Optimized Design of Shale Gas Processing and NGL Recovery Plant under Uncertain Feed Conditions

Shale gas is an increasingly booming resource and it has been predicted to increase from 1% in 2000 to 40% in 2035 of the total US domestic gas produced. Since shale gas is both industrially economical and environmentally clean compared to oil or coal as a resource, many studies are focused on developing technologies to monetize shale gas. However, one of the key challenges in utilizing shale gas is its fluctuating flow rate and compositional behavior. The flow rate of a shale gas well dwindles over a period of time and the composition of shale gas differs from well to well in the same shale play. This provides a challenge in designing a plant of optimum size for a shale gas processing and NGL recovery plant. In this study, this uncertainty in shale gas feed flow rate and composition is addressed while designing a shale gas processing and NGL recovery plant. First, different shale gas flow rates are chosen over a period of shale gas well life based on the average shale gas rate declination curve of a shale play. Second, two different process flow sheets are developed (i) using conventional technology and (ii) using novel technology. In the novel technology, the NGL recovery section of the conventional technology is modified to accommodate novel changes such as using a divided wall column or pre-fractionated sequence to separate methane, ethane, and propane. Later, these process flow sheets are simulated in Aspen plus for comparing the economics of different plant sizes. Furthermore, heat integration and optimization of individual units of the process flow sheets are carried out using pinch and sensitivity analyses, respectively. Lastly, the economic analysis of a plant of optimum size with constant feed flow rate over its plant life is evaluated. In this case, shale gas from different wells is collected in a header and adjusted such that the shale gas flow rate is constant to the plant. Environmental impact of the process is also observed. From the economic analysis of various cases for conventional and novel technology, it is observed that case-3 provides the optimum plant design with highest ROI percentage compared to other cases and for case-3, novel technology ROI is 4.17% more compared to conventional technology. Finally, constant production rate case, at the flow rate of case-3, the ROI percentage is observed to be more than minimum requirement implying that this processing plant is economically viable to implement

Optimized Design of Shale Gas Processing and NGL Recovery Plant under Uncertain Feed Conditions

Shale gas is an increasingly booming resource and it has been predicted to increase from 1% in 2000 to 40% in 2035 of the total US domestic gas produced. Since shale gas is both industrially economical and environmentally clean compared to oil or coal as a resource, many studies are focused on developing technologies to monetize shale gas. However, one of the key challenges in utilizing shale gas is its fluctuating flow rate and compositional behavior. The flow rate of a shale gas well dwindles over a period of time and the composition of shale gas differs from well to well in the same shale play. This provides a challenge in designing a plant of optimum size for a shale gas processing and NGL recovery plant. In this study, this uncertainty in shale gas feed flow rate and composition is addressed while designing a shale gas processing and NGL recovery plant. First, different shale gas flow rates are chosen over a period of shale gas well life based on the average shale gas rate declination curve of a shale play. Second, two different process flow sheets are developed (i) using conventional technology and (ii) using novel technology. In the novel technology, the NGL recovery section of the conventional technology is modified to accommodate novel changes such as using a divided wall column or pre-fractionated sequence to separate methane, ethane, and propane. Later, these process flow sheets are simulated in Aspen plus for comparing the economics of different plant sizes. Furthermore, heat integration and optimization of individual units of the process flow sheets are carried out using pinch and sensitivity analyses, respectively. Lastly, the economic analysis of a plant of optimum size with constant feed flow rate over its plant life is evaluated. In this case, shale gas from different wells is collected in a header and adjusted such that the shale gas flow rate is constant to the plant. Environmental impact of the process is also observed. From the economic analysis of various cases for conventional and novel technology, it is observed that case-3 provides the optimum plant design with highest ROI percentage compared to other cases and for case-3, novel technology ROI is 4.17% more compared to conventional technology. Finally, constant production rate case, at the flow rate of case-3, the ROI percentage is observed to be more than minimum requirement implying that this processing plant is economically viable to implement

An Integrated Approach to Water-Energy Nexus with Multiple Energy Sources

The sustainable development of the entire world is confronting considerable challenges due to the tremendous expansion in energy demand that synchronizes with fresh water scarcity, vast depletion of conventional energy sources and climate change. Consequently, the necessity has emerged for creating suitable management strategies for existing water resources (e.g., wastewater treatment) and for integrating traditional energy sources with renewables (e.g., solar energy, wind energy, biofuels, etc.). The objective of this study is to develop a novel design framework of the water-energy nexus system, which optimized according to economic and environmental metrics using certain parameters (leading to deterministic optimization) and uncertain parameters (leading to stochastic optimization). The system comprises multiple energy sources, cogeneration process, and desalination technologies. Solar energy is incorporated to provide thermal power directly to a multi-effect distillation plant (MED) exclusively (to be more feasible economically), or to the entire system through a steam generator. Thus, MED is driven by direct solar energy, indirect solar energy (thermal energy storage), and surplus heat from the cogeneration process. Additionally, electric power production is intended to meet a reverse osmosis plant (RO) demand and the local electric grid (if it is connected to the system). The deterministic optimization problem is formulated as a multi-period Mixed Integer Non-Linear Programming (MINLP) to discretize operation period to track the diurnal fluctuations of solar energy. However, the stochastic optimization problem is formulated as a multiscenario MINLP problem that is a deterministic equivalent of a two-stage stochastic programming model for handling uncertainty in operational parameters (normal direct irradiance, fossil fuel price) through a finite set of scenarios. A case study is solved for water treatment and energy management for Eagle Ford Basin in Texas to obtain the maximum annual profit of the entire system. The long-term evaluation for the techno-economic performance of solar energy conversion systems is highly dependent on the availability of solar radiation data and their accuracy. This study offers hierarchical calculation methodologies to estimate solar irradiance values for a specific location under different sky conditions. A case study is solved to predict hourly direct normal irradiance for San Antonio city in Texas