利用常规测井资料评价碳酸盐岩裂缝-孔隙性储层

对于碳酸盐岩裂缝-孔隙型储层,在没有成像等测井资料的井中,裂缝的准确识别及裂缝孔隙度的计算主要应用常规测井资料。常用的方法是利用深浅电阻率测井识别裂缝和进行裂缝孔隙度的计算。但是由于浅侧向测井响应受井径、泥浆及泥质等多方面因素的影响,所得结果的不确定性较高,实际应用效果并不理想。提出了一种改进的双电阻率方法,即用重构的基质地层电阻率曲线代替浅侧向电阻率曲线,与深侧向电阻率曲线一起进行裂缝识别及裂缝孔隙度的计算,计算结果与取心及试油资料符合较好,该方法在哈萨克斯坦碳酸盐岩地区应用效果良好。

压扭走滑作用对深水碳酸盐岩储层的改造——以Sinjar隆起西翼为例

通过分析阿拉伯板块北缘Sinjar隆起西翼始新统Chilou组—渐新统Jaddala组构造特征和油藏分布的关系,认为深部压扭走滑作用改造了浅层致密的深水碳酸盐岩沉积,使之成为良好的油气储层。这种改造和控制作用主要表现在2个方面:一是压扭走滑作用控制了裂缝异常发育带和裂缝性储层的形成与分布;二是压扭走滑作用产生的同沉积局部斜列褶皱隆起遭受大气淡水与海水混合水的溶蚀作用,从而改善和提高了致密沉积物的储集能力。这一实例表明,致密的深水碳酸盐岩沉积经改造后具有良好的勘探潜力,在油气藏勘探和开发过程中应重视类似局部构造。

Pore structure effect on reservoir electrical properties and well logging evaluation

The reservoir pore structure controls the reservoir quality and resistivity response of hydrocarbon-bearing zones and thus, critically affects logging interpretation. We use petrophysical data in three types of reservoir with different pore structure characteristics to show that the complexity of pore structure had a significant effect on the effective porosity and permeability regardless of geological factors responsible for the formation of pore structure. Moreover,, the distribution and content of conductive fluids in the reservoir varies dramatically owing to pore structure differences, which also induces resistivity variations in reservoir rocks. Hence, the origin of low-resistivity hydrocarbon-bearing zones, except for those with conductive matrix and mud filtrate invasion, is attributed to the complexity of the pore structures. Consequently, reservoir-specific evaluation models, parameters, and criteria should be chosen for resistivity log interpretation to make a reliable evaluation of reservoir quality and fluids.

Effects of micrite microtextures on the elastic and petrophysical properties of carbonate reservoirs

Apparent differences in sedimentation and diagenesis exist between carbonate reservoirs in different areas and affect their petrophysical and elastic properties.To elucidate the relevant mechanism,we study and analyze the characteristics of rock microstructure and elastic properties of carbonates and their variation regularity using 89 carbonate samples from the different areas The results show that the overall variation regularities of the physical and elastic properties of the carbonate rocks are controlled by the microtextures of the microcrystalline calcite,whereas the traditional classification of rock-and pore-structures is no longer applicable.The micrite microtextures can be divided,with respect to their morphological features,into porous micrite,compact micrite,and tight micrite.As the micrites evolves from the first to the last type,crystal boundaries are observed with increasingly close coalescence,the micritic intercrystalline porosity and pore-throat radius gradually decrease;meanwhile,the rigidity of the calcite microcrystalline particle boundary and elastic homogeneity are enhanced.As a result,the seismic elastic characteristics,such as permeability and velocity of samples,show a general trend of decreasing with the increase of porosity.For low-porosity rock samples(φ<5%)dominated by tight micrite,the micritic pores have limited contributions to porosity and permeability and the micrite elastic properties are similar to those of the rock matrix.In such cases,the macroscopic physical and elastic properties are more susceptible to the formation of cracks and dissolution pores,but these features are controlled by the pore structure.The pore aspect ratio can be used as a good indication of pore types.The bulk modulus aspect ratio for dissolution pores is greater than 0.2,whereas that of the intergranular pores ranges from 0.1 to 0.2.The porous and compact micrites are observed to have a bulk modulus aspect ratio less than 0.1,whereas the ratio of the tight micrite approaches 0.2。

Estimation of reservoir porosity using probabilistic neural network and seismic attributes

Porosity is one of the most important properties of oil and gas reservoirs. The porosity data that come from well log are only available at well points. It is necessary to use other method to estimate reservoir porosity.Seismic data contain abundant lithological information. Because there are inherent correlations between reservoir property and seismic data,it is possible to estimate reservoir porosity by using seismic data and attributes.Probabilistic neural network is a powerful tool to extract mathematical relation between two data sets. It has been used to extract the mathematical relation between porosity and seismic attributes. Firstly,a seismic impedance volume is calculated by seismic inversion. Secondly,several appropriate seismic attributes are extracted by using multi-regression analysis. Then a probabilistic neural network model is trained to obtain a mathematical relation between porosity and seismic attributes. Finally,this trained probabilistic neural network model is implemented to calculate a porosity data volume. This methodology could be utilized to find advantageous areas at the early stage of exploration. It is also helpful for the establishment of a reservoir model at the stage of reservoir development.

Application of multi-component joint inversion in oil and gas exploration:A case study of reservoir and gas saturation prediction of the Xujiahe formation in the PLN area of the central Sichuan Basin

Multi-component seismic exploration is an important technique in the utilization of P-waves and converted S-waves for oil and gas exploration.It has unique advantages in the structural imaging of gas zones,reservoir prediction,lithology,and gas-water identifi cation,and the development direction and degree of fractures.Multi-component joint inversion is one of the most important steps in multi-component exploration.In this paper,starting from the basic principle of multi-component joint inversion,the diff erences between the method and single P-wave inversion are introduced.Next,the technique is applied to the PLN area of the Sichuan Basin,and the P-wave impedance,S-wave impedance,and density are obtained based on multi-component joint inversion.Through the velocity and lithology,porosity,and gas saturation fi tting formulas,prediction results are calculated,and the results are analyzed.Finally,multi-component joint inversion and single P-wave inversion are compared in eff ective reservoir prediction.The results show that multi-component joint inversion increases the constraints on the inversion conditions,reduces the multi-solution of a single P-wave inversion,and is more objective and reliable for the identification of reservoirs,effectively improving the accuracy of oil and gas reservoir prediction and development.