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Gas solubility in brine is crucial input for engineering design of various chemical/petroleum processes such as oil recovery, CO2 sequestration in saline aquifers and water bearing formations, separation and utilization, corrosion in wellbores/facility/pipelines with acidic gases, and gas processing. However, with limited experimental data, intrinsic trends of gas solubility under varying conditions (pressure/ temperature/brine composition) are difficult to obtain. This study uses machine-learning-based approach to explore those intrinsic trends while identifying the contributing elements correlated with fluid properties. Gas solubility in brine depends on various thermodynamic properties of the components, pressure, temperature, and salinity. To explore effect of these factors, we employ various Machine learning (ML) tools: decision tree (DT), random forest (RF) and artificial neural network (ANN) techniques due to their stability and convergence characteristics coherent with the data utilized and develop a framework to determine solubility of a gas in brine solutions. While most existing literature are limited to very few salts (NaCl/KCl/CaCl2), this work captures combinations of various common salts (chlorides, carbonates/bicarbonates, and sulphates) as they are seen in real formation brines and water utilities. The prediction from ML models were validated against the available experimental data that were not used in training. The main results are as follows: Validation processes indicated that ML models predict the experimental trends accurately, within the relative error of 1% for gas-water systems and 3% for complex gas-brine systems. Various input features based on the thermodynamic and physical properties of gases and ions (cations and anions) were considered and main contributing features were identified. Most importantly, the framework is general, fast, convenient and can easily be extended for different gas species including greenhouse or hydrocarbon gases, as well as for variety of salts. Additionally, it can fill the gaps in experimental data for the gas-brine systems, and extrapolate to elevated pressure and temperature conditions. While ML-based approach to estimate gas solubility in brines have been developed in the literature, they are very restrictive in terms of their broadness/applicability and valid for specific gases such as CO2 as well as few salts (NaCl/KCl/CaCl2). Here, the ML-based framework covers a wide range of salts and gases, and additionally, the current framework can easily be extended to other systems.
Gas solubility in brine is crucial input for engineering design of various chemical/petroleum processes such as oil recovery, CO2 sequestration in saline aquifers and water bearing formations, separation and utilization, corrosion in wellbores/facility/pipelines with acidic gases, and gas processing. However, with limited experimental data, intrinsic trends of gas solubility under varying conditions (pressure/ temperature/brine composition) are difficult to obtain. This study uses machine-learning-based approach to explore those intrinsic trends while identifying the contributing elements correlated with fluid properties. Gas solubility in brine depends on various thermodynamic properties of the components, pressure, temperature, and salinity. To explore effect of these factors, we employ various Machine learning (ML) tools: decision tree (DT), random forest (RF) and artificial neural network (ANN) techniques due to their stability and convergence characteristics coherent with the data utilized and develop a framework to determine solubility of a gas in brine solutions. While most existing literature are limited to very few salts (NaCl/KCl/CaCl2), this work captures combinations of various common salts (chlorides, carbonates/bicarbonates, and sulphates) as they are seen in real formation brines and water utilities. The prediction from ML models were validated against the available experimental data that were not used in training. The main results are as follows: Validation processes indicated that ML models predict the experimental trends accurately, within the relative error of 1% for gas-water systems and 3% for complex gas-brine systems. Various input features based on the thermodynamic and physical properties of gases and ions (cations and anions) were considered and main contributing features were identified. Most importantly, the framework is general, fast, convenient and can easily be extended for different gas species including greenhouse or hydrocarbon gases, as well as for variety of salts. Additionally, it can fill the gaps in experimental data for the gas-brine systems, and extrapolate to elevated pressure and temperature conditions. While ML-based approach to estimate gas solubility in brines have been developed in the literature, they are very restrictive in terms of their broadness/applicability and valid for specific gases such as CO2 as well as few salts (NaCl/KCl/CaCl2). Here, the ML-based framework covers a wide range of salts and gases, and additionally, the current framework can easily be extended to other systems.
Gas solubility in brine plays crucial role in designing various industrial applications such as oil recovery, CCS, corrosion, and gas processing. However, most studies include only standard salts and may not capture the full spectrum of formation brines. The objective of this work is to develop a semi-hybrid framework that can determine the gas solubility in brine solution at extended pressure/temperature ranges, which is applicable to any gas and salt mixture of choice. The work includes the coupling of semi-empirical model and machine learning (ML) approach. In particular, it is an extension to Setschenow's correlation where coefficients are evaluated using ML tool based on decision tree (DT). The features in the ML models include the ionic properties of cations and anions, and thermodynamic properties of gases. This work captures combinations of various salts such as chlorides, carbonates/bicarbonates, and sulphates (as they are seen in real formation brines and water utilities applications), and various standard gases (including hydrocarbon, non-hydrocarbon/polar and acidic gases). A semi-hybrid (physics augmented) framework is developed to estimate gas solubility in brines for a generic gas-brine systems. It is applicable for a wide range of pressures, temperatures, and brine compositions. The prediction from semi-hybrid models were validated against the available experimental data. The main results are as follows: The Setschenow's coefficients for any cations, anions and gases can be generated within 1 – 3% accuracies.The semi-hybrid models predict the experimental trends of gas solubility in brine solution accurately, within the relative error of 1 – 6% for complex gas-brine systems.Most importantly, the framework is general, fast, convenient and can easily be extended for a novel species including greenhouse or hydrocarbon gases, as well as for variety of salts. Additionally, it can fill the gaps in experimental data for the gas-brine systems, and can extrapolate to elevated pressure and temperature conditions. In this work, the applicability is demonstrated for many salts that are seen in formation brine, and many gases that are used in gas injection/storage and gas processing applications. The most ML, correlation and EOS-based studies in the literature on estimating gas solubility in brine are restrictive and valid only for specific gases such as CO2 as well as few salts (NaCl/KCl/CaCl2). Here, we develop a semi-hybrid framework that can estimate the solubility of any gas in a given brine composition that could consists of wide range of salts and salt mixtures, which is the main novelty of the work.
Reduction in Carbon-footprint has been gaining attention in variety of industries from manufacturing to energy due to the geopolitical pressures and climate related issues. Carbon capture and storage (CCS) and enhanced geothermal systems using CO2 as energy carrier are some of the possible decarbonization pathways. Process design for these options requires accurate estimation of thermochemical properties of CO2 at various temperature/pressure conditions, in both subcritical and supercritical regions. The objective of this work is to present coupled experimental- and equation-of-state (EOS) modeling based on general framework to estimate heat capacities, enthalpy, entropy, sonic velocity, density, Joule-Thomson coefficient, and compressibility of CO2 that is applicable to wide range of pressure and temperature conditions. The sonic velocity measurement is based on a pulse-echo technique while the density measurements were performed in a PVT cell. The subject measurements were conducted at two temperatures (300 and 311K), one below and the other one being above the critical temperature of CO2 (304K). The pressure points for the measurements range between 1 - 200 bar. Phase behavior is modeled using Peng and Robinson (1976, 1978) Equation of State (PR78-EOS) with Peneloux et al. (1982) volume-shift shift to accurately determine the CO2 density. First, the ideal part of the CO2 heat capacity is obtained from correlations available in literature and the residual part is obtained using the EOS. After evaluation of the heat capacities, enthalpy, entropy, speed of sound, Joule-Thomson coefficient and compressibility are directly obtained from EOS. This work presents experimental and modeling results on sonic velocity and density of CO2 at two different temperatures (300 and 311K) within the pressure range of 1- 200 bar. An EOS-based framework, utilizing PR78 with Peneloux et al. volume shift, is developed to determine the CO2 properties (such as phase boundary, density, heat capacities, enthalpy, entropy, sonic velocity and compressibility) at extended pressure and temperature conditions. The main results of this study are as follows: Experimental results on density and sonic velocity are aligned with the measured data found in the literature.Estimation of the CO2 properties from EOS-based framework agrees very well with the literature and newly presented data within, all within 1-3% relative error.Compressibility of the fluid is derived directly from the experimental measurements, bypassing the density-derivative-based approach and hence avoiding the significant errors associated with the discrete density data containing noise/fluctuations and as well as the nature of the compressibility being a derivative property.Most importantly, the framework is general, and applicable for the use of other EOS models, and can also be extended to other fluid systems. Novelty of this work lies in new experimental data on sonic velocity and density of CO2 (especially at high pressures) as well as development of an EOS-framework to determine thermodynamic properties of CO2 through sonic velocity. Proposed framework leads to more accurate estimation of compressibility, density, sonic velocity, heat capacities, enthalpy and entropy.
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