Thermodynamic modeling and correlations of CH4, C2H6, CO2, H2S, and N2 hydrates with cage occupancies

Abstract

The present work focuses on developing a framework for accurate prediction of thermodynamic conditions for single-component hydrates, namely CH4, CO2, N2, H2S, and C2H6 (coded in MATLAB). For this purpose, an exhaustive approach is adopted by incorporating eight different equations of states, namely Peng–Robinson, van der Waals, Soave–Redlich–Kwong, Virial, Redlich–Kwong, Tsai-Teja, Patel, and Esmaeilzadeh–Roshanfekr, with the well-known van der Waals–Platteeuw model. Overall, for I–H–V phase region, the Virial and van der Waals equation of state gives the most accurate predictions with minimum AAD%. For Lw–H–V phase region, Peng–Robinson equation of state is found to yield the most accurate predictions with overall AAD of 3.36%. Also, genetic programming algorithm is adopted to develop a generalized correlation. Overall, the correlation yields quick estimation with an average deviation of less than 1%. The accurate estimation yields a minimal AAD of 0.32% for CH4, 1.93% for C2H6, 0.77% for CO2, 0.64% for H2S, and 0.72% for N2. The same correlation can be employed for fitting phase equilibrium data for other hydrates too. The tuning parameter, n, is to be used for fine adjustment to the phase equilibrium data. The findings of this study can help for a better understanding of phase equilibrium and cage occupancy behavior of different gas hydrates. The accuracy in phase equilibria is intimately related to industrial applications such as crude oil transportation, solid separation, and gas storage. To date, no single correlation is available in the literature that can accurately predict phase equilibria for multiple hydrate species. The novelty of the present work lies in both the accuracy and generalizability of the proposed correlation in predicting the phase equilibrium data. The genetic programming generalized correlation is convenient for performing quick equilibrium prediction for industrial applications.

Application of Cloud Computing and GIS Based on Internet of Things in Oil and Gas Storage and Transportation Production Management and Safety Monitoring and Early Warning System

“…It also allocates materials efficiently and reasonably in the military and urban management. In large-scale urban construction, the planning of roads, sports venues, parking lots, green spaces, etc., enables the rational distribution of facilities and the efficient use of resources [14,15].…”

Data Analysis and Symbolic Regression Models for Predicting CO and NOx Emissions from Gas Turbines

“…[12] presents the formulae for estimating bubble-point pressure and the formation volume factor of crude oil with four basic oil properties: temperature, gas solubility, oil API gravity and gas-specific gravity as predictors. In [13], the SR methodology was used to develop a correlation to predict thermodynamic conditions for hydrates' formation. In [14], explicit approximations of widely used hydraulics, the Colebrook equation for flow friction obtained with SR method are considered.…”

Physio-Chemical and Mineralogical Characteristics of Gas Hydrate-Bearing Sediments of the Kerala-Konkan, Krishna-Godavari, and Mahanadi Basins

“…The basin is distributed in an area of 5.8 × 10 5 km 2 and the main sources of sediments in the Arabian Sea are the Indus River, which discharges in the northernmost part of the Arabian Sea and the Tapti and Narmada Rivers and ends in the Gulf of Combay. The Kerala-Konkan basin contains 109,000 km 3 Cenozoic sediments [40]. The sediments on the west coast are primarily globigerina clay [39].…”

“…Gas hydrates are formed when the guest molecules of suitable size and shape are captured through van der Waals forces in the well-defined cages of host molecules [1]. The molecules of water and gas chemically interact to construct an ice-like crystalline structure [2], which is stable at an absolute pressure and temperature range [3]. The bottom of the stability area of hydrate structures is designated with the Bottom-Simulating Reflectors (BSR), which are parallel to the seafloor at a sub-bottom depth of a specific hundred meters [4,5].…”