     

Application of a Monte‐Carlo simulation approach for the probabilistic assessment of seismic hazard for geographically distributed portfolio

The conventional integral approach is very well established in probabilistic seismic hazard assessment (PSHA). However, Monte‐Carlo (MC) simulations can become an efficient and flexible alternative against conventional PSHA when more complicated factors (e.g. spatial correlation of ground shaking) are involved. This study aims at showing the implementation of MC simulation techniques for computing the annual exceedance rates of dynamic ground‐motion intensity measures (GMIMs) (e.g. peak ground acceleration and spectral acceleration). We use multi‐scale random field technique to incorporate spatial correlation and near‐fault directivity while generating MC simulations to assess the probabilistic seismic hazard of dynamic GMIMs. Our approach is capable of producing conditional hazard curves as well. We show various examples to illustrate the potential use of the proposed procedures in the hazard and risk assessment of geographically distributed structural systems. Copyright © 2015 John Wiley & Sons, Ltd.     

纵横波弹性阻抗联立反演在GD地区的应用

在GD油田复杂油藏描述中应用叠前纵横波弹性阻抗反演,精确地进行了油藏岩性的划分。利用三个或三个以上部分叠加数据,进行纵横波弹性阻抗联立反演,既克服了因叠后地震反演结果单一而不能满足复杂储层描述的需求,又避免了由于叠前道集信噪比低造成反演结果不稳定的缺陷。本文论述了叠前弹性波阻抗反演的基本原理,结合GD地区实际资料,对反演过程中涉及的角道集子波提取、层位标定、横波速度预测、弹性参数提取与解释等关键步骤进行了详细研究,指出基于测井资料分析的多种弹性参数综合解释是提高叠前地震反演应用效果的关键。     

Earthquake risk prediction as a stochastic process

The extrapolation in time of an earthquake sequence considered as a multidimensional stochastic point process is discussed. Estimates of seismic risk for both long- and short-term predictions are considered and an algorithm for the calculations is proposed. Several examples of short-term extrapolations are carried out by means of Monte Carlo simulations of the process. An assessment of the predictability of the seismic process shows that the catalog of strong earthquakes (M ? 7.0) contains about 0.4 bits of information per earthquake for the particular model of the process applied here.