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.

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.

Yes / Multistage Flash (MSF) desalination process is still a dominant process, especially in the Gulf region, to produce high quality freshwater. Although there has been energy price surge in recent years, MSF process will continue to operate in that region for some foreseeable future. The key challenge is how to make such processes still profitable. Understanding the dynamics of any processes under uncertainty and disturbances is very important to make a process operationally feasible and profitable. The main aim of this work is to understand the dynamics of industrial scale MSF process using high fidelity and reliable process model. For this purpose, a detailed dynamic model for the MSF process incorporating key and new features is developed and validated against the actual data of a large-scale seawater desalination plant. The model is then used to study the behaviour of large scale MSF processes for disturbances in steam temperature, feed temperature and the recycle brine flow rate. The simulation results show that the last stage requires a longer time to settle compared to the preceding stages. In addition, steam temperature shows insignificant influence on the performance ratio compared to the inlet seawater temperature and recycle brine flow rate. Furthermore, it is found that the productivity of plant can increase in the winter compared to that in the summer. However, this benefit comes at the expense of increased steam consumption in the winter, resulting in a low performance ratio.