Determining the distribution of porosity and permeability is one of the main challenges in carbonate petroleum reservoir characterization and requires a thorough understanding of pore type and origin, as well as their spatial distributions. Conventional studies of carbonate reservoirs require interpretation and analysis of cores to understand porosity. This study investigates the use of NMR logs in the determination of pore type and origin. This study is based on the analysis of both thin section petrographic and NMR data from a single well that cored the Lower Cretaceous (Aptian) shelf carbonates belonging to the Shuaiba Formation of the Middle East. Photographs of thin sections were used to determine pore type and origin according to Ahr's genetic classification of carbonate porosity. Descriptive statistics and modeling were used to analyze the NMR T2relaxation time distributions. Descriptive statistical analyses included estimating arithmetic average, standard deviation, skewness, median, mode and 90th percentile. T2modeling was performed by fitting multiple log-normal distributions to the measured T2distribution. Data from thin section petrography and from NMR measurements were then compared using conditional probabilities. As expected, thin section analysis revealed the predominance of mud-supported fabrics and micropores between matrix grains Vugs and dissolved rudistid fragments account for most of the macro porosity. Descriptive statistics showed that the mode and th percentile of the T2distribution had the greatest power to discriminate pores by origin. The first principal component (PC1) of the mode-90th percentile system was then used to compute the probabilities of having each pore origin, knowing that PC1 belongs to a given interval. Results were good, with each origin being predictable within a certain range of PC1. Decomposition of the T2distributions was performed using up to 3 log-normal component distributions. Samples of different pore origin behaved distinctively. Depositional porosity showed no increase in fit quality with increasing number of distributions whereas facies selective and diagenetic porosity did, with diagenetic porosity showing the greatest increase.

Pore type variations account for complex velocity-porosity relationship and intensive permeability heterogeneity and consequently low oil and gas recovery in carbonate reservoir. However, it is a challenge for geologist and geophysicist to quantitatively estimate the influences of pore type complexity on velocity variation at a given porosity and porosity-permeability relationship. A new rock physics-based integrated approach in this study was proposed to quantitatively characterize the diversity of pore types and its influences on wave propagation in carbonate reservoir. Based on above knowledge, permeability prediction accuracy from petrophysical data can be improved compared to conventional approach. Two carbonate reservoirs with different reservoir features, one is a shallow carbonate reservoir with average high porosity (>10%) and another one is a supper-deep carbonate reservoir with average low porosity (<5%), are used to test the proposed approach. Paleokarst is a major event to complicate carbonate reservoir pore structure. Because of limited data and lack of appropriate study methods, it is a difficulty to characterize subsurface paleokarst 3D distribution and estimate its influences on reservoir heterogeneity. A method by integrated seismic characterization is applied to delineate a complex subsurface paleokarst system in the Upper San Andres Formation, Permian basin, West Texas. Meanwhile, the complex paleokarst system is explained by using a carbonate platform hydrological model, similar to modern marine hydrological environments within carbonate islands. How to evaluate carbonate reservoir permeability heterogeneity from 3D seismic data has been a dream for reservoir geoscientists, which is a key factor to optimize reservoir development strategy and enhance reservoir recovery. A two-step seismic inversions approach by integrating angle-stack seismic data and rock physics model is proposed to characterize pore-types complexity and further to identify the relative high permeability gas-bearing zones in low porosity reservoir (< 5%) using ChangXing super-deep carbonate reservoir as an example. Compared to the conventional permeability calculation method by best-fit function between porosity and permeability, the results in this study demonstrate that gas zones and non-gas zones in low porosity reservoir can be differentiated by using above integrated permeability characterization method.

Determining the distribution of porosity and permeability is one of the main challenges in carbonate petroleum reservoir characterization and requires a thorough understanding of pore type and origin, as well as their spatial distributions. Conventional studies of carbonate reservoirs require interpretation and analysis of cores to understand porosity. This study investigates the use of NMR logs in the determination of pore type and origin. This study is based on the analysis of both thin section petrographic and NMR data from a single well that cored the Lower Cretaceous (Aptian) shelf carbonates belonging to the Shuaiba Formation of the Middle East. Photographs of thin sections were used to determine pore type and origin according to Ahr's genetic classification of carbonate porosity. Descriptive statistics and modeling were used to analyze the NMR T2relaxation time distributions. Descriptive statistical analyses included estimating arithmetic average, standard deviation, skewness, median, mode and 90th percentile. T2modeling was performed by fitting multiple log-normal distributions to the measured T2distribution. Data from thin section petrography and from NMR measurements were then compared using conditional probabilities. As expected, thin section analysis revealed the predominance of mud-supported fabrics and micropores between matrix grains Vugs and dissolved rudistid fragments account for most of the macro porosity. Descriptive statistics showed that the mode and th percentile of the T2distribution had the greatest power to discriminate pores by origin. The first principal component (PC1) of the mode-90th percentile system was then used to compute the probabilities of having each pore origin, knowing that PC1 belongs to a given interval. Results were good, with each origin being predictable within a certain range of PC1. Decomposition of the T2distributions was performed using up to 3 log-normal component distributions. Samples of different pore origin behaved distinctively. Depositional porosity showed no increase in fit quality with increasing number of distributions whereas facies selective and diagenetic porosity did, with diagenetic porosity showing the greatest increase.