Detailed reservoir inversion addressing geological problems in reservoir development

垦71开发区块的主要产油层系为新近系的馆陶组河流相和古近系的东营组三角洲相地层,具有被断层复杂化的多油水系统。该区井网密集,储层厚度薄,横向关系复杂,储层与非储层速度差异小,后续开发方案调整所要求的精细储层描述难度较大。本文展示了以精细储层描述为目标的地震约束反演实例,利用多井地震、地质标定,结合三维构造解释成果,建立三维多断层精细约束地质模型。在此基础上,采用随机地震反演方法,进行了波阻抗、岩性等多种地层参数反演,较好地解决了该区薄互层储层精细描述问题,证实了精细储层反演技术在油田开发中的重要作用。     

基于反假频基函数的井震匹配快速算法

河流相储层、碳酸盐岩孔缝洞密集带等地层,地震同相轴不具有层状特征,可形象地称为具有复杂网状特征,利用层状地层模型难以精细描述井外地层.传统的宽带约束反演,其目标函数一般含有一个层状的初始模型、离散光滑约束、稀疏约束和线性拟合函数约束.利用这种层状的初始模型进行井震匹配,其高频信息主要来源于井资料插值得到的层状地层模型,井之间地震信息很难起作用.不仅如此,其计算量较大,且不易调节层状信息与稀疏约束反映的地震资料中的非层状信息的比例,难以适应的复杂网状地层的井震信息融合.本文利用地震资料构造反假频基函数以描述复杂网状地层,避免了对层状模型的过分依赖,使得联合重建的信息在井旁与测井资料拟合较好,在井外能充分地反映地震资料所含非层状信息.本文利用大庆下白垩地层扶余油层的河流相资料进行实验,证实了方法的快速和有效性.     

井震联合断层识别技术及其应用——以杏北油田为例

小断层是影响开发后期高含水油田剩余油挖潜和注采调整关系的主要控制因素之一,因此,油田开发后期对精确描述井间小断层的位置和走向提出了更高的要求.单纯依靠测井资料描述断层和储层特征具有一定的局限性,尤其在断点组合、井间断层延伸长度、平面位置以及井间砂体连续性等方面,还需要井震结合进一步深入研究.采用更真实融入噪音的地震正演模型分析小断层识别的可行性出发,利用三维化、多手段的断层分级识别描述集成技术,形成"先大后小、先简单到复杂、三维可视组合、井震精细甄别"的小断层精细构造解释方法,通过三维地震像素处理和地震属性处理技术,进行断层增强和放大等手段,结合井震标定精细甄别确定小断层,有效解决密井网油田复杂地质条件的断层问题.对寻找断层附近、井间砂体及过渡带外扩潜力,进一步深化高含水老油田精细油藏描述,精细剩余油挖潜具有重要意义.     

喇嘛甸油田3D3C地震资料的应用探索

喇嘛甸油田在高含水开发后期剩余油精细挖潜研究中,面临密井网条件下厚度2m以上砂体的边界识别问题,常规纵波地震解释存在多解性,引入转换波信息可增加地震解释的可靠性.基于喇嘛甸工区三维三分量地震数据,本文由测井曲线分析得出,密度、LR和MR能够很好地反映储层岩性变化.在纵波层位约束下的纵横波层位精细匹配解释基础上,由纵横波联合反演求出这3种属性数据体.井-震结合,应用属性切片技术得到不同小层的砂体平面展布,与钻井绘制的沉积相图具有较高的一致性,并在气藏描述、砂体识别、河道刻画中见到显著的地质效果.该研究为喇嘛甸油田主力油层剩余油挖潜研究提供了基础资料,同时展示了该区扶余油层的良好勘探前景.     

波阻抗反演预测奥陶系灰岩顶部含隔水性

地震反演技术是岩性地震勘探的重要手段之一,其中地震井约束波阻抗反演技术是利用钻孔测井数据高的纵向分辨率,对井旁地震资料进行井约束反演,然后在此基础上对井间地震资料进行反演,推断地层岩性在空间上的变化情况.本文根据新驿煤矿首采区的三维地震和测井资料,利用波阻抗反演技术预测该区奥陶系灰岩顶部的含隔水性,并依据波阻抗值的大小圈定含水层中的可能富水区.     

地震波形分类技术在河流相储层研究中的应用

地震波形分类技术虽然比较成熟,但是在河流相油田开发中的应用还不完善。河流相储层侧向突变快,非均质性强,在井距较大的条件下井间储层非均质性预测便成了关键性难题。通过测井和三维地震结合,采用地震波形分类技术,以测井相型为桥梁,将地震波形特征转化为储层叠置特征,提高了储层平面分布非均质性描述的精细程度。研究表明：河流相储层典型的4种测井相型,对应4种地震响应特征,分别代表河流相储层的不同叠合模式。利用地震波形分类技术可以有效地预测不同叠合模式砂体的分布。通过在已开发油田的应用,为油田的调整、挖潜提供支持。     

辽河坳陷大民屯凹陷三台子地区下第三系碎屑岩储层预测研究

针对目标区下第三系储层横向变化快,沉积特征不明显等特点,为提高滚动探井、开发井的钻探成功率,本文研制形成一套具有针对性的油气藏勘探预测研究方法.即运用多井约束波阻抗地震反演方法来预测砂体展布,同时对生成的地震波阻抗反演数据体中的振幅、厚度等暗含的有效信息进行提取,对砂体的分布范围及厚度变化进行准确而精细的描述和预测,最终依据此法优选有利相带,部署井位,取得了很好的勘探开发效果.     

地质统计学反演在储层预测中的应用——以WSD区P_3w为例

研究区发育砂砾岩储层,一般由多期扇体叠置而成,横向变化快,非均质性强,因而给储层预测带来了困难.常规约束稀疏脉冲反演受制于地震频带,纵向分辨率不足,无法清晰地展现出砂体的叠置关系,因此本文确定了以地质统计学反演为核心的储层预测方法重点对WSD区P_3w_1~2段油组进行储层预测综合研究.首先对基础数据(井数据、地震数据、储层敏感参数、合成记录、子波等)进行质控,确定储层预测的可行性,其次根据井资料、约束稀疏脉冲反演结果以及对该区沉积模式与岩性展布的初步认识,求取地质统计学反演参数,针对研究对象进行地质统计学反演和储层砂体厚度的计算,同时对两种反演方法进行对比分析,然后对反演结果、储层砂体平面分布情况以及沉积微相平面展布特征进行综合研究分析,最后从平面、剖面两个角度对反演结果分别进行定性、定量地验证分析,证明反演结果的可靠性.研究表明地质统计学反演在WSD区具有一定的适用性,为该区有利区的优选和评价提供了依据,同时也为冲积扇储层预测提供了有效的方法.     

松辽盆地丰乐地区营城组火山岩储层预测

徐家围子断陷的主要产气层段为营城组火山岩段地层,所以研究该地层对寻找天然气藏具有非常重要的意义.本文利用丰乐地区的高分辨率地震资料、岩石物性资料、测井资料对下白垩统营城组火山岩进行构造精细解释,火山岩地震相及岩相分析从而进行储层预测,利用均方根振幅、瞬时频率能量及瞬时吸收系数能量等地震属性分析结合地震反演技术精细刻画火山岩储层.结果认为：均方根振幅、瞬时频率能量和瞬时吸收系数能量能较好预测火山岩相和储层物性;有利储层在反演剖面上表现为中低阻抗特征,中等密度;较好储层主要分布在工区的西部与中部.     

叠后地震属性分析在油气田勘探开发中的应用

地震属性分析技术一直是地震特殊处理和解释的主要研究内容.随着油气勘探开发的发展,地震属性分析技术已经成为油藏地球物理研究的核心内容,是勘探地震与开发地震之间纽带.本文针对鄂尔多斯盆地的低幅度构造、低孔隙、低渗透率、致密性隐蔽油气藏的特点,综合应用相干数据体分析、地震相自动分类定性识别砂体厚度、地震振幅属性分析、频谱分解、多井约束的储层叠后反演等叠后属性分析技术,探索了一套适合该区油气特征的储层横向预测及油气识别模式.为该区油气勘探开发,储量计算提供可靠依据.同时也为隐蔽性油气藏的勘探开发积累了经验.     

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.     

鄂北上古生界隐蔽气藏地震储层预测技术

鄂尔多斯盆地北部上古生界陆相致密碎屑岩隐蔽气藏具有低孔、低渗、致密、薄互层、多层叠置和非均质性强的特点.其勘探开发一直是世界性难题.针对该区储层特点，展开了以三维地震为核心的储层综合地球物理预测技术研究，开发应用了储层岩石物理分析、地震属性优化、随机模拟反演、地震吸收衰减、地震频谱分析、地震波正演模拟、地震相划分和AVO等多项技术，形成了一整套适合该地区油气地质特征的方法技术系列，有效提高了储层预测的精度，提高了勘探开发井位部署的成功率.     

叠前反演和地震吸收技术在复杂天然气藏地震预测中的应用

针对某复杂断块天然气目标储层,在岩石物理分析的指导下,综合利用地质、地震、测井等资料,提出了一套面向复杂天然气藏的叠前地震预测技术.首先基于地震岩石物理分析得到的初始横波信息,采用叠前贝叶斯非线性三参数反演得到了井旁控制点处精确纵横波速度和密度信息,然后通过叠前/叠后联合反演技术实现了面向目标的弹性阻抗体反演及含气储层敏感参数直接提取,最后结合小波变换时频谱分析的方法从叠前地震资料中估算地层吸收参数值,提高天然气藏识别精度.实际应用表明,综合各种叠前地震预测技术,可以大大提高对复杂天然气藏的识别精度,降低勘探风险.     

Integrating facies-based Bayesian inversion and supervised machine learning for petro-facies characterization in the Snadd Formation of the Goliat Field,south-western Barents Sea

Seismic petro-facies characterization in low net-to-gross reservoirs with poor reservoir properties such as the Snadd Formation in the Goliat field requires a multidisciplinary approach. This is especially important when the elastic properties of the desired petro-facies significantly overlap. Pore fluid corrected endmember sand and shale depth trends have been used to generate stochastic forward models for different lithology and fluid combinations in order to assess the degree of separation of different petro-facies. Subsequently, a spectral decomposition and blending of selected frequency volumes reveal some seismic fluvial geomorphological features. We then jointly inverted for impedance and facies within a Bayesian framework using facies-dependent rock physics depth trends as input. The results from the inversion are then integrated into a supervised machine learning neural network for effective porosity discrimination. Probability density functions derived from stochastic forward modelling of endmember depth trends show a decreasing seismic fluid discrimination with depth. Spectral decomposition and blending of selected frequencies reveal a dominant NNE trend compared to the regional SE–NW pro-gradational trend, and a local E–W trend potentially related to fault activity at branches of the Troms-Finnmark Fault Complex. The facies-based inversion captures the main reservoir facies within the limits of the seismic bandwidth. Meanwhile the effective porosity predictions from the multilayer feed forward neural network are consistent with the inverted facies model, and can be used to qualitatively highlight the cleanest regions within the inverted facies model. A combination of facies-based inversion and neural network improves the seismic reservoir delineation of the Snadd Formation in the Goliat Field.     

A study and application of high-resolution methods for reef reservoir identification

Reefs represent a special type of carbonate trap that plays a key role in the migration, accumulation, and formation of a reservoir. They have commonly been the targets of exploration and development. However, reefs have complex interior structures and easily grow as thin, interbedded geological frames with reef microfacies that include the cap, core, and base of the reef. Because of the inherent drawbacks of seismic signals, including their low frequencies and narrow bandwidths, it is difficult to accurately identify reef reservoirs. Fortunately, the seismic frequency, phase, energy, waveform and other dynamic and geometrical properties can be used to compensate for the energy, expand the frequency bandwidth, and decompose and reconstruct the wavelet to obtain high-resolution seismic data. These data can highlight certain seismic responses of reefs, including boundary reflections, dome-shaped reflections from the reef outline, strong reflections from the reef cap, reflections from the reef bottom, and onlap reflections from the reef flanks. Some impedance response regularities, such as the lower impedance of the reef cap relative to the reef core and biodetritus beach and the fluctuating impedance of the reef-flat complex, are observed by combining log data with geological and high-resolution seismic data for a reef reservoir inversion. These methods were applied to the Changxing Formation in the Yuanba Gas Field. Good prediction results were obtained with a high consistency between the log and seismic data in a comparative analysis with the original seismic data and well logs.     

多属性融合技术在苏14井区的应用

In this study area the geological conditions are complicated and the effective sandstone is very heterogeneous. The sandstones are thin and lateral and vertical variations are large. We introduce multi-attribute fusion technology based on pre-stack seismic data, pre-stack P- and S-wave inversion results, and post-stack attributes. This method not only can keep the fluid information contained in pre-stack seismic data but also make use of the high SNR characteristics of post-stack data. First, we use a one-step recursive method to get the optimal attribute combination from a number of attributes. Second, we use a probabilistic neural network method to train the nonlinear relationship between log curves and seismic attributes and then use the trained samples to find the natural gamma ray distribution in the Su-14 well block and improve the resolution of seismic data. Finally, we predict the effective reservoir distribution in the Su-14 well block.     

基于统计岩石物理模型的各向异性页岩储层参数反演

提出了各向异性页岩储层统计岩石物理反演方法.通过统计岩石物理模型建立储层物性参数与弹性参数的定量关系,使用测井数据及井中岩石物理反演结果作为先验信息,将地震阻抗数据定量解释为储层物性参数、各向异性参数的空间分布.反演过程在贝叶斯框架下求得储层参数的后验概率密度函数,并从中得到参数的最优估计值及其不确定性的定量描述.在此过程中综合考虑了岩石物理模型对复杂地下介质的描述偏差和地震数据中噪声对反演不确定性的影响.在求取最大后验概率过程中使用模拟退火优化粒子群算法以提高收敛速度和计算准确性.将统计岩石物理技术应用于龙马溪组页岩气储层,得到储层泥质含量、压实指数、孔隙度、裂缝密度等物性,以及各向异性参数的空间分布及相应的不确定性估计,为页岩气储层的定量描述提供依据.     

基于线性预测倒谱系数的地震相分析

本文借鉴语音识别技术中的线性预测倒谱系数(LPCC系数)特征参数提取方法对地震数据进行分解,这种方法的优点是:可以获得将子波和反射系数信息分离的地震语音特征参数,对地质现象边界具有较好的描述能力,使我们可以从不同维度更细致地观察隐藏在地震数据中的地质特征.理论模型分析表明,基于LPCC系数的地震分析具有较高的地震相划分能力.实际地震资料应用表明,LPCC系数对储层特征的描述比常规三瞬属性更为细致,不同阶次LPCC系数在描述储层不同特征时也保持了内在的联系.采用K均值聚类方法对提取的12阶和24阶LPCC系数进行聚类分析,聚类结果与目的层段古地形较为吻合,较好地反映了研究区的断裂、礁滩相带、深水扇和储层的分布特征,说明在地震相分析中采用LPCC系数作为特征参数是可行和有效的.     

地震岩相识别概率表征方法

储层岩相分布信息是油藏表征的重要参数,基于地震资料开展储层岩相识别通常具有较强的不确定性.传统方法仅获取唯一确定的岩相分布信息,无法解析反演结果的不确定性,增加了油藏评价的风险.本文引入基于概率统计的多步骤反演方法开展地震岩相识别,通过在其各个环节建立输入与输出参量的统计关系,然后融合各环节概率统计信息构建地震数据与储层岩相的条件概率关系以反演岩相分布概率信息.与传统方法相比,文中方法通过概率统计关系表征了地震岩相识别各个环节中地球物理响应关系的不确定性,并通过融合各环节概率信息实现了不确定性传递的数值模拟,最终反演的岩相概率信息能够客观准确地反映地震岩相识别结果的不确定性,为油藏评价及储层建模提供了重要参考信息.模型数据和实际资料应用验证了方法的有效性.