基于应力敏感常数的致密砂岩储层应力敏感性评价研究(10)

利用渗流应力耦合拟三轴实验系统,针对华北、青海等区块共34块天然砂岩岩心展开应力敏感性测试实验.基于应力敏感常数对实验结果进行拟合,获得砂岩储层岩心应力敏感常数与渗透率的变化关系.结果显示,岩心应力敏感常数能够表征砂岩储层岩心渗透率的应力敏感特性,应力敏感常数越大,砂岩渗透率应力敏感性越强,且砂岩应力敏感常数随岩心渗透率的增大而减小.     

深层高压低渗砂岩储层微观孔喉特征参数研究

为研究深层高压低渗砂岩储层微观孔隙结构特征参数,应用常规压汞技术对东濮凹陷文东油田深层高压低渗砂岩储层样品进行分析测试.定义退汞饱和度参数(SE),提出以退汞效率(EW)与退汞饱和度(SE)的乘积作为储层微观孔隙结构分类评价指标(E).研究结果表明:储层孔隙、喉道类型多样,储层微观孔隙结构非均质性强;储层物性与孔喉分选系数呈正相关关系,这与常规中、高渗储层相反,与常规低渗、特低渗储层一致;储层物性与SE,EW的相关性差,这一点与常规储层不同;文东油田深层高压低渗砂岩储层微观孔隙结构可分为3类,Ⅰ类储层E大于11.21,Ⅱ类储层E在2.90～11.21之间,Ⅲ类储层E小于2.90.从微观孔隙结构特征参数角度来看,深层高压低渗砂岩储层既不同于常规中、高渗储层,又不同于常规低渗储层,属于非常规储层范畴.影响开发效果的主要因素是储层非均质性.     

断层相关裂缝的发育模式及分布预测——以东濮凹陷沙三段为例

低孔低渗砂岩是东濮凹陷沙三段的主要储层,储层中与断层相关的裂缝十分发育,是形成优质储层甜点区的重要因素。为阐明断层相关裂缝的发育模式和分布规律,利用大量岩芯、地震、成像测井及实验分析等资料,对东濮凹陷沙三段储层中的断层相关裂缝基本特征、主控因素、发育模式及油气地质意义进行了研究,并利用断地比定量预测了裂缝的分布规律。结果表明:东濮凹陷沙三段断层相关裂缝主要走向为NNE向、NE向,形成于沙三晚期和东营晚期,与主控断层在力学性质和形成时间上具有一致性;断层相关裂缝的发育程度随着距断层距离的增大呈指数递减,且断层上盘裂缝线密度要高于下盘;裂缝的发育程度和裂缝带宽度与主控断层的规模成正相关关系;断层相关裂缝可显著提高储层的物性和含油性;断地比大于10的区域为裂缝发育区;采用断地比参数法预测出5个致密储层裂缝甜点区。     

异常高压对储层溶蚀及成岩阶段的影响机理——以渤中凹陷西北次凹为例

综合运用薄片、岩心、粒度、SEM等分析方法,研究了渤中地区异常高压对溶蚀作用和成岩阶段的影响机理,发现了一种新溶蚀机制:不同地层压力条件下,颗粒溶蚀会优先发育于特定粒级的砂岩.揭示了异常高压会促进其界面内外储层的溶蚀作用,提供了其抑制有机质成熟和黏土矿物转化的有力证据.异常高压区→常压区,溶蚀强度变化不大,其包络线呈缓坡状.研究表明:异常高压区,颗粒溶蚀优先发育于粗粒级砂岩,平均溶蚀增孔率为32.0%,3 200~3 400m段平均Ro值为0.56%,混层中蒙脱石质量分数为20.0%;2 950m深度以下常压区,颗粒溶蚀优先发育于细粒级砂岩,平均溶蚀增孔率为34.2%,平均Ro值为0.58%,混层中蒙脱石质量分数为14.6%;2 950m深度以上常压区,溶蚀与粒级无相关性,平均溶蚀增孔率为26.5%.紧邻异常高压的常压区,溶孔发育的深度段大于430m.总结异常高压对溶蚀作用的影响模式,结合新溶蚀机制,为寻找次生溶孔有利区提供理论依据.     

Model building for Chang-8 low permeability sandstone reservoir in the Yanchang formation of the Xifeng oil field

In order to build a model for the Chang-8 low permeability sandstone reservoir in the Yanchang formation of the Xifeng oil field, we studied sedlimentation and diagenesis of sandstone and analyzed major factors controlling this low permeability reser-voir. By doing so, we have made clear that the spatial distribution of reservoir attribute parameters is controlled by the spatial dis-tribution of various kinds of sandstone bodies. By taking advantage of many coring wells and high quality logging data, we used regression analysis for a single well with geological conditions as constraints, to build the interpretation model for logging data and to calculate attribute parameters for a single well, which ensured accuracy of the 1-D vertical model. On this basis, we built a litho-facies model to replace the sedimentary facies model. In addition, we also built a porosity model by using a sequential Gaussian simulation with the lithofacies model as the constraint. In the end, we built a permeability model by using Markov-Bayes simula-tion, with the porosity attribute as the covariate. The results show that the permeability model reflects very well the relative differ-ences between low permeability values, which is of great importance for locating high permeability zones and forecasting zones favorable for exploration and exploitation.