叠前同时反演在吐哈盆地致密油气有利储层预测中的应用

致密油气已成为勘探热点。吐哈盆地温吉桑地区水西沟群发育多套致密砂岩,但含油气较好的相对优质储层厚度较薄,且横向变化快,加之上覆煤层影响及其本身低孔低渗的特点,导致相对优质储层与围岩的地球物理响应特征差异较小。常规储层预测方法及流体检测结果多解性强、精度低,很难满足该区致密砂岩勘探需要。利用地震、钻井、测井等信息,开展了主要反演井横波估算、弹性参数计算及模型正演、敏感参数分析,结果表明,该区含油气层顶表现为Ⅰ类AVO特征,λρ、Vp/Vs交会可以较好区分含油气砂岩。在此基础上,运用高品质三维地震资料,对温吉桑地区进行了AVO属性分析、叠前同时反演,并综合其他预测结果,对该区有利储层及含油气性进行了预测,在实际致密砂岩勘探中取得了较好应用效果。实践表明,叠前反演技术是目前储层预测、含油气检测的有效方法,极大地减少了常规储层反演的多解性。下一步将以这种技术手段为基础,应用于其他致密油气勘探工区。     

射线参数叠前反演技术在浊积岩储层预测中的应用

射线弹性阻抗对角度域的常规弹性阻抗进行了改良,它不像常规弹性阻抗那样把入射角与透射角的比值作为一个常量,而是引入不变的地震射线参数作为常量,更符合弹性波传播的物理规律,也更能真实反映地层中的各类信息。所运用的射线参数道集不需借助速度模型,可直接从原始地震道集转换得到,使转换结果具有确定性,避免了速度建模误差带来的影响。同时,基于射线参数理论的叠前地震反演流程,通过贝叶斯理论开展稀疏脉冲反演及利用约束搜索法对储层叠前弹性参数进行求取。相比于角度域的叠前弹性参数反演,该方法的反演结果具有相对更高的纵横向分辨率及更好的客观性。将其应用于利津洼陷沙三下—沙四上浊积岩储层预测,应用效果表明该方法在预测横向变化较大的复杂储层方面具有可靠性和准确性,其预测结果 对研究区的勘探开发工作具有重要的借鉴意义。     

川中地区茅口组白云岩储层地震预测

川中地区白云岩主要为晶粒白云岩亚类,包括泥质粉晶白云岩、中-细晶白云岩、泥质细晶白云岩、细晶灰质白云岩等小类,大多零星分布。通过对川中地区茅口组白云岩储层的储集空间特征和地球物理特征的表述,明确白云岩储层的地震响应特征,即白云岩储层地震振幅增强,纯的白云岩储层比灰岩波阻抗值高,从而利用地震属性和地震反演方法预测白云岩储层在平面上呈块状、条带状分布,与研究区内断裂和裂缝分布区域和走向一致。     

地震多属性分析在含油气储层预测中的应用

油气勘探领域中广泛应用的三维地震数据蕴涵着丰富的地质信息。地震多属性分析技术就是对地震数据进行各种数学、物理变换,提取出包含其中的岩性、含油气性、物性信息,为舍油气储层预测提供技术支持。叙述了地震属性的分类、多属性分析技术的应用方法,给出了多属性分析技术的应用实例,指出了多属性分析技术在含油气储层预测中的作用。     

基于地质统计学反演的薄层预测技术在南海东部A油田的应用

薄层预测一直是地质、地球物理界的一大难题。地震波的分辨能力极限近似于四分之一波长,对于厚度小于四分之一波长的地层,传统的预测方法是无能为力的。地质统计学反演,在岩石物理可行性的基础上,可以通过随机反演得到高分辨率的结果 ,达到对薄层的预测效果。A油田是中国海上某探区第一个采用钻采平台开发的边际油田。油田构造幅度低,油层多且薄,非均质性强,连续性差,储量规模小。本文以A油田为例,阐述了研究区L30Up层储层预测的难点和存在的问题,提出了针对性的地质统计学反演预测技术,精细解释出该层砂体分布,实现了薄层的高精度预测。该预测结果 经过油田的实际应用,极大地指导了新钻生产井的实施,砂岩钻遇率近100%,水平井取得了良好的开发效果,大大提高了边际油田的经济效益。     

畸变条件下井控速度模型优化方法与应用

海上油气田在勘探阶段,尤其在受到上覆速度异常体影响的情况下,叠前深度偏移地震资料速度模型往往存在精度不足的问题,时间偏移剖面反映的构造形态就不再准确,使其下伏地层构造形态发生畸变.本文以南海东部珠江口盆地H油田为例,首先利用叠前反演精细刻画高速灰岩内幕速度,然后基于已钻井统计闭合差,利用恒定旅行时层析速度反演迭代优化速...     

薄层生物礁滩灰岩储层一体化表征研究——以珠江口盆地A油田为例

以珠江口盆地A油田生物礁滩储层为研究对象,针对开发初期钻井资料少而储层厚度薄、横向变化快、孔隙度-渗透率关系多样的情况,提出了一种基于地震、测井、岩心资料的一体化储层表征技术.通过对储层沉积特征、岩性、物性进行精细描述,明确了生物礁滩储层类型;以地质模式为指导,采用相控地质统计学反演精细预测了薄层甜点储层的井间展布,为...     

地震沉积学的内涵与外延

从地震沉积学的产生至今,它已成为继地震地层学、层序地层学之后的1门新兴边缘交叉学科。提出地震岩石学、地震储层学和地震地貌学构成了地震沉积学的三大核心内容。叙述了在沉积学理论指导下,利用地球物理技术揭示岩相、储层和沉积体系的展布形态及其内部结构,进而实现对有利区带预测的研究思路。指出,它比地震地层学和层序地层学的研究更加精细和准确。     

洞穴型碳酸盐岩储层识别及预测技术

塔里木轮古西地区奥陶系灰岩是古岩溶发育的有利场所。根据储层岩电特征的差异,将轮古西地区碳酸盐岩储层划分为孔洞型、裂缝型、裂缝—孔洞型及洞穴型四种类型。其中,裂缝型以及孔洞型储层很难形成稳定的产能;裂缝—孔洞型储层既有较好的储集能力,又有良好的渗透性能,能够形成稳定的产能,是本区一类重要产层(如Lg9、Lg42井);洞穴型储层具有最好的储集能力和渗透性能,是本区最有价值的储层类型。根据地球物理正演模拟和过井地震剖面分析,洞穴型储层在地震剖面上表现为"串珠状"强反射的特点。通过地震属性敏感性分析,优选出了平均反射强度、均方根振幅、平均能量、能量半衰时斜率以及相干属性。利用这些地震属性参数,通过模糊神经网络的方法,对碳酸盐岩储层进行综合预测与评价。多属性综合预测结果与实际钻井资料十分吻合,该区岩溶作用与断裂的关系非常密切,多呈条带状分布,展布方向与断裂发育方向具有比较高的一致性。     

复杂地质条件下薄储层高分辨率精细预测技术研究——以珠江口盆地X油田为例

珠江口盆地X油田是典型高泥质疏松砂岩油田,虽然储层整体连续性较好,但砂体具有单层薄、砂岩疏松、泥质含量高和非均质性强等特点.在这种复杂地质条件背景下,波阻抗反演可靠性低,储层空间展布规律难以掌握.随着油田开发进程的加快,对储层预测的要求越来越精细,而常规反演方法很难满足高精度储层预测的要求.针对这些难题,本文利用神经网...     

Hot water flooding and cold water flooding in heavy oil reservoirs

Murphy Oil’s Seal asset in the Peace River Oil Sands region in North West Alberta, Canada, has a remarkable capacity for implementation of Enhanced Oil Recovery (EOR) processes. Some wells in Seal have shown strong historical production due to excellent permeability and the foamy solution gas drive mechanism. The reservoir characteristics in this portion are highly variable where determining the most suitable EOR method is very challenging. Two of the potential EOR candidates which have been evaluated for this portion include conventional cold waterflooding and hot waterflooding. Results of EOR core analysis, logging, detailed seismic and petrophysical studies, and history-matched reservoir simulation and forecast indicate that cold waterflooding outperforms hot waterflooding. This is surprising as hot waterflooding is believed to outperform cold waterflooding in most medium heavy oil reservoirs.     

Shale gas reservoir characterisation: A typical case in the southern Sichuan Basin of China

The Lower Silurian Longmaxi Formation is an organic-rich (black) mudrock that is widely considered to be a potential shale gas reservoir in the southern Sichuan Basin (the Yangtze plate) in Southwest China. A case study is presented to characterise the shale gas reservoir using a workflow to evaluate its characteristics. A typical characterisation of a gas shale reservoir was determined using basset sample analysis (geochemical, petrographical, mineralogical, and petrophysical) through a series of tests. The results show that the Lower Silurian Longmaxi Formation shale reservoir is characterised by organic geochemistry and mineralogical, petrophysical and gas adsorption. Analysis of the data demonstrates that the reservoir properties of the rock in this region are rich and that the bottom group of the Longmaxi Formation has the greatest potential for gas production due to higher thermal maturity, total organic carbon (TOC) enrichment, better porosity and improved fracture potential. These results will provide a basis for further evaluation of the hydrocarbon potential of the Longmaxi Formation shale in the Sichuan Basin and for identifying areas with exploration potential.     

裂缝性潜山油藏储集层识别与预测

埕北30太古宇为裂缝性潜山油藏,裂缝是其主要的储集空间和流体运移通道。裂缝性储集层识别与预测是这类油藏描述的核心和关键。为此,岩心、录井、测井、试井结合建立了储集层划分和分类标准,识别描述了井点储集层的分布;以井点储集层为约束条件,应用三维地震反演技术,预测了埕北30潜山太古宇油藏的储集层三维空间展布。     

速度建模技术在吉林探区复杂构造成像处理中的应用

针对吉林探区伊通盆地西北缘地震资料的复杂构造成像,叠前深度偏移是目前比较有效的成像技术,而复杂构造准确成像面临的核心问题不是偏移算法,而是速度模型的准确性问题。速度模型的准确与否直接关系到偏移成像的效果。结合伊通盆地复杂构造成像的实践经验,提出了速度建模思路和关键技术。经过叠前时间偏移、时间域层位解释这两步工作,建立了均方根速度场和时间域的构造模型;在时间域速度体上抽取和计算层速度,经过平滑、编辑、速度约束等一系列处理后进行时深转换,形成深度域初始深度-层速度模型。通过采用沿层剩余速度拾取与叠前深度偏移迭代来对初始模型进行更新,形成了精度更高的叠前深度偏移的深度-速度模型。通过采用井资料约束、地质构造模式约束、地震成像约束等手段对速度模型进行了有效质量控制。与叠前时间偏移剖面对比,叠前深度偏移剖面波组特征更清楚,层间内幕更清晰,西北缘成像精度更高。     

向家坝水库蓄水前库区天然地震本底特征

研究水电站工程所在区域及水库影响区天然地震背景水平,不仅可对水库诱发性地震的危险性进行前期评价, 而且可为蓄水后的诱发地震活动监测提供可靠的鉴别依据。通过分析向家坝水电工程所在的区域地震背景、构造特征、应力场及中强震震源机制特点,并结合相关地震监测数据和向家坝水库地震监测台网的地震监测数据,探讨了向家坝水库影响区及周边区域范围天然地震年频次、月频次、地震震级-频度关系及震源机制特征,可为向家坝水库蓄水后库区水库地震监测、预测及地震类型判识提供参考。     

频谱分解技术定量求取储层厚度方法研究

老油田的勘探已经进入到以薄层为主的隐蔽油藏勘探阶段，厚度作为储层的主要特征参数，反映了物性、物源方向、沉积环境、沉积背景等。因此，储层厚度的精确求取关系到油藏勘探的成败。有关薄储层厚度的计算已有不少成熟的方法，如反射振幅法、反演法等，这些方法因适应性低或过程复杂，没有形成一种完全有效的厚度求取方法。近几年发展起来的频谱分解技术，是一种无井约束的高分辨率储层预测技术，也是储层厚度求取方法的补充和完善。频谱分解技术根据薄层调谐原理，利用全频段的信息，将地震数据从时间域转换到频率域，突出高频信号，相对提高地震资料的主频，从而提高了对薄储层厚度的识别能力。从基于离散傅里叶变换和小波变换两种原理的频谱分解技术出发，对两种方法处理的结果进行分析，并基于处理得到的不同参数，如调谐频率、最大能量等，提出了三种求取储层厚度的方法，并在实际应用中取得了较好效果。     

High-temperature fluid flows in the dachny field of the Mutnovsky hydrothermal system, Russia

High-temperature hydrothermal reservoirs typically have complex structures that are difficult to characterize even after a number of wells have been drilled. The most effective methods for characterizing the flow regime within a reservoir are: (1) three-dimensional mapping of the geological structure, temperature, pressure and permeability; (2) interpretation of tracer tests and reservoir fluid chemistry; and (3) flow test data analysis. (It is assumed that the petrophysical parameters of the various lithologic units have been determined on the basis of core and geophysical log data.) When these methods are applied to the Dachny reservoir of the Mutnovsky geothermal system, they yield the distribution in the field of lithologies, temperatures, phases and pressures, as well as the characteristics of the high-temperature fluid circulation (natural state initial and boundary conditions for the associated heat transfer problem).     

Shear-wave splitting as a tool for the characterization of geothermal fractured reservoirs: lessons learned

We review our experience with the construction of models of subsurface fracturing in geothermal fields by the inversion of shear-wave splitting (SWS) observations from natural and induced seismic events recorded by local arrays of three-component digital seismometers. SWS is a phenomenon whereby shear seismic waves split into two as a result of the mechanical anisotropy created in an otherwise isotropic rock by aligned micro-fractures. The two split waves travel at different speeds, and the polarization of the faster wave is usually parallel to crack orientation. The time delay between the two split S-waves is proportional to the number of cracks per unit volume.Success in the inversion of SWS data hinges on the assumption that the observed SWS is due solely to the mechanical anisotropy induced by aligned cracks and micro-cracks in an otherwise isotropic matrix. The presence of lithologic anisotropy and/or strong heterogeneity in the reservoir rock limits the resolvability of the method. However, despite the large amount of data and diversity of geologic settings we have studied so far, the above assumption has been found to be reliable. In practice, stability and resolution in the inversion of SWS data are the issues of utmost importance since both are critically dependent on the distribution of the two SWS measured parameters (polarization and time delay) around each seismic sensor.In this paper we discuss a few lessons we have learned as to the value of SWS for geothermal exploration, its limitations and potential extensions, from nearly a decade of practice.     

Porosity and permeability prediction from well logs using an adaptive neuro-fuzzy inference system in a naturally fractured gas-condensate reservoir

Interpretation of petrophysical log is one of the most useful and important tools in petroleum geology. Well logs help determine the physical characteristics of a reservoir, such as lithology, porosity, permeability, producer regions and their depth and thickness, and also differentiating between oil and gas and water in a reservoir and defining hydrocarbon reserve. A continual record of the physical characteristics of rocks in various depths is called well logs. Petrophysical logs usually include gamma ray, spontaneous potential, resistance, density, neutron, nuclear magnetic resonance, and sonic. The purpose of this study is to determine those characteristics of the reservoir that cannot be specified directly through present measuring well logs, using an intelligent problem-solving system of neuro-fuzzy. In this study, porosity and permeability are determined as two of the most important reservoir characteristics having much influence on reservoir understanding, reservoir reserve, and capability of reservoir production. The system of determining reservoir characteristics (neuro-fuzzy) was tested on collected well log data from oil and gas fields in the south of Iran. The most important result obtained in this study is that if all data influencing one of the reservoir characteristics are presented to the neuro-fuzzy system, then this system will be an excellent model with low error for determining all of the complex characteristics of the reservoir. These produced intelligent systems predict porosity and permeability completely on training and testing data and the correlation coefficient is near 1 and normalized mean square error is near to zero. Engineers and researchers can predict the reservoir characteristics with very good precision by these intelligent systems. The results of this study prove that a neuro-fuzzy intelligent system is a very powerful tool for determining permeability and porosity at the wells of a naturally fractured gas condensate reservoir. It is the first time to predict porosity and permeability from well logs using adaptive neuro-fuzzy inference system in a naturally fractured gas-condensate reservoir in Khuff Formation (Kangan and Upper Dalan Formation), which is a heterogeneous formation with a wide range of permeability and porosity in the Middle East; this method predicts permeability and porosity precisely in this heterogeneous formation. The other novelty of this study is the choice of appropriate input parameters to determine porosity and permeability precisely. Because of the heterogeneous condition of naturally fractured gas-condensate reservoirs in Khuff Formation, the appropriate input parameters cause the system to train optimally and thus increase the system ability to estimate the output in various conditions precisely.