带先验约束的碎屑岩储层物性参数反演方法

对弹性参数进行转换而获取物性参数的方法,适用范围有限且预测精度不高.本文基于岩石物理理论,提出一种带先验约束的碎屑岩储层物性参数预测方法,为储层预测、流体识别、储层描述、储量估计、烃类开发方案设计提供重要参数.应用岩石物理理论和物性参数先验信息建立了带先验约束的目标函数;并利用最优化方法对反演目标函数进行优化求解,对储层的物性及孔隙度和泥质含量进行同步迭代反演.经过正演数据及实际地震资料的测试应用,其结果表明本文方法反演得到的孔隙度和泥质含量为储层综合预测提供了愈加丰富的评价指标,同时孔隙度和泥质含量数据的联合应用对减小储层预测的多解性和降低勘探开发的风险有重要意义.     

用地震资料预测孔隙度的广义协克里格法

在Doyen工作的基础上,本文对简单协克里格法作了一些改进,提出了用地震资料预测孔隙度的广义协克里格法,详细推导证明了其基本公式.并分别用3种方法预测了松辽盆地宋站地区扶余油层含气砂岩储层的孔隙度分布.井孔处的交互检验证明,广义协克里格法比线性回归法和简单协克里格法预测孔隙度的均方根误差分别小40％和30％以上.     

岩石物理驱动的储层物性参数非线性地震反演方法

叠前地震反演和岩石物理反演分别是获取弹性参数和物性参数的重要手段,两者结合有助于实现储层参数预测并精细刻画储层特征.储层物性参数的反演依赖于岩石物理模型,在进行物性参数反演时可以将复杂的岩石物理模型做泰勒展开,进而得到其一阶或高阶的近似表达式,然而这会降低模型的精确性并增加反演的误差.为了提高储层物性参数反演的稳定性和准确性,本文以碎屑岩储层为例,提出了岩石物理驱动的储层物性参数非线性地震反演方法.首先,基于贝叶斯框架和高斯分布约束条件,从叠前地震数据中实现纵、横波速度及密度等弹性参数的反演.其次,通过碎屑岩岩石物理模型建立起弹性参数与物性参数之间的联系.最后,利用粒子群算法进行全局寻优获得较为准确的孔隙度、泥质含量和含水饱和度等物性参数.合成数据和实际资料测试结果验证了所提方法的可行性和准确性,反演结果与测井数据吻合较好,可有效指示含气储层区域,本文方法在储层预测和评价方面具有广泛的应用前景.     

花岗岩储层测井解释方法研究及应用

针对花岗岩储层非均质性特点,采用了三重孔隙结构解释模型对其进行解释评价.该模型的储集空间包括基质、裂缝和孔洞三部分,随着储集空间的变化,可将该模型转化为孔隙型储层解释模型、孔洞型储层解释模型和裂缝型储层解释模型,这样就可适应多种储层性质的解释需要.由此利用常规测井资料计算了花岗岩储层的总孔隙度、基质孔隙度、孔洞孔隙度和裂缝孔隙度,利用岩心和电成像测井对裂缝孔隙度和孔洞孔隙度的计算结果进行标定和验证,并根据这些参数划分储层类型,计算含油气饱和度,划分油气水层.含油气饱和度的计算是采用反映储层孔隙结构变化的孔隙结构指数变m值,使阿尔奇公式适用于非均质花岗岩储层含油气饱和度的计算.应用上述测井解释方法,对目标井进行精细处理解释和综合评价,取得了很好的应用效果.     

基于地质统计学反演的缝洞储集体物性定量评价

塔河油田奥体系发育孔、洞、缝等多种储集体,空间非均质性极强.定量刻画不同类型储集体及其物性在三维空间的展布一直是该类油藏开发的技术难题.本文采用一种基于马蒙算法的地质统计学反演方法,结合地质、测井先验信息,获得表征缝洞储集体类型及反映物性参数的纵波阻抗数据.再通过寻找波阻抗与储集体孔隙度之间的统计关系,利用一种"云变换"手段,协模拟得到缝洞储集体等效孔隙度数据体,以此从物性角度定量评价缝洞储集体的空间非均质性.与常规方法对比说明,该方法评价结果与井点解释物性有较高的吻合程度,井间趋势上更加尊重地震响应特征,是一种地震数据主控的储集体物性评价方法,对于强非均质性油藏的物性发育规律评价具有指导意义.     

受孔隙形态影响的碳酸盐岩孔隙度反演

深层碳酸盐岩往往具有孔隙度低,孔隙形态复杂的特点.传统孔隙度反演方法仅考虑孔隙空间大小对地层岩石物性的影响,未考虑孔隙形态对岩石弹性特征的作用,在反演深层碳酸盐岩孔隙度时往往存在偏差.孔隙形态的影响可以利用含有孔隙纵横比的等效介质模型表示;通过引入DEM模型对声波孔隙度方程进行改进,定量体现孔隙形态的影响.将改进的声波反演与中子反演、密度反演相结合,得到井区受孔隙形态影响的地层孔隙度.首先利用中子、密度和纵波时差反演得到地层孔隙度初值;进而通过DEM公式获得孔隙纵横比初值;最后综合利用中子测井、密度测井和声波测井数据,实现受孔隙纵横比影响的地层孔隙度反演.应用该方法反演了西南某地MX19井深层白云岩储层的孔隙度,通过与岩心孔隙度测量数据进行对比,验证了反演结果的准确性.     

基于地震属性约简的深度学习储层物性参数预测:以莺歌海盆地乐东区为例

储层物性参数作为描述储层特性、储层建模和流体模式的重要指标,其准确估算可以为储层预测提供有力参考依据,但传统储层物性参数反演方法无法兼顾反演精度及空间连续性。针对上述问题,本文引入地震属性作为深度学习算法输入,针对地震属性之间存在的信息冗余特征,利用随机森林-递归消除法对地震属性进行约简预处理,最终建立一种基于地震属性约简的储层物性参数预测方法。实际数据测试结果表明,地震属性约简的深度学习储层物性参数预测结果具有良好的精度及横向分辨率,证实本文方法的有效性。      

基于弹性阻抗的孔隙结构与物性参数非线性同步反演

碳酸盐岩储层具有复杂的孔隙结构,其显著影响了岩石的弹性和地震响应特征,也影响物性参数预测精度.现有的岩石物理反演方法主要是针对碎屑岩储层,反演的对象主要是孔隙度和饱和度,采用的数据主要是弹性参数,使用的算法主要是线性近似法,缺少针对碳酸盐岩储层的孔隙结构与物性参数反演方法.针对这些问题,文章首次提出了一种基于弹性阻抗的适用于碳酸盐岩复杂孔隙储层的孔隙结构和物性参数贝叶斯非线性同步反演新方法,该方法把多孔介质岩石物理模型、Gassmann方程、AVO理论、贝叶斯理论和非线性反演算法结合起来,实现复杂孔隙储层的孔隙结构和物性参数的定量同步预测.模型正演揭示,孔隙结构参数即孔隙扁度对AVO反射系数和弹性阻抗的贡献度仅次于孔隙度,远远大于含水饱和度的影响.实际资料应用表明,文章提出的从叠前道集中直接反演物性参数和等效孔隙扁度新方法可以准确预测储层孔隙度和饱和度及其空间展布,并可评价有效储层的孔隙结构.     

砂泥岩储层孔隙度和含水饱和度同步反演

基于岩石物理和地震反演理论,提出了一种同步反演储层孔隙度和含水饱和度的方法.以岩石物理为基础,建立了砂泥岩储层物性和弹性参数之间定量的关系-Simon模型,以贝叶斯理论为手段,结合不同类型的砂泥岩储层,建立了多信息联合约束的物性参数反演目标函数,并通过蒙特卡罗和遗传算法相结合的思路求解该目标函数,最终得到孔隙度和含水饱和度的同步反演结果.将该方法应用于河道砂和砂砾岩两种不同的砂泥岩储层中,孔隙度和含水饱和度数据的联合应用,进一步减少了储层预测的多解性,为石油地质综合研究提供了更加丰富准确的基础数据.     

基于广义S变换的分频反演在南海深水区油气勘探中的应用

通过建立地质模型寻找地震属性参数来预测含油气性的方法不断发展并取得了较好的效果.但是对于海相沉积环境下受构造控制的复合油气藏的研究,常规的油气预测方法存在局限性、多解性,还无法准确识别有效含气层,因此探索适合该地区的油气预测技术有着重大意义.为此本文首先对物性参数敏感性孔隙度及渗透率作了定量分析,依据分频数据体对流体信息的高灵敏度,利用改进的广义S变换结合AVF关系同时在井数据约束下对孔隙度和渗透率进行了分频反演,获得了孔隙度和渗透率反演剖面,准确有效地刻画了含气储层.实验证明,分频反演方法对南海深水区珠江组储层进行精细预测,克服了常规反演在少井无井区无法有效预测的问题,且分频反演预测油气结果比常规方法吻合度要高,提高了深水区油气勘探的精度.     

基于Markov链模型的相控随机建模

Markov链模型在储层随机建模中的作用越来越受到关注,但其多用于类型属性(岩相、沉积相、沉积亚相等)的模拟,对于连续型属性(孔隙度、渗透率、含油气饱和度等)的模拟还比较困难.本文提出用Markov链模型相控建模方法模拟连续型属性的思路,即首先用Markov链模型模拟出类型属性,其次在类型属性约束下模拟出连续型属性,从而解决连续型属性不能产生突变边界的问题.最后应用此方法进行了模拟实验,模拟结果显示不同岩相中孔隙度差异较大,而同种岩相中孔隙度变化较小,证明了此方法的可靠性和适用性.     

联合多点地质统计学与序贯高斯模拟的随机反演方法

岩相和储层物性参数是油藏表征的重要参数,地震反演是储层表征和油气藏勘探开发的重要手段.随机地震反演通常基于地质统计学理论,能够对不同类型的信息源进行综合,建立具有较高分辨率的储层模型,因而得到广泛关注.其中,概率扰动方法是一种高效的迭代随机反演策略,它能综合考虑多种约束信息,且只需要较少的迭代次数即可获得反演结果.在概率扰动的优化反演策略中,本文有效的联合多点地质统计学与序贯高斯模拟,并结合统计岩石物理理论实现随机反演.首先,通过多点地质统计学随机模拟,获得一系列等可能的岩相模型,扰动更新初始岩相模型后利用相控序贯高斯模拟建立多个储层物性参数模型;然后通过统计岩石物理理论,计算相应的弹性参数;最后,正演得到合成地震记录并与实际地震数据对比,通过概率扰动方法进行迭代,直到获得满足给定误差要求的反演结果.利用多点地质统计学,能够更好地表征储层空间特征.相控序贯高斯模拟的应用,能够有效反映不同岩相中储层物性参数的分布.提出的方法可在较少的迭代次数内同时获得具有较高分辨率的岩相和物性参数反演结果,模型测试和实际数据应用验证了方法的可行性和有效性.     

The formation mechanism of high-quality dolomite reservoir in the deep of Puguang Gas Field

The discovery of Puguang Gas Field provides the exploration of China deep marine carbonate rock with important references.In Puguang Gas Field,the dolomite reservoirs discovered in the deep are the best in the present of China,which present big thickness and wide-range distribution,and develop abundant secondary porosity.The researches show that Puguang Gas Field bears the characteristics of early gas-filling time,deep burial,high matured organic matter and long-term interaction of hydrocarbon(oil and gas)-water-rock(carbonate reservoir).The developments of secondary pores in this area are affected by multiple diagenesis and their formation mechanisms are complicated.Through the research on depositional environment,sedimentary facies and reservoir porosity characters of Changxing and Feixianguan Formations,it is thought that high-quality dolomite reservoirs of Puguang Gas Field form on the favorable sedimentary facies belts,which are the integrate result affected by several factors including superficial corrosion,burial corrosion,overpressure and tectonic movement,among which burial corrosion of TSR to reservoir and overpressure formed by thermal evolution of organic matter have great effect on the formation of secondary porosity of Changxing and Feixianguan Formations.     

The formation mechanism of high-quality dolomite reservoir in the deep of Puguang Gas Field

The discovery of Puguang Gas Field provides the exploration of China deep marine carbonate rock with important references. In Puguang Gas Field, the dolomite reservoirs discovered in the deep are the best in the present of China, which present big thickness and wide-range distribution, and develop abundant secondary porosity. The researches show that Puguang Gas Field bears the characteristics of early gas-filling time, deep burial, high matured organic matter and long-term interaction of hydrocarbon (oil and gas)-water-rock (carbonate reservoir). The developments of secondary pores in this area are affected by multiple diagenesis and their formation mechanisms are complicated. Through the research on depositional environment, sedimentary facies and reservoir porosity characters of Changxing and Feixianguan Formations, it is thought that high-quality dolomite reservoirs of Puguang Gas Field form on the favorable sedimentary facies belts, which are the integrate result affected by several factors including superficial corrosion, burial corrosion, overpressure and tectonic movement, among which burial corrosion of TSR to reservoir and overpressure formed by thermal evolution of organic matter have great effect on the formation of secondary porosity of Changxing and Feixianguan Formations.     

二维叠后地震数据的平稳随机介质参数估计

随机介质参数估计是随机介质理论应用于地震勘探的关键.本文提出了一种从二维叠后地震数据中估计平稳随机介质参数的方法.文中阐述了二维叠后地震数据与随机介质波阻抗模型的关系,以及随机介质自相关函数参数的估计原理和方法,并结合实例详细介绍了应用功率谱法进行随机介质参数估计的具体步骤;通过多个二维理论模型的估计试验,验证了方法的可行性和正确性;还对实际地震数据进行了随机介质参数的估计试验,结果表明,随机介质参数可以为三角洲沉积相的进一步划分提供参考,反映了该方法有较好的应用前景.相比前人的研究,本文所提出的随机介质参数估计方法是一种真正的二维算法,特别是能给出自相关角度θ的估计,这种基于功率谱的估计方法具有直观且高效率的优点,但也存在着误差较大的问题,需要进一步的改进和完善.     

Deep learning long short-term memory combined with discrete element method for porosity prediction in gravel-bed rivers

The porosity of gravel riverbed material often is an essential parameter to estimate the sediment transport rate, groundwater-river flow interaction, river ecosystem, and fluvial geomorphology. Current methods of porosity estimation are time-consuming in simulation. To evaluate the relation between porosity and grain size distribution (GSD), this study proposed a hybrid model of deep learning Long Short-Term Memory (LSTM) combined with the Discrete Element Method (DEM). The DEM is applied to model the packing pattern of gravel-bed structure and fine sediment infiltration processes in three-dimensional (3D) space. The combined approaches for porosity calculation enable the porosity to be determined through real time images, fast labeling to be applied, and validation to be done. DEM outputs based on the porosity dataset were utilized to develop the deep learning LSTM model for predicting bed porosity based on the GSD. The simulation results validated with the experimental data then segregated into 800 cross sections along the vertical direction of gravel pack. Two DEM packing cases, i.e., clogging and penetration are tested to predict the porosity. The LSTM model performance measures for porosity estimation along the z-direction are the coefficient of determination (R²), root mean squared error (RMSE), and mean absolute error (MAE) with values of 0.99, 0.01, and 0.01 respectively, which is better than the values obtained for the Clogging case which are 0.71, 0.14, and 0.03, respectively. The use of the LSTM in combination with the DEM model yields satisfactory results in a less complex gravel pack DEM setup, suggesting that it could be a viable alternative to minimize the simulation time and provide a robust tool for gravel riverbed porosity prediction. The simulated results showed that the hybrid model of the LSTM combined with the DEM is reliable and accurate in porosity prediction in gravel-bed river test samples.     

物探方法在临清坳陷某区块山西组沉积相分析中的应用

临清坳陷某区块缺少岩心、薄片、录井及常规测井资料。为了研究其目的层山西组沉积相对储层的控制作用,指导非常规天然气的高效开发。通过利用较少的录井岩性统计资料,结合地震物探方法,分析地震剖面特征、优选地震均方根振幅及相对波阻抗属性,确定该区目的层沉积相。通过上述地震属性研究的物探方法,确定山西组储层为河控浅水三角洲相和碳酸盐台地相,主要发育分流河道、分流间湾及局限台地沉积微相。其中三角洲相是主要的沉积相类型,分流河道是有效储层发育的主要微相类型。      

相控地震多属性预测技术在南堡凹陷碳酸盐岩储层的应用

碳酸盐岩储层是南堡凹陷潜山重要的勘探领域,由于受到构造、岩性等多种因素的影响,常规储层预测难度大.本文提出相控储层预测方法,通过井上精细对比,确定南堡凹陷潜山碳酸盐岩储层的优势发育段,结合古地貌确定其有利分布相带,在有利相带约束下,优选出地震平均能量属性、曲率体属性、蚂蚁体属性和谱分解属性,融合这些属性,综合预测储层发育区.通过与已钻井的对比,储层发育区与钻井资料吻合度高,证实了该方法的有效性,同时为南堡凹陷潜山的展开勘探提供了参考.     

Statistical facies classification from multiple seismic attributes: comparison between Bayesian classification and expectation–maximization method and application in petrophysical inversion

We present here a comparison between two statistical methods for facies classifications: Bayesian classification and expectation–maximization method. The classification can be performed using multiple seismic attributes and can be extended from well logs to three‐dimensional volumes. In this work, we propose, for both methods, a sensitivity study to investigate the impact of the choice of seismic attributes used to condition the classification. In the second part, we integrate the facies classification in a Bayesian inversion setting for the estimation of continuous rock properties, such as porosity and lithological fractions, from the same set of seismic attributes. The advantage of the expectation–maximization method is that this algorithm does not require a training dataset, which is instead required in a traditional Bayesian classifier and still provides similar results. We show the application, comparison, and analysis of these methods in a real case study in the North Sea, where eight sedimentological facies have been defined. The facies classification is computed at the well location and compared with the sedimentological profile and then extended to the 3D reservoir model using up to 14 seismic attributes.     

Interrelations Between Thermal Conductivity and Other Physical Properties of Rocks: Experimental Data

— A new non-contact and non-destructive optical scanning instrument provided a large number of high-precision measurements of thermal conductivity tensor components in samples of sedimentary and impact rocks, as well as new insights into interrelations between thermal conductivity and other physical properties. More than 800 core samples (dry and fluid-saturated) of sedimentary rocks from different Russian oil-gas deposits and impact rocks from the well “Nördlingen 1973” drilled in the Ries impact structure (Germany) were studied using optical scanning technology. It was established that the thermal conductivity parallel to the stratification is more informative for petrophysical investigations than the thermal conductivity perpendicular to the layering. Different approaches were developed to estimate porosity, permeability, pore space geometry, and matrix thermal conductivity with a combination of thermal conductivity measurements in dry and fluid-saturated samples and mathematical modelling. These approaches allow prediction of the rock porosity and permeability and their spatial distribution along a well using thermal conductivity measurements performed with the optical scanning instrument directly applied to cores. Conditions and constraints for using Lichtenecker-Asaad's theoretical model for the estimation of porosity and thermal conductivity of sedimentary rocks were determined. A correlation between thermal conductivity and acoustic velocity, porosity, density, and electric resistivity of impact rocks was found for different rock types. New relationships between permeability, electrical and thermal conductivity found for sedimentary rocks are described.