Robust salt-dome detection using the ranking of texture-based attributes

The accurate interpretation and analysis of seismic data heavily depends on the robustness of the algorithms used. We focus on the robust detection of salt domes from seismic surveys. We discuss a novel feature-ranking classification model for saltdome detection for seismic images using an optimal set of texture attributes. The proposed algorithm overcomes the limitations of existing texture attribute-based techniques, which heavily depend on the relevance of the attributes to the geological nature of salt domes and the number of attributes used for accurate detection. The algorithm combines the attributes from the Gray-Level Co-occurrence Matrix (GLCM), the Gabor filters, and the eigenstructure of the covariance matrix with feature ranking using the information content. The top-ranked attributes are combined to form the optimal feature set, which ensures that the algorithm works well even in the absence of strong reflectors along the salt-dome boundaries. Contrary to existing salt-dome detection techniques, the proposed algorithm is robust and computationally efficient, and works with small-sized feature sets. I used the Netherlands F3 block to evaluate the performance of the proposed algorithm. The experimental results suggest that the proposed workflow based on information theory can detect salt domes with accuracy superior to existing salt-dome detection techniques.     

Semi-auto horizon tracking guided by strata histograms generated with transdimensional Markov-chain Monte Carlo

Although horizon interpretation is a routine task for building reservoir models and accurately estimating hydrocarbon production volumes, it is a labour-intensive and protracted process. Hence, many scientists have worked to improve the horizon interpretation efficiency via auto-picking algorithms. Nevertheless, the implementation of a classic auto-tracking method becomes challenging when addressing reflections with weak and discontinuous signals, which are associated with complicated structures. As an alternative, we propose a workflow consisting of two steps: (1) the computation of strata histograms using transdimensional Markov-chain Monte Carlo and (2) horizon auto-tracking using waveform-based auto-tracking guided by those strata histograms. These strata histograms generate signals that are vertically sharper and more laterally continuous than original seismic signals; therefore, the proposed workflow supports the propagation of waveform-based auto-picking without terminating against complicated geological structures. We demonstrate the performance of the novel horizon auto-tracking workflow through seismic data acquired from the Gulf of Mexico, and the Markov-chain Monte Carlo inversion results are validated using log data. The auto-tracked results show that the proposed method can successfully expand horizon seed points even though the seismic signal continuity is relatively low around salt diapirs and large-scale faults.     

盐下构造速度建模与逆时偏移成像研究及应用

盐丘速度建模及成像是盐下油气藏勘探有关技术瓶颈问题.盐下构造由于盐丘速度与围岩地层差异大,且厚度横向变化大,造成地震波场复杂及时间域构造畸变.针对H区复杂盐丘的地质特征,通过技术创新重新认识盐下油气藏.针对盐丘速度建模的难点,提出了"多信息约束层控实体建模技术",采用序贯高斯模拟及克里金趋势约束速度反演方法,较好解决了盐下速度异常问题,大大提高了速度建模的精度;针对盐下复杂构造成像, 基于有限差分方法研究了精确且高效的差分格式逆时波场外推算法.基于GPU/CPU协同平台,将波场延拓通过GPU实现.采用逆时偏移深度域成像技术,使高角度反射界面、甚至超过90°盐丘侧翼界面的反射波精确成像.通过盐丘理论模型试算验证算法及方法的正确性.上述方法解决了盐丘速度建模精度问题、盐丘侧翼的回转构造成像问题,实现了对盐丘边界及盐丘侧翼的准确归位.消除了速度异常造成的时间域构造畸变,使盐下地层在深度域能够准确成像.     

主动底辟盐构造的二维离散元模拟

盐岩底辟构造是一类与油气藏关系极为密切的构造类型,是当前国外构造地质研究和油气勘探的热点.目前盐岩底辟构造的研究仍处于描述性阶段,对盐及其相关构造动力学演化的研究还很薄弱.对于油气而言,盐岩底辟构造既提供了油气运移的驱动力和运移通道,又可以产生丰富的圈闭.离散元模拟是构造地质地球物理研究中一种新的动力学模拟方法,作者通过构建离散元模型,模拟了主动底辟盐构造断裂系统的演化过程,结果表明,该断裂系统的发育主要经历了对称变形、不对称变形、整体破裂三个阶段.研究结果揭示了盐岩底辟构造的动力学演化过程,有利于提高对盐岩底辟构造相关油气藏的勘探和预测.     

Salt body detection from seismic data via sparse representation

In seismic interpretation and seismic data analysis, it is of critical importance to effectively identify certain geologic formations from very large seismic data sets. In particular, the problem of salt characterization from seismic data can lead to important savings in time during the interpretation process if solved efficiently and in an automatic manner. In this work, we present a novel numerical approach that is able to automatically segmenting or identifying salt structures from a post‐stack seismic data set with a minimum intervention from the interpreter. The proposed methodology is based on the recent theory of sparse representation and consists in three major steps: first, a supervised learning assisted by the user which is performed only once, second a segmentation process via unconstrained ?₁ optimization, and finally a post‐processing step based on signal separation. Furthermore, since the second step only depends upon local information at each time, the whole process greatly benefits from parallel computing platforms. We conduct numerical experiments in a synthetic 3D seismic data set demonstrating the viability of our method. More specifically, we found that the proposed approach matches up to 98.53% with respect to the corresponding 3D velocity model available in advance. Finally, in appendixes A and B, we present a convergence analysis providing theoretical guarantees for the proposed method.     

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.     

Seismic driven probabilistic classification of reservoir facies for static reservoir modelling: a case history in the Barents Sea

In this paper we present a case history of seismic reservoir characterization where we estimate the probability of facies from seismic data and simulate a set of reservoir models honouring seismically‐derived probabilistic information. In appraisal and development phases, seismic data have a key role in reservoir characterization and static reservoir modelling, as in most of the cases seismic data are the only information available far away from the wells. However seismic data do not provide any direct measurements of reservoir properties, which have then to be estimated as a solution of a joint inverse problem. For this reason, we show the application of a complete workflow for static reservoir modelling where seismic data are integrated to derive probability volumes of facies and reservoir properties to condition reservoir geostatistical simulations. The studied case is a clastic reservoir in the Barents Sea, where a complete data set of well logs from five wells and a set of partial‐stacked seismic data are available. The multi‐property workflow is based on seismic inversion, petrophysics and rock physics modelling. In particular, log‐facies are defined on the basis of sedimentological information, petrophysical properties and also their elastic response. The link between petrophysical and elastic attributes is preserved by introducing a rock‐physics model in the inversion methodology. Finally, the uncertainty in the reservoir model is represented by multiple geostatistical realizations. The main result of this workflow is a set of facies realizations and associated rock properties that honour, within a fixed tolerance, seismic and well log data and assess the uncertainty associated with reservoir modelling.     

区域性地震安全性评价发展现状与定位初探

对区域性地震安全性评价的产生背景进行回顾,系统地梳理相关制度建设和业务开展的基本现状,总结在属性定位、推进力度、技术要求和监管力度等方面存在的主要问题,并对基本原因进行分析,提出区域性地震安全性评价的属性定位设计和在抗震设防要求管理体系中的位置,以期为深化改革和加快制度建设提供参考。     

A compound method for random noise attenuation

Random noise attenuation, preserving the events and weak features by improving signal‐to‐noise ratio and resolution of seismic data are the most important issues in geophysics. To achieve this objective, we proposed a novel seismic random noise attenuation method by building a compound algorithm. The proposed method combines sparsity prior regularization based on shearlet transform and anisotropic variational regularization. The anisotropic variational regularization which is based on the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation attenuates noises while preserving the events of seismic data and it effectively avoids the fine‐scale artefacts due to shearlets from the restored seismic data. The proposed method is formulated as a convex optimization problem and the split Bregman iteration is applied to solve the optimization problem. To verify the effectiveness of the proposed method, we test it on several synthetic seismic datasets and real datasets. Compared with three methods (the linear combination of weighted anisotropic total variation and anisotropic second‐order total variation, shearlets and shearlet‐based weighted anisotropic total variation), the numerical experiments indicate that the proposed method attenuates random noises while alleviating artefact and preserving events and features of seismic data. The obtained result also confirms that the proposed method improves the signal‐to‐noise ratio.     

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.     

Application of 3D vertical seismic profile multi‐component data to tight gas sands‡

Due to the complicated geophysical character of tight gas sands in the Sulige gasfield of China, conventional surface seismic has faced great challenges in reservoir delineation. In order to improve this situation, a large‐scale 3D‐3C vertical seismic profiling (VSP) survey (more than 15 000 shots) was conducted simultaneously with 3D‐3C surface seismic data acquisition in this area in 2005. This paper presents a case study on the delineation of tight gas sands by use of multi‐component 3D VSP technology. Two imaging volumes (PP compressional wave; PSv converted wave) were generated with 3D‐3C VSP data processing. By comparison, the dominant frequencies of the 3D VSP images were 10–15 Hz higher than that of surface seismic images. Delineation of the tight gas sands is achieved by using the multi‐component information in the VSP data leading to reduce uncertainties in data interpretation. We performed a routine data interpretation on these images and developed a new attribute titled ‘Centroid Frequency Ratio of PSv and PP Waves’ for indication of the tight gas sands. The results demonstrated that the new attribute was sensitive to this type of reservoir. By combining geologic, drilling and log data, a comprehensive evaluation based on the 3D VSP data was conducted and a new well location for drilling was proposed. The major results in this paper tell us that successful application of 3D‐3C VSP technologies are only accomplished through a synthesis of many disciplines. We need detailed analysis to evaluate each step in planning, acquisition, processing and interpretation to achieve our objectives. High resolution, successful processing of multi‐component information, combination of PP and PSv volumes to extract useful attributes, receiver depth information and offset/ azimuth‐dependent anisotropy in the 3D VSP data are the major accomplishments derived from our attention to detail in the above steps.     

Connectivity constrained segmentation of geologic features

Segmentation of geologic features plays a significant role in seismic interpretation. Based on the segmentation results, interpreters can readily recognize the shape and distribution of geologic features in three-dimensional space and conduct further quantitative analysis. Usually, there are mainly two steps for the segmentation of geologic features: the first step is to extract seismic attributes that can highlight the occurrence of geologic features, and the second step is to apply the segmentation algorithm on the seismic attribute volumes. However, the occurrence of geologic features is not always corresponding to the anomaly value on the seismic attribute volumes and vice versa because of several factors, such as noise in the seismic data, the limited resolution of seismic images and the limited effectiveness of the seismic attribute. Therefore, the segmentation results, which are generated solely based on seismic attributes, are not sufficient to give an accurate depiction of geologic features. Aiming at this problem, we introduce the connectivity constraint into the process of segmentation based the assumption that for one single geologic feature all of its components should be connected to each other. Benefiting from this global constraint, the segmentation results can precisely exclude the interference by false negatives on seismic attribute volumes. However, directly introducing the connectivity constraint into segmentation would face the risk that the segmentation results would deteriorate significantly because of false positives with relatively large area when the connectivity constraints are enforced. Therefore, based on the seismic attribute that highlights the boundary of geologic feature, we further propose a post-processing technique, called pruning, to refine the segmentation results. By taking the segmentation of the channel as an example, we demonstrate that the proposed method is able to preserve the connectivity in the process of segmentation and generate better segmentation results on the field data.     

Structural complexity-guided predictive filtering

Random noise attenuation utilizing predictive filtering achieves great performance in denoising seismic data. Conventional predictive filtering methods are based on fixed filter operators and neglect the complexity of structures. In this way, the denoised data cannot meet the requirement of balancing the signal preservation and noise removal. In this study, we proposed a structural complexity-guided predictive filtering method that utilizes an adapted filter operator to adjust the changes of structural complexity. The proposed structural complexity-guided predictive filtering mainly consists of two stages. A slope field information is acquired according to plane-wave destruction to assess the structural complexity. In addition, an adaptive filter operator is obtained to denoise the seismic data according to the adaptive factor. Both synthetic data and real seismic profiles are employed to examine the denoising capacity and flexibility of the refined predictive filtering using adaptive lengths. The analysis of the predicted results shows that adaptive predictive filtering is powerful and has the ability to eliminate random noises with negligible distortions.     

Seismic data interpolation using generalised velocity‐dependent seislet transform

Data interpolation is an important step for seismic data analysis because many processing tasks, such as multiple attenuation and migration, are based on regularly sampled seismic data. Failed interpolations may introduce artifacts and eventually lead to inaccurate final processing results. In this paper, we generalised seismic data interpolation as a basis pursuit problem and proposed an iteration framework for recovering missing data. The method is based on non‐linear iteration and sparse transform. A modified Bregman iteration is used for solving the constrained minimisation problem based on compressed sensing. The new iterative strategy guarantees fast convergence by using a fixed threshold value. We also propose a generalised velocity‐dependent formulation of the seislet transform as an effective sparse transform, in which the non‐hyperbolic normal moveout equation serves as a bridge between local slope patterns and moveout parametres in the common‐midpoint domain. It can also be reduced to the traditional velocity‐dependent seislet if special heterogeneity parametre is selected. The generalised velocity‐dependent seislet transform predicts prestack reflection data in offset coordinates, which provides a high compression of reflection events. The method was applied to synthetic and field data examples, and the results show that the generalised velocity‐dependent seislet transform can reconstruct missing data with the help of the modified Bregman iteration even for non‐hyperbolic reflections under complex conditions, such as vertical transverse isotropic (VTI) media or aliasing.     

High-order generalized screen propagator migration based on particle swarm optimization

Various migration methods have been proposed to image high-angle geological structures and media with strong lateral velocity variations; however, the problems of low precision and high computational cost remain unresolved. To describe the seismic wave propagation in media with lateral velocity variations and to image high-angle structures, we propose the generalized screen propagator based on particle swarm optimization (PSO-GSP), for the precise fitting of the single-square-root operator. We use the 2D SEG/EAGE salt model to test the proposed PSO-GSP migration method to image the faults beneath the salt dome and compare the results to those of the conventional high-order generalized screen propagator (GSP) migration and split-step Fourier (SSF) migration. Moreover, we use 2D marine data from the South China Sea to show that the PSO-GSP migration can better image strong reflectors than conventional imaging methods.     

An automated cross‐validation method to assess seismic time‐to‐depth conversion accuracy: a case study on the Cooper and Eromanga basins,Australia

Selecting a seismic time‐to‐depth conversion method can be a subjective choice that is made by geophysicists, and is particularly difficult if the accuracy of these methods is unknown. This study presents an automated statistical approach for assessing seismic time‐to‐depth conversion accuracy by integrating the cross‐validation method with four commonly used seismic time‐to‐depth conversion methods. To showcase this automated approach, we use a regional dataset from the Cooper and Eromanga basins, Australia, consisting of 13 three‐dimensional (3D) seismic surveys, 73 two‐way‐time surface grids and 729 wells. Approximately 10,000 error values (predicted depth vs. measured well depth) and associated variables were calculated. The average velocity method was the most accurate overall (7.6 m mean error); however, the most accurate method and the expected error changed by several metres depending on the combination and value of the most significant variables. Cluster analysis tested the significance of the associated variables to find that the seismic survey location (potentially related to local geology (i.e. sedimentology, structural geology, cementation, pore pressure, etc.), processing workflow, or seismic vintage), formation (potentially associated with reduced signal‐to‐noise with increasing depth or the changes in lithology), distance to the nearest well control, and the spatial location of the predicted well relative to the existing well data envelope had the largest impact on accuracy. Importantly, the effect of these significant variables on accuracy were found to be more important than choosing between the four methods, highlighting the importance of better understanding seismic time‐to‐depth conversions, which can be achieved by applying this automated cross‐validation method.     

基于地震属性各向异性的火山机构识别技术

本文通过对地震资料的分方位处理,分别提取各方位的地震属性,筛选出最具代表性的地震属性,通过属性方位椭圆拟合,求取该属性椭圆的"扁率",用椭圆"扁率"来表征储层的各向异性,并在此基础上识别火山机构.本文提出了基于地震属性各向异性的火山机构识别方法,利用叠前地震资料分方位提取地震属性,并对方位地震属性值进行椭圆拟合,利用椭...     

A new seismic attribute for ambiguity reduction in hydrocarbon prediction

Hydrocarbon prediction from seismic amplitude and amplitude‐versus‐offset is a daunting task. Amplitude interpretation is ambiguous due to the effects of lithology and pore fluid. In this paper, we propose a new attribute “J” based on a Gassmann–Biot fluid substitution to reduce ambiguity. Constrained by seismic and rock physics, the J attribute has good ability to detect hydrocarbons from seismic data. There are currently many attributes for hydrocarbon prediction. Among the existing attributes, far‐minus‐near times far and fluid factor are commonly used. In this paper, the effectiveness of these two existing attributes was compared with the new attribute. Numerical modelling was used to test the new attribute “J” and to compare “J” with the two existing attributes. The results showed that the J attribute can predict the existence of hydrocarbon in different porosity scenarios with less ambiguity than the other two attributes. Tests conducted with real seismic data demonstrated the effectiveness of the J attribute. The J attribute has performed well in scenarios in which the other two attributes gave inaccurate predictions. The proposed attribute “J” is fast and simple, and it could be used as a first step in hydrocarbon analysis for exploration.     

Quantitative pore-type characterization from well logs based on the seismic petrophysics in a carbonate reservoir

Quality, availability and consistency of the measured and interpreted well log data are essential in the seismic reservoir characterization methods, and seismic petrophysics is the recommended workflow to achieve data consistency between logs and seismic domains. This paper uses seismic petrophysics workflow to improve well logs and pore geometry interpretations for an oil carbonate reservoir in the Fahliyan Formation in the southwest of Iran. The petrophysical interpreted well logs, rock physics and well-to-seismic tie analysis are integrated into the proposed workflow. Our implementation incorporates revising petrophysical well log interpretations and updating pore geometry characteristics to obtain a better well-tie quality. We first propose an improved pore-type characterization approach based on both P- and S-wave velocities for quantifying pore geometry. Then, seismic logs are estimated accordingly, and the results are used in the well-to-seismic analysis. The quality of the well-tie is improved, furthermore, by iterating on the petrophysical interpreted well logs as well as the calculated pore geometries. For the intervals with high-quality data, our workflow improves the consistency between the results of measured and modelled seismic logs. For the intervals with problematic well logs, the application of our proposed workflow results in the successful replacement of the poor data and subsequently leads to an improved wavelet estimation and well-tie results. In both cases, a higher quantification of pore geometries is achieved, which in turn is confirmed by the core images and formation micro-imager analysis.