利用常规的相干类属性与蚂蚁追踪组合技术所获取的不连续性信息,难以满足油田对于河流相和三角洲相薄砂岩储层精细剖析的需求.为了更好地检测薄砂岩储层内部不连续性,首先利用对薄砂岩储层变化更加敏感的均方根振幅属性计算灰度共生矩阵的均质性统计量,初步得到薄砂岩储层的不连续性特征数据;然后根据其不连续性结构的展布特点,运用路径弯曲度约束人工蚂蚁的移动方向,优化蚁群算法的平面增强效果,达到压制干扰信息、突出不连续性特征的目的;最终形成更适应薄砂岩储层不连续性检测的组合技术.模型和实际工区数据的应用结果表明,采用上述组合技术能较好地识别薄砂岩体的边缘以及其内部的小尺度不连续性结构,说明了该技术对薄砂岩储层内部不连续性检测的有效性,并且其检测结果能用于提升砂岩厚度预测精度,为后续的砂体内部结构精细刻画提供技术支持.

海上油田开发中后期,识别并预测河流相砂体边界是老油田剩余油挖潜中亟待解决的重点问题.由于海上井点少、井距大,砂体尺度小、散度大;密井网条件下的测井方法和常规砂体边界地震识别方法并不适用.基于能反映砂体横向展布的地震属性,使用联合双边滤波、Otsu自适应阈值处理方法及形态学算法改进Canny算子,进行河流相砂体边界的预测,形成了一套自适应、高精度的河流相砂体边界的预测方法.从河流相砂体的沉积特点和演变规律出发,建立了 一组正演模型,验证了方法的有效性.将此方法应用于渤海Q油田,在目标层取得了良好的效果.

在河流相砂体储层中,由于河道凸岸侧向加积,以及河道本身的迁移摆动等,常常使河道砂体产生了叠置沉积界面.这些不连续性沉积界限的存在,会直接影响到砂体内流体的流动,影响河流相砂岩油气藏的开发效果.针对河流相叠置砂体不连续界限检测问题,提出了一种展开相位谱属性识别叠置砂体不连续性界限位置的方法.具体思路是对小时窗内地震数据求得展开相位谱,并将展开相位谱转化为积分相位谱属性,通过这种属性的变化来识别砂体叠置的不连续性界限位置.

本文提出的断层自动识别方法HCA能够有效改善这一现状.HCA利用混合卷积进行非连续特征提取,然后使用通道注意力机制捕捉断层证据,最后通过ASPP模块进一步提取地震图像的多尺度特征用于断层结果输出.在设计实验中本模型的准确率为0.9502,高于其他模型,能够用于实际地震解释节省时间和人力成本.

金课建设是高校人才培养体系的关键.基于OBE理念,文章从课程目标、达成途径和评价机制三方面介绍了弹性波动力学课程有关金课建设的探索与实践.其探索与实践过程以预期学习成果为核心,树立知识、能力、素质培养有机结合的课程目标,以学生为中心设置教学模式,采用多种教学方法提升课程的互动性和探究性,优化考核方式,建立完善的教学和评价体系,推动进行基于评价的持续改进,切实提高课程水平和教学质量,有针对性、系统性、持久性地推动高校金课建设.

作为油气目标勘探的最主要技术手段,地震勘探具有资料覆盖面广、技术手段灵活多样、(储层)预测精度高等特点.地震解释的目的是为了从地震数据中提取更多的信息进行地下构造解释以及地层和岩性特征的描述.在构造地质理论的指导下,充分运用测井、地质、地震资料,综合利用地震解释方法,建立川东北地区构造圈闭识别技术流程,在广安地区五峰组构造圈闭识别中取得了较好的应用效果.

随着石油勘探领域的快速发展,勘探精度要求越来越高,关于薄层的定量解释也亟待解决.这里提出在自适应时窗上提取波形结构属性,并建议利用此方法对薄层作定量解释.首先基于楔形模型介绍地震垂向分辨率问题;其次给出波形结构属性的定义与方法原理;最后着重分析固定时窗、半个峰谷时窗、自适应时窗对波形结构属性提取的影响,并详细分析波形结构属性随楔形厚度的变化情况.通过理论模型的建立、波形结构属性的提取分析得出结论:在自适应时窗上提取波形结构属性,更能反映波形随楔形厚度的变化,并有效地刻画了振幅随时间的变化,其实质也是在提高地震垂向分辨率,有助于对薄层作定性和定量的分析与解释.

At present,the method to predict seismic reflection patterns with neural network generally fails to keep outputs constant.The outputs vary and hence the accuracy of prediction cannot be guaranteed.Besides,the prediction values can only indicate reflection types and fail to reveal corresponding geological indications and meanings.Therefore,a method under the constraint of geological information for the prediction of seismic reflection patterns with probabilistic neural network has been built up based on the constructed geological information database and geological-to-seismic transformation technology,which can solve two critical problems about,firstly,how to reflect the constraint of geological information on seismic information,and secondly,how to accomplish the high-accuracy prediction of seismic reflection patterns under the constraint of geological information.In this paper,with a certain fluvial reservoir in the QHD32-6 oilfield of the Bohai sea as an example,where 20 wells were chosen for tests and another 25 wells for constraint,the coincidence rate for the prediction of reservoir seismic reflection patterns under the above method reached up to 86％.As indicated by simulation and in-field application,when the main seismic frequency is about 45 Hz,with the above method,the channel sand bodies of 3～8 m in thickness can be effectively identified and the number of mudstone interlayers thicker than 2 m distinguished.Therefore,the method is thought characterized by high effectiveness,stabilization,intellectualization,great interpretability,etc..

在河流相储层中,砂体叠置现象非常普遍,砂体单层厚度相对较薄,横向变化复杂,受地震资料分辨率所限及断层发育的影响,砂体边界不易识别,增加了河流沉积砂体的储层预测难度.在前人研究的基础上,首先建立河流相沉积砂体叠置模型,正演分析叠置砂体在地震记录上的波形强弱、尖锐度、稳定性、对称性交化特征以及同相轴的连续性;其次通过模型验证在一个周期时窗上提取波形结构属性的有效性,并提出利用波形结构属性识别砂体叠置区的方法;最后应用实际资料,验证了基于波形结构属性识别砂体叠置区方法的可行性.

The probabilistic neural network (PNN),with simple training method and good classification ability,can be utilized in pattern recognition of seismic attributes.However,the number of well logs is lack at the early stage of seismic exploration,which leads to the sample size of the pattern recognition using seismic attributes is small.In this case,the critical issues are also the smoothing parameter and the selection of training samples,which can determine the recognition accuracy of the network.Firstly we analyzed the influence of smoothing parameter on the classification coincidence rate of the network.Then,after a series of experiments with regard to selection of smoothing parameter and training samples,we proposed a method of training sample selection and an optimal interval of smoothing parameter to achieve the high classification coincidence rate in the context of sample normalization.Finally,the application of real seismic data in X area indicated that the PNN has application potential in the case of less well logs and can be used as an alternative in pattern recognition of seismic attributes at the early stage of exploration.

In this paper,we summarized well-logging response characteristics of Sichuan Triassic potash deposits (i.e.polyhalite and potassium-rich brine ).And based on that,we firstly put forward well-logging identification method of potash;and secondly we introduced well-logging cyclostratigraphy prediction method of that,which mainly used to effectively speed up data process;and finally,we used the two methods together to depict distribution of Sichuan Triassic potash deposits.Based on our prediction results,we not only dividied six salting forming periods into four salinity cycles,but pointed out three potash forming periods and four favorable potassium areas.

鄂尔多斯盆地苏里格气田59区块盒八段的主要砂体类型属于典型的河流相沉积，河流相砂体厚度的预测将直接决定后续含气性的预测精度。该段储层总体上表现为低孔、低渗、低丰度、横向非均质性强，即对于该研究区的勘探开发难度较高，风险较大。本文通过对盒八段上、下两小层地震属性的提取、预处理、基于粗集理论分析方法的属性优化，运用模式识别储层预测方法对该研究区盒八段上、下两小层分别进行有利砂体厚度预测。并针对储层预测结果进行评价和检验，以期望获得较高精度的储层预测结果。

本文通过鄂尔多斯盆地苏里格气田59区块盒八上段下段两小层地震属性的提取、预处理及基于粗集理论分析方法的属性优化,运用模式识别储层预测方法对该研究区盒八上段下段两小层分别进行有利砂体厚度预测.并针对储层预测结果进行评价和检验,以期望获得较高精度的储层预测结果.

Potash deposition is the result of final phase of brine sedimentary evolution,it’s a limited distribution and easily soluble mineral,it is difficult to find the features on the ground,therefore the prospecting is extremely tough.

四川盆地陆相碎屑岩油气藏多为低渗透油气藏,虽然油气资源总量丰富,但单井产量普遍较低,因此需要由多井低产向少井高产转变,由低效开发向高效开发转变.盆地主要储层须家河组目前上产的主要手段是水平井技术,面临的主要问题是优质储层发育带和高含气区的寻找.近年来先后出现多口水平井钻井失误,迫切需要利用物探技术指导水平井设计及跟踪评价.地震勘探的储层预测技术可为地质靶点优选提供较为可靠的目标,但很少涉及水平井轨迹设计、地质导向和随钻跟踪等多个重要环节.如何利用地震属性技术提高预测分析的可靠性和实时指导性是问题的核心和关键.通过研究地震属性优选技术的理论和方法,利用SDC敏感属性分析技术优化水平井轨迹设计,四川盆地安岳区块实钻表明,该方法能够有效提高水平井地质导向的成功率和储层钻遇率.

地震勘探的储层预测技术可为地质靶点优选提供较为可靠的目标，但在涉及水平井轨迹设计、地质导向和随钻跟踪等多个环节中，如何利用地震属性技术提高预测分析的可靠性和实时指导是问题的核心和关键。通过研究地震属性优选技术的理论和方法，在川中地区利用地震敏感属性分析技术优化水平井轨迹设计，可以提高水平井地质导向的成功率和储层钻遇率。

By using mathematical tools and computer graphic technology, many seismic attributes can be mapped to low-dimensional data for interpretation,and then improve the efficiency of seismic attribute analysis.Seismic multi-attributes PCA-RGBA color blending is a vision -based attribute analysis method.Firstly,seismic attributes are transformed to low-dimension by Principal Component Analysis (PCA) technology,and the principal components are arranged in terms of eigenvalues from big to small,the first three(or four) principal components are processed by RGBA(Red-Green-Blue-Alpha) color blending to obtain a blending map.Then,combing with the actual geologic data,geologic targets are interpreted by the vision characteristics(regional and abnormally changed of color) on blending map.Conventional seismic attributes of Bohai SZ oilfield were processed by the method;good results have been achieved in aiding for fault recognition and identifying the characteristics of reservoir fluid variation in time-lapse data.

In the course of water flooding in Pohai SZ36-1 oilfield,water saturation became very large in some regions whereas the pressure in other regions dropped significantly resulting in degassing.Time-lapse seismic analysis was implemented to monitor the fluid changes in the oilfield.The difference between conventional time-lapse seismic attribute volumes is usually not suitable for simultaneously monitoring anomalies from water flooding and degassing.Based on reservoir petrophysics observations,we established a wedge model to simulate the time-lapse seismic reflections.We analyzed the spectral responses of seismic reflections to different thickness in areas of water flooding and degassing by performing Fourier transform on the time-lapse seismic data.Then we used spectral decomposition technique to obtain a series of tuning frequency amplitude volume.We conclude that the negative anomalies in low-frequency end are caused by water flooding whereas the positive anomalies in high-frequency end are caused by degassing.

The self-organizing neural network technology,combining with seismic attribute,is often used in the automatic identification of seismic facies,but in practical applications,this method has some problems to solve difficultly,such as the classification and recognition of neural network,the selection of seismic attributes and the resolution of the Clustering self-organizing image in an orderly manner and so on.In this paper,some improvement on Kohonen self-organizing networks is made,and the sensitive attribute analysis techniques are used to solve the problem of the choose of seismic attributes,and finally,combining self-organizing network clustering parameters,using of RBF reservoir parameters,the multi-attribute reservoir prediction accuracy is predicted and improved more effectively.

In the study of seismic reservoir predication,lots of seismic attributes are utilizable,but too many attributes could affect the accuracy of reservoir predication.So,for the sake of improving the accuracy of seismic reservoir predication,this stady utilizes the rough set theory to extract useful attributes and simplify information processing,to sort out significative attributes from seismic attributes.We use a self-organized neural network quantization method that is dependent on the variance of seismic attributes,and optimize seismic attributes by the discernibility matrix attributes frequency reduction algorithm.The example indicates:that this method is very effective,and can delete utmostly redundant seismic attributes.The multiple reservoir parameters are well predicated by significative attributes is very good.