基于岩石物理相分类确定特低渗透油层有效厚度——以安塞油田沿河湾地区长6特低渗透储层评价为例

针对陕北斜坡中部特低渗透储层受沉积环境、成岩作用、构造等因素影响,储层储集性能和渗流结构差异较大,测井响应复杂的特点,利用岩石物理相分类确定特低渗透油层有效厚度。通过安塞油田沿河湾地区长6储层各类测井、岩芯和试油资料,研究评价岩石物理相的多种信息、划分方法及其分类评价,建立不同类别岩石物理相油层有效厚度参数下限的差异、特征及评价标准,分析了微电极曲线提取特低渗储层岩石物理相背景特征。并以实例分析提取特低渗透储层岩石物理相分类表征参数,实现了将非均质、非线性问题转化为相对均质、线性问题解决,提高了测井精细解释油层有效厚度的精度和效果。     

东河储层敏感性参数与测井的响应关系

在岩心分析的基础上。分析和提取塔中“东河砂岩”储层的11个主要敏感性参数，同时利用常规测井方法可获得的岩石骨架。泥质含量。粘土矿物和物性参数等敏感性参数。然后在油田储层敏感性流动实验资料的基础上。运用逐步回归方法建立储层敏感性与测井资料的解释模型。对实际井进行处理分析。结果表明，由测井解释得到的储层敏感性结合与研究区的敏感性流动实验基本一致。     

壳幔岩石矿物物性参数整合

为理解壳-幔物质密度、速度结构的地质意义,在历时数年的地质-地球物理综合解释工作成果的基础上,整合壳-幔岩石矿物物性参数资料,供地学工作者加深对中国大陆深层区域构造格架的认识.一般来说,随深度增加地壳、地幔和外核岩石矿物密度和速度有正相关的变化规律.从曲线图、柱状图、表格、构造层、岩石矿物多个角度阐述壳-幔岩石矿物物性参数的变化规律,提供综合解释地球内部物质组成的地质-地球物理基础资料.     

松辽盆地外围中生代盆地的岩石物性 ——以突泉盆地和乌兰盖盆地为例

系统采集突泉盆地、乌兰盖盆地的地层岩石物性标本,测试了密度、磁化率、剩磁及电阻率。研究发现,由于含油气盆地勘探目标层往往含泥质、炭质成分较多,采用常规的物性标本测试方法很难获得这类地层岩石真实的物性参数,特别是电性参数。通过井旁测井模型与岩心及测井曲线对比,证明井旁测深技术是一种获取地层电性参数的有效方法。结合前人物性研究资料与本次工作结果,系统地整理了突泉盆地、乌兰盖盆地地层岩石物性参数,建立了主要地层-物性柱,分析了盆地的地层岩石物性特征,为在该地区开展非震地球物理测量提供了基础性资料。     

基于贝叶斯判定的反演域岩石物理交会

岩石物理交会图中有时需要多个弹性参数来识别各交会对象,并且交会对象之间也会互相重叠,因此岩石物理交会分析是一个多总体判定问题。常规岩石物理交会的弹性参数都来自于测井数据,但是测井数据和反演的弹性参数并不完全一致,二者之间存在一定的误差。因此,这里提出了基于贝叶斯判定的反演域岩石物理交会方法,不仅能解决各交会对象多参数判定问题,还能指导反演域弹性参数的物性解释。     

岩石物理建模技术在致密砂岩储层预测中的应用——以鄂尔多斯盆地北部H区块为例

岩石物理建模是叠前反演前重要的基础工作。以致密砂岩储层为目标,剖析了岩石物理建模涉及的主要过程。将测井评价与岩石物理建模视为迭代过程,测井评价提供岩石物理建模的输入数据,岩石物理建模控制测井评价参数的质量。在测井参数具有较高质量的情况下,优选岩石物理建模方法,优化关键参数,较为准确地预测了横波速度等弹性参数,为叠前弹性参数反演提供了可靠的基础数据。利用岩石物理解释模板对叠前反演纵横波速度体进行了解释,提高了致密砂岩气层的识别精度,在鄂尔多斯盆地取得了较好的应用效果。     

基于测井资料的岩石物理学正反演技术

综合利用测井、地震、地质资料,以多口井岩石物理分析为基础,结合地震解释层位,通过对测井曲线储层岩石参数的分析解释,建立油藏模型,进行弹性波动方程正演模拟,并综合分析储层与油气地球物理响应特征,快速进行有色反演,其结果不仅可以对地震解释成果进行验证,而且可以对基于地震的波阻抗反演结果进行比较.本方法在苏家屯地区的应用得到较好的效果.     

一种基于物性数据的深部三维地质建模方法

地质采样信息不足是制约深部三维地质建模的重要因素, 深部物性探测数据由于其易于获取而能够有效形成可视化模型.结合这一特点, 在地质调查项目工作中探索出了一种基于物性探测数据的三维地质建模方法.它首先利用岩石样品物性测量实验数据提取出物性参数及其对应地质属性的映射关系, 然后将不同地球物理方法所获取到的物性数据进行综合建模并解释, 最后将解释后的可视化模型转换为地质三维模型.实践证明, 该方法能够针对性地解决项目中的一些深部地质三维建模问题.      

陕北东部延长气区上古生界储层测井属性特征分析

通过岩心分析、测井资料综合解释和试井分析等手段,研究了陕北东部气区上古生界砂岩储层测井属性特征。研究区上古生界储层存在石英砂岩与长石砂岩两种类型,对应测井属性特征及其物性参数之间的关系、测井解释模型等均存在明显差异。针对岩屑砂岩渗透率约束困难的问题,提出了应用声波-自然电位曲线重叠图幅度以及不同探测范围电阻率曲线之间的幅度关系,综合反映砂岩储层渗透率及其变化特征的方法,进而分析了储层含气性的测井属性特征。     

基于岩石物理模版的碳酸盐岩含气储层定量解释

笔者针对碳酸盐岩储层勘探开发面临的储层预测与流体识别难度大的问题,基于岩石物理理论,提出了一种对碳酸盐岩含气储层进行定量解释的方法:首先,综合碳酸盐岩地质和测井信息,以岩石物理理论为依据,实现了适用的碳酸盐岩岩石物理模型的构建;然后基于岩石物理模型优选出对孔隙度、含气饱和度较为敏感的参数λρ和μρ,构建能够反映储层孔隙度与含气饱和度的碳酸盐岩岩石物理模版;最后,将岩石物理模版与工区反演提取的参数相结合,确定储层分布范围,并对储层范围内孔隙度和含气饱和度数值进行了定量解释。实例分析表明,基于该模版定量解释得到的孔隙度和含气饱和度与实际地层孔隙度及产气结果基本一致,经验证碳酸盐岩岩石物理模版的可靠性和适用性较好。     

HD油田水平井测井储层参数解释研究

水平井测井解释,主要体现在测井仪器响应的理论模拟以及各向异性的实验研究方面,远未达到实际水平井测井储层参数解释应用的深度和要求。在对比分析HD油田水平井段与对应直井层段测井响应特征基础上,这里针对水平井段测井曲线进行了校正,提出使用直井取芯物性数据刻度水平井段测井曲线,来建立适应于水平井段井眼和曲线特征的孔隙度、渗透率神经网络解释模型,进而对整个研究区水平井段进行测井储层参数解释和评价。通过用直井岩芯物性对测井解释结果的检验表明,水平井测井储层参数解释精度得到了明显改善和提高,能够较好地满足三维地质建模的井属性参数精度要求。     

核磁测井资料在复杂储层评价中的应用效果

用自主研发的P型核磁数据处理软件对实际的复杂地层核磁测井资料进行处理,并结合常规测井资料和其他成像测井资料进行解释评价,改进了对地层流体性质评价的可靠性。结果表明,核磁测井资料的应用对低阻、低差异油气藏,低孔、低渗油气藏的识别,复杂岩性储层的评价均能起到明显的改善作用。     

Reservoir parameters determination using artificial neural networks: Ras Fanar field, Gulf of Suez, Egypt

Ras Fanar field is one of the largest oil-bearing carbonate reservoirs in the Gulf of Suez. The field produces from the Middle Miocene Nullipore carbonate reservoir, which consists mainly of algal-rich dolomite and dolomitic limestone rocks, and range in thickness between 400 and 980 ft. All porosity types within the Nullipore rocks have been modified by diagenetic processes such as dolomitization, leaching, and cementation; hence, the difficulty arise in the accurate determination of certain petrophysical parameters, such as porosity and permeability, using logging data only. In this study, artificial neural networks (ANN) are used to estimate and predict the most important petrophysical parameters of Nullipore reservoir based on well logging data and available core plug analyses. The different petrophysical parameters are first calculated from conventional logging and measured core analyses. It is found that pore spaces are uniform all over the reservoirs (17–23%), while hydrocarbon content constitutes more than 55% and represented mainly by oil with little saturations of secondary gasses. A regular regression analysis is carried out over the calculated and measured parameters, especially porosity and permeability. Fair to good correlation (R <65%) is recognized between both types of datasets. A predictive ANN module is applied using a simple forward backpropagation technique using the information gathered from the conventional and measured analyses. The predicted petrophysical parameters are found to be much more accurate if compared with the parameters calculated from conventional logging analyses. The statistics of the predicted parameters relative to the measured data, show lower sum error (<0.17%) and higher correlation coefficient (R >80%) indicating that good matching and correlation is achieved between the measured and predicted parameters. This well-learned artificial neural network can be further applied as a predictive module in other wells in Ras Fanar field where core data are unavailable.     

大庆油田北部河流相储层沉积微相与水淹特征

大庆油田北部发育砂质辫状河道、曲流河道、高弯度分流河道和低弯度分流河道等砂体类型。以井间可对比的单一河流沉积物为研究单元 ,从河流相储层成因特征入手 ,应用油田开发后期密井网资料开展各类河流砂体细分微相研究。重点阐述了各类砂体的规模、宽厚比、发育的主要微相、物性特征及层内非均质特点。应用不同时期密闭取心检查井资料、测井水淹解释、生产动态测试资料 ,结合不同砂体和微相的非均质特点研究了不同微相的水淹变化特征。研究结果表明 ,不同阶段各类砂体层内及平面水淹变化特征不同 ,高含水后期剩余油主要分布于河间薄层砂、部分决口水道和废弃河道微相中 ,低弯度分流河道砂体中剩余油相对发育     

重、磁、电联合反演在银额盆地定量解释中的应用

依据大地电磁测深连续二维反演结果初步确定构造与地层,通过综合分析区内物性特征给定地层与岩石的物性参数,建立正演地质一地球物理初始模型,在地质、钻井、地震资料约束下进行重、磁、电联合反演,不断修改地质一地球物理模型和大地电磁测深连续二维反演结果,使重、磁正演数据和实测数据拟合度达到最佳。在银额盆地构造与地层解释中的应用结果表明,重、磁、电联合反演可以有效克服单方法解释的多解性,准确识别地质层位,大大提高定量解释的精度。     

A genetic meta-algorithm-assisted inversion approach: hydrogeological study for the determination of volumetric rock properties and matrix and fluid parameters in unsaturated formations

An evolutionary inversion approach is suggested for the interpretation of nuclear and resistivity logs measured by direct-push tools in shallow unsaturated sediments. The efficiency of formation evaluation is improved by estimating simultaneously (1) the petrophysical properties that vary rapidly along a drill hole with depth and (2) the zone parameters that can be treated as constant, in one inversion procedure. In the workflow, the fractional volumes of water, air, matrix and clay are estimated in adjacent depths by linearized inversion, whereas the clay and matrix properties are updated using a float-encoded genetic meta-algorithm. The proposed inversion method provides an objective estimate of the zone parameters that appear in the tool response equations applied to solve the forward problem, which can significantly increase the reliability of the petrophysical model as opposed to setting these parameters arbitrarily. The global optimization meta-algorithm not only assures the best fit between the measured and calculated data but also gives a reliable solution, practically independent of the initial model, as laboratory data are unnecessary in the inversion procedure. The feasibility test uses engineering geophysical sounding logs observed in an unsaturated loessy-sandy formation in Hungary. The multi-borehole extension of the inversion technique is developed to determine the petrophysical properties and their estimation errors along a profile of drill holes. The genetic meta-algorithmic inversion method is recommended for hydrogeophysical logging applications of various kinds to automatically extract the volumetric ratios of rock and fluid constituents as well as the most important zone parameters in a reliable inversion procedure.     

核磁共振T2谱反演影响因素

为了进一步改进完善核磁共振T2谱反演算法,利用核磁共振正演模拟信号,研究回波间隔、原始回波采集数量、信噪比等因素对反演结果的影响,同时在不同信噪比下对最优化T2谱反演算法、奇异值分解反演算法进行比较分析。研究表明:采集弛豫信号时,回波间隔大小的设置与回波数量、组分大小相匹配,才能得到最佳的反演效果;信噪比越高反演效果越好,当信噪比大于25时反演谱与构造谱基本吻合;在反演不同信噪比理论回波数据时,最优化反演方法对信噪比要求不高,奇异值分解反演方法对信噪比要求高,而奇异值分解反演速度较快。     

蒙古国苏赫巴托尔—乌兰巴托—达兰扎达嘎德剖面岩石物性基本特征

为了深入认识与理解南北地震带北段和蒙古中部大陆强震孕育与发生的深部构造环境,在南北纵贯蒙古国全境的苏赫巴托尔—乌兰巴托—达兰扎达嘎德剖面上开展了大规模重磁测量及岩石样品采集。笔者系统地分析与总结了剖面上585块岩石样品的密度、磁化率和天然剩余磁化强度等物性参数。结果表明,整个剖面以火成岩为主,沉积岩为辅,仅有少量变质岩出露,且各岩石大类、类和亚类以及各年代地层之间,均存在着不同程度的密度与磁性差异。火成岩磁化率和天然剩余磁化强度分别具有3~5个数量级差异,密度变化范围较大;而沉积岩磁化率和天然剩余磁化强度相对较弱。一方面,物性差异为利用重磁测量开展剖面地壳结构的反演与研究奠定了基础;另一方面,这些物性参数为相关地球物理测量数据的反演与解释提供了重要约束。     

Deep low-salinity groundwater in sedimentary basins: petrophysical methods from a case study in Somalia

Oil and gas exploration data for the Northern Somalia and Horn of Africa region have been utilised for deep freshwater resources exploration. This unique geophysical and petrophysical dataset is integrated into a regional hydrogeological study and it allows for targeting of unmapped and unknown deep aquifers that would otherwise be beyond the conventional reach of the groundwater sector. The database includes open-hole well logging surveys (resistivity, gamma ray, and spontaneous potential), mud logging and drill stem test (DST) data. The results from the data analysis are combined with the geological interpretation to assess the freshwater potential of each mapped hydrostratigraphic unit in the region. The results highlight the presence of several previously unknown deep low-to-medium salinity (<9  g/L total dissolved solids (TDS)) aquifers within the Jurassic to Eocene units, including the Adigrat, Hamanlei, Gabredarre, Gumburo, Jessoma, and Auradu formations. With tested intervals of water containing less than 3.8 g/L total dissolved solids, the Auradu, Jessoma, and Gumburo formations are the most promising deep freshwater aquifers. Additional results on the analysis of groundwater parameters, such as hydraulic head, provide the basis for further groundwater modelling. The results show that the method has good potential, in particular for use in drought-stricken areas of arid regions. Ultimately, the results from this region have global significance, as the method can provide a new means of boosting fresh groundwater resources in water-poor regions, and supporting sustainable development and utilization of the resources in the medium and long term.

     

PNN在煤田随钻测井岩性判识应用研究

介绍了PNN方法原理及其算法训练学习过程,详细阐述了网络识别岩性参数的选取、岩性识别模型的建立过程.通过对比研究PNN与其他6种岩性识别方法,分析相同条件下预测结果,得到不同识别方法的优劣性.经研究发现,PNN概率神经网络方法在生产应用中效果更佳、训练识别用时最短.利用人工智能神经网络对测井数据进行自动解释分析,可满足随钻测井时效性及快速解释处理的地质导向需求.