- Experimental and Numerical Study of High-Pressure Adsorption and Desorption of Methane, Ethane And CO2 and Their Mixtures on Shale Gas and depleted reservoirs to Optimize the economics of recovery and storage
- This work will aim at investigating the high pressure adsorption and desorption of Methane, Ethane and CO2 and their mixtures on different samples from different reservoirs. Secondly numerical simulation and modelling of adsorbed gas contribution to overall gas storage and recovery in these reservoirs will be explored. Due to the nonexistence of an adsorption database for shale gas and reservoirs in the UK, this study will provide a basis for providing such a database that could prove useful in accounting for gas storage and recovery in these reservoirs. The results from the study would also prove useful in identifying adsorption models that can adequately predict the adsorption capacities to further enhance numerical studies of gas storage and recoveries. To be able to achieve this aim, the following studies will be conducted:
- 1. Study the binary and ternary mixtures of methane, ethane and CO2 on several samples from shale gas and gas reservoirs in order to obtain the adsorption and desorption equilibria of these gas affinity regions.
- 2. Study the effect of several reservoirs characteristics such as porous structure, surface composition and density on the adsorption and desorption behavior of gas mixtures.
Multi-criteria decision-making MCDM is a branch of operations research. Decision making often involves inaccuracies and ambiguities that can be effectively handled using fuzzy sets and fuzzy decision techniques. Much research has been carried out into the theoretical and applied aspects of MCDM and fuzzy in recent years. Most real-world decision problems have several conflicting criteria and goals that need to be considered at the same time. MCDM addresses the need for a number structure in the material selection process. MCDM provides a basis for the selection, classification and prioritization of materials/economic and policy drivers and helps with the overall assessment. The value of information/data VOI is a normative theory (i.e. regarding however individuals ought to decide in a very rational manner) for creating choices concerning data acquisition. Value of information VOI aims to assess the advantages of gathering information before making a decision. The key side of VOI is that the possibility of assessing whether or not it's worthy to amass new information, and this relies on the chance of exploitation of new data to various decisions that may be created otherwise without that information.
Most decision problems in real life have several conflicting criteria and goals that need to be considered at the same time; for example, the compromises necessary to strike a balance between the performance and cost of a car, or between health and the enjoyment of rich foods this can be expanded to chemical properties and performance indicators in material/ process selection and design. Of the many uses for MCDM, the choice of the source is undoubtedly one of the most important. MCDM provides a basis for the selection, classification and prioritization of materials and helps with the overall assessment. The MCDM generally follows six steps, which include formulating problems, identifying requirements, setting goals, identifying various alternatives, establishing criteria, and identifying and applying decision-making techniques
Our research is broken down to two elements that map sustainability wholistic Process design and to Safety and risk analysis.
We are currently operating two projects in this area with the support of the world association for sustainable development WASD
- An integrated decision-making solution for a sustainable Whole Process Design: with the objectives of this project is to develop an effective Multi-Criteria Decision-Making MCDM solution for application in sustainable Whole Process Design (WPD) that includes the fuzzy nature of data refined through assessing the Value of Information VOI
- Intelligent prediction, monitoring and decision-making for risk in the chemical and energy industries: The research aim is to create a decision-making system to investigate ways to alleviate risks in Chemical and Energy Industries. A predictive system based on trend analysis of historic data to determine the probable future condition of trait ‘states’ and so predict potential situations that may lead to catastrophe.