储层预测的地震属性优选技术研究

地震属性分析的关键是地震属性的优选问题。目前主要用来优选属性的方法有4种,即①专家经验法；②数学理论法；③专家经验与数学理论结合法；④正演模拟确定法。其中,数学理论法具有灵活性强、工作量低、效率较高等优点,是目前进行属性优选的主流方法。在此基础上,提出了一种基于数学理论的地震属性优选方法。该属性优选方法的基本原理是：首先选择有效性大的属性；然后选择符合率大的属性,并对有效性大的属性和符合率大的属性进行交集运算,获得灵敏属性；最后对灵敏属性进行压缩处理,得到用来进行储层预测的属性。给出了各类属性的选择方法和原则,并用检验井对预测效果进行了误差分析。将该方法应用于绥中36-1油田某一储层的含油气性预测,并与单纯利用有效性或符合率的预测结果进行了对比。对比结果和检验井验证结果均表明,该方法的预测效果好,预测结果与实际情况吻合率高。

采区三维地震勘探资料精细处理解释在P2煤矿的应用

P2煤矿在采掘过程中,发现原地震资料解释的地质成果与实际揭露情况存在误差。浅部主采煤层断层位置误差较大,深部主采煤层A煤组与下伏太灰顶界面距离约为40m,煤层及灰岩构造发育情况不明确,存在断层导水隐患。因此,利用先进的层析静校正、叠前偏移技术处理技术,可以使空间成像更准确、断点收敛更清晰;精细解释利用多种地震属性提取技术可全方位观测断裂的展布;蚂蚁体融合技术解释细小构造发育情况及其它异常现象;目标处理有利于解释深部煤层及灰岩顶界面赋存构造形态,特别是与巷道揭露的地质信息进行综合分析,提高了地震勘探的精度和解决地质问题的能力,资料精细处理解释非常必要。从精细处理及解释两个方面采用新技术、新方法深度掘潜了潜在的地震地质信息,论述了精细解释的成果和精度。

Seismic attribute extraction based on HHT and its application in a marine carbonate area

The Hilbert-Huang transform（HHT） is a new analysis method suitable for nonlinear and non-stationary signals.It is very appropriate to seismic signals because they show both non-stationary and nonlinear characteristics.We first introduce the realization of HHT empirical mode decomposition（EMD） and then comparatively analyze three instantaneous frequency algorithms based on intrinsic mode functions（IMF） resulting from EMD,of which one uses the average instantaneous frequency of two sample intervals having higher resolution which can determine that the signal frequency components change with time.The method is used with 3-D poststack migrated seismic data of marine carbonate strata in southern China to effectively extract the three instantaneous attributes.The instantaneous phase attributes of the second intrinsic mode functions（IMF2） better describe the reef facies of the platform margin and the IMF2 instantaneous frequency attribute has better zoning.Combining analysis of the three IMF2 instantaneous seismic attributes and drilling data can identify the distribution of sedimentary facies well.

New seismic attribute:Fractal scaling exponent based on gray detrended fluctuation analysis

Seismic attributes have been widely used in oil and gas exploration and development. However, owing to the complexity of seismic wave propagation in subsurface media, the limitations of the seismic data acquisition system, and noise interference, seismic attributes for seismic data interpretation have uncertainties. Especially, the antinoise ability of seismic attributes directly affects the reliability of seismic interpretations. Gray system theory is used in time series to minimize data randomness and increase data regularity. Detrended fluctuation analysis （DFA） can effectively reduce extrinsic data tendencies. In this study, by combining gray system theory and DFA, we propose a new method called gray detrended fluctuation analysis （GDFA） for calculating the fractal scaling exponent. We consider nonlinear time series generated by the Weierstrass function and add random noise to actual seismic data. Moreover, we discuss the antinoise ability of the fractal scaling exponent based on GDFA. The results suggest that the fractal scaling exponent calculated using the proposed method has good antinoise ability. We apply the proposed method to 3D poststack migration seismic data from southern China and compare fractal scaling exponents calculated using DFA and GDFA. The results suggest that the use of the GDFA-calculated fractal scaling exponent as a seismic attribute can match the known distribution of sedimentary facies.

The algorithm of 3D multi-scale volumetric curvature and its application

To fully extract and mine the multi-scale features of reservoirs and geologic structures in time/depth and space dimensions, a new 3D multi-scale volumetric curvature （MSVC） methodology is presented in this paper. We also propose a fast algorithm for computing 3D volumetric curvature. In comparison to conventional volumetric curvature attributes, its main improvements and key algorithms introduce multi-frequency components expansion in time-frequency domain and the corresponding multi-scale adaptive differential operator in the wavenumber domain, into the volumetric curvature calculation. This methodology can simultaneously depict seismic multi-scale features in both time and space. Additionally, we use data fusion of volumetric curvatures at various scales to take full advantage of the geologic features and anomalies extracted by curvature measurements at different scales. The 3D MSVC can highlight geologic anomalies and reduce noise at the same time. Thus, it improves the interpretation efficiency of curvature attributes analysis. The 3D MSVC is applied to both land and marine 3D seismic data. The results demonstrate that it can indicate the spatial distribution of reservoirs, detect faults and fracture zones, and identify their multi-scale properties.

Semi-supervised least squares support vector machine algorithm:application to offshore oil reservoir

At the early stages of deep-water oil exploration and development, fewer and further apart wells are drilled than in onshore oilfields. Supervised least squares support vector machine algorithms are used to predict the reservoir parameters but the prediction accuracy is low. We combined the least squares support vector machine （LSSVM） algorithm with semi-supervised learning and established a semi-supervised regression model, which we call the semi-supervised least squares support vector machine （SLSSVM） model. The iterative matrix inversion is also introduced to improve the training ability and training time of the model. We use the UCI data to test the generalization of a semi-supervised and a supervised LSSVM models. The test results suggest that the generalization performance of the LSSVM model greatly improves and with decreasing training samples the generalization performance is better. Moreover, for small-sample models, the SLSSVM method has higher precision than the semi-supervised K-nearest neighbor （SKNN） method. The new semi- supervised LSSVM algorithm was used to predict the distribution of porosity and sandstone in the Jingzhou study area.

Application of seismic multi-attribute fusion method based on D-S evidence theory in prediction of CBM-enriched area

D-S evidence theory provides a good approach to fuse uncertain inlbrmation. In this article, we introduce seismic multi-attribute fusion based on D-S evidence theory to predict the coalbed methane （CBM） concentrated areas. First, we choose seismic attributes that are most sensitive to CBM content changes with the guidance of CBM content measured at well sites. Then the selected seismic attributes are fused using D-S evidence theory and the fusion results are used to predict CBM-enriched area. The application shows that the predicted CBM content and the measured values are basically consistent. The results indicate that using D-S evidence theory in seismic multi-attribute fusion to predict CBM-enriched areas is feasible.

Ordovician limestone aquosity prediction using nonlinear seismic attributes:Case from the Xutuan coal mine

Ordovician limestone water is coal mines. In this paper, we analyze the the main source of water inrush in North China characteristic of three kinds of nonlinear seismic attributes, such as the largest lyapunov exponent,fractal dimension and entropy and introduce their calculation methods. Taking the 81st and 82nd coal districts in the Xutuan coal mine as examples, we extract the three seismic attributes based on the 3D prestack migration seismic data of this area, which can display the Ordovician limestone fracture distribution in the mine. We comprehensively analyzed the three nonlinear seismic attributes and compared the results with transient electromagnetic exploration results and determined the possible Ordovician limestone aquosity distribution. This demonstrated that the nonlinear seismic attributes technology is an effective approach to predict the aquosity of Ordovician limestone.

Pre-stack-texture-based reservoir characteristics and seismic facies analysis

Seismic texture attributes are closely related to seismic facies and reservoir characteristics and are thus widely used in seismic data interpretation.However,information is mislaid in the stacking process when traditional texture attributes are extracted from poststack data,which is detrimental to complex reservoir description.In this study,pre-stack texture attributes are introduced,these attributes can not only capable of precisely depicting the lateral continuity of waveforms between different reflection points but also reflect amplitude versus offset,anisotropy,and heterogeneity in the medium.Due to its strong ability to represent stratigraphies,a pre-stack-data-based seismic facies analysis method is proposed using the selforganizing map algorithm.This method is tested on wide azimuth seismic data from China,and the advantages of pre-stack texture attributes in the description of stratum lateral changes are verified,in addition to the method＇s ability to reveal anisotropy and heterogeneity characteristics.The pre-stack texture classification results effectively distinguish different seismic reflection patterns,thereby providing reliable evidence for use in seismic facies analysis.

Seismic sedimentology of conglomeratic sandbodies in lower third member of Shahejie Formation (Palaeogene) in Shengtuo area, East China

The sand-conglomerate fans are the major depositional systems in the lower third member of Shahejie Formation in Shengtuo area, which formed in the deep lacustrine environment characterized by steep slope gradient, near sources and intensive tectonic activity. This work was focused on the sedimentary feature of the glutenite segment to conduct the seismic sedimentology research. The near-shore subaqueous fans and its relative gravity channel and slump turbidite fan depositions were identified according to observation and description of cores combining with the numerous data of seismic and logging. Then, the depositional model was built depending on the analysis of palaeogeomorphology. The seismic attributes which are related to the hydrocarbon but relative independent were chosen to conduct the analysis, the reservoir area of the glutenite segment was found performing a distribution where the amplitude value is relatively higher, and finally the RMS amplitude attribute was chosen to conduct the attribute predicting. At the same time, the horizontal distribution of the sedimentary facies was analyzed qualitatively. At last, the sparse spike inversion method was used to conduct the acoustic impedance inversion, and the inversion result can distinguish glutenite reservoir which is greater than 5 m. This method quantitatively characterizes the distribution area of the favorable reservoir sand.

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Property Identification of Anomalous Seismic Bodies by GMES Techniques, A Case History

What are the anomalous seismic reflection bodies at depths of over 6000m？Are they reefs or igneous rock？This is a difficult problem for seismic techniques,but the GMES technique can handle it .The GMES technique is a joint exploration technique combining gravity,magnetic,electrical,and seismic techniques.The specific procedure is to conduct a 2D interface-constrained CEMP inversion using 2D seismic and log data followed by a property parameter inversion of the anomalous bodics using gravity and seismic data by the stripping technique.We then estimate the physical properties ofthe anomalous bodies,such as density,susceptibility,resistivity,velocity,and etc.to deduce the geological features of the bodies and provide a basis for drilling decisions.The work in the TZ area reported in this paper shows the applicability of the technique.

Developments and prospects of microseismic monitoring technology in underground metal mines in China

Microseismic monitoring technology has become an important technique to assess stability of rock mass in metal mines.Due to the special characteristics of underground metal mines in China,including the high tectonic stress,irregular shape and existence of ore body,and complex mining methods,the application of microseismic technology is more diverse in China compared to other countries,and is more challenging than in other underground structures such as tunnels,hydropower stations and coal mines.Apart from assessing rock mass stability and ground pressure hazards induced by mining process,blasting,water inrush and large scale goaf,microseismic technology is also used to monitor illegal mining,and track personnel location during rescue work.Moreover,microseismic data have been used to optimize mining parameters in some metal mines.The technology is increasingly used to investigate cracking mechanism in the design of rock mass supports.In this paper,the application,research development and related achievements of microseismic technology in underground metal mines in China are summarized.By considering underground mines from the perspective of informatization,automation and intelligentization,future studies should focus on intelligent microseismic data processing method,e.g.,signal identification of microseismic and precise location algorithm,and on the research and development of microseismic equipment.In addition,integrated monitoring and collaborative analysis for rock mass response caused by mining disturbance will have good prospects for future development.

Sedimentary microfacies of the H8 member in the Su14 3D seismic test area

The distribution of sedimentary microfacies in the eighth member of the Shihezi formation（the H8 member） in the Sul4 3D seismic test area was investigated.A Support Vector Machine（SVM） model was introduced for the first time as a way of predicting sandstone thickness in the study area.The model was constructed by analysis and optimization of measured seismic attributes.The distribution of the sedimentary microfacies in the study area was determined from predicted sandstone thickness and an analysis of sedimentary characteristics of the area.The results indicate that sandstone thickness predictions in the study area using an SVM method are good.The distribution of the sedimentary microfacies in the study area has been depicted at a fine scale.

Analysis and interpretation on the concrete quality of shaft lining by elastic wave technique

The quality problem of the concrete body and backwall grouting of shaft lining must be taken into consideration during the engineering construction of the shaft. Detection and evaluation are needed to determine the parameters such as the location and depth of drilling. The record of elastic wave can be gained through laying the surveying lines of the ring and ver- tical direction in the shaft lining by the elastic wave method. And specifically, through analyzing the different parameters of seismic attribute such as the velocity of high frequency reflection wave, amplitude and frequency, the abnormal range on the wall or under the wall can be forecasted. The concrete quality of shallow layer in the shaft lining can be evaluated through the velocity of surfer wave. Using the evaluating technique of comprehensive frequency and the phase feature of waveform, the basic features such as inner construction, wall back filling and failure depth of shaft lining can be interpreted from qualitatively to half quantitatively, and the interpreting section can be drawn. The results show that the detection effect for the shaft quality is significant by elastic wave technique, and the delineation of abnormal areas is accurate. Its guidance function is better for pro- duction.

Applications of texture attribute analysis to seismic interpretation

The first generation coherence algorithm(namely C1 algorithm) is based on the statistical cross-correlation theory, which calculates the coherency of seismic data along both in-line and cross-line. The work, based on texture technique, makes full use of seismic information in different directions and the difference of multi-traces, and proposes a novel methodology named the texture coherence algorithm for seismic reservoir characterization, for short TEC algorithm. Besides, in-line and cross-line directions, it also calculates seismic coherency in 45° and 135° directions deviating from in-line. First, we clearly propose an optimization method and a criterion which structure graylevel co-occurrence matrix parameters in TEC algorithm. Furthermore, the matrix to measure the difference between multi-traces is constructed by texture technique, resulting in horizontal constraints of texture coherence attribute. Compared with the C1 algorithm, the TEC algorithm based on graylevel matrix is of the feature that is multi-direction information fusion and keeps the simplicity and high speed, even it is of multi-trace horizontal constraint, leading to significantly improved resolution. The practical application of the TEC algorithm shows that the TEC attribute is superior to both the C1 attribute and amplitude attribute in identifying faults and channels, and it is as successful as the third generation coherence.

Gas reservoir identification by seismic AVO attributes on fluid substitution

Traditionally, fluid substitutions are often conducted on log data for calculating reservoir elastic properties with different pore fluids. Their corresponding seismic responses are computed by seismic forward modeling for direct gas reservoir identification. The workflow provides us with the information about reservoir and seismic but just at the well. For real reservoirs, the reservoir parameters such as porosity, clay content, and thickness vary with location. So the information from traditional fluid substitution just at the well is limited. By assuming a rock physics model linking the elastic properties to porosity and mineralogy, we conducted seismic forward modeling and AVO attributes computation on a three-layer earth model with varying porosity, clay content, and formation thickness. Then we analyzed the relations between AVO attributes at wet reservoirs and those at the same but gas reservoirs. We arrived at their linear relations within the assumption framework used in the forward modeling. Their linear relations make it possible to directly conduct fluid substitution on seismic AVO attributes. Finally, we applied these linear relations for fluid substitution on seismic data and identified gas reservoirs by the cross-plot between the AVO attributes from seismic data and those from seismic data after direct fluid substitution.

Calculation method of the signal-to-noise ratio attribute of seismic data based on structural orientation

At present,most signal-to-noise ratio(SNR)estimation methods can only calculate the global and not the local SNR of seismic data.This paper proposes a calculation method of a structure-oriented-based seismic SNR attribute.The purpose is to characterize the temporal and spatial variation of the seismic data SNR.First,the local slope parameters of the seismic events are calculated using a plane wave decomposition filter.Then,the singular value decomposition method is used to calculate the local seismic data SNR,thereby obtaining it in time and space.The proposed method overcomes the insufficiency of a conventional global SNR to characterize any local seismic data features and uses the SNR as an attribute of seismic data to more accurately describe the signal-noise energy distribution characteristics of seismic data in time and space.The results of a theoretical model test and real data processing show that the SNR attribute can be used not only for seismic data quality evaluation but also for analysis and evaluation of denoising methods.

Application of seismic facies and attributes analysis on the identification of Permian igneous rock

Seismic facies and attributes analysis techniques are introduced.The geological characteristics of some oil fields in western China are used in conjunction with drilling results and logging data to identify the lithology,intrusion periods,and distribution range of the Permian igneous rocks in this area.The lithologic classification,the vertical and horizontal distribution,and the intrusion periods of igneous rock were deduced through this study.Combining seismic facies and attributes analysis based on optimization can describe the igneous rock in detail.This is an efficient way to identify lithology and intrusion periods.Using geological data and GR-DT logging cross-plots the Permian igneous rock from TP to TT was divided into three periods.The lithology of the first period is tuff and clasolite with a thickness ranging from 18 to 80 ms.The second is basalt with a thickness ranging from 0 to 20 ms.The third is tuff and clasolite and dacite whose thickness ranges from 60 to 80 ms.These results can help understand the clasolite trap with low amplitude and the lithologic trap of the Carboniferous and Silurian.They can also guide further oil and/or gas exploration.

致密砂岩油气藏地震地质研究关键技术

致密砂岩油气藏在当前油气勘探开中占有重要地位.地震地质综合研究是此类油气藏勘探开发工作的重要组成部分.多数致密砂岩油气藏地表条件复杂,导致地震资料品质较差.油气藏岩性致密、低孔、低渗的地质特征导致地球物理异常微弱.这两个不利条件给地震地质综合研究造成了较大的困难.结合致密砂岩油气藏地震地质研究工作实践,本文对地震资料特殊处理、储层和特殊地质体地震预测与描述的关键技术进展及应用效果进行了阐述.面对黄土塬、山地等复杂地表采集条件和致密砂岩储层等特殊地质体地震地质研究的需要,介绍了高精度静校正、叠前多域去噪、地表一致性振幅补偿、小波变换提高分辨率处理技术及在鄂尔多斯盆地一些探区的应用情况.围绕致密储层等特殊地质体的地震预测与描述需求,着重介绍了致密砂泥岩地层岩石物理分析、煤系等特殊地层的地震属性分析与优化、致密碎屑岩储层反演、叠前地震资料流体检测、致密储层裂缝发育带地震预测和储层综合建模等关键技术在部尔多斯及川西探区实际应用中面临的具体问题、改进对策和实施效果.