New insights into transport phenomena involved in carbonated water injection: effective mathematical modeling strategies

Carbonated water injection (CWI) is a promising enhanced oil recovery (EOR) method that provides an efficient and a more environmentally friendly alternative to meet the ever-increasing demand for energy. An additional benefit from the implementation of CWI is the storage of anthropogenic CO₂ and this has made it even more attractive. Over the years, several attempts have been made to model CWI as an EOR process but have been of very little success due to the underlying assumptions used or the modelling strategy. There are several multi-physics involved during CWI and to have an accurate model to investigate CWI, these physics need to be adequately captured. In this thesis, we have attempted to model CWI adequately by using more realistic and practical assumptions to present a novel modeling strategy. This thesis shows our research in a manuscript-based format which is presented in each chapter as major contributions. Firstly, a comprehensive review of CWI where the behavior of fluids, fluid-rock interactions and challenges associated with CWI technique have been thoroughly discussed. Secondly, the modelling investigation to capture the critical salinity which plays an important role in EOR techniques for sandstones and carbonate as well as the solubility of CO₂ during CWI is presented. Thirdly, a 3-D modeling method to investigate CWI which considers important terms such as gravity, non-instantaneous equilibrium, heterogeneity, anisotropy and well orientation is presented. Fourthly, a 1-D core modelling approach which considers the reaction term and rock dissolution in an improved attempt to capture CWI is presented. Finally, a deterministic approach is presented to effectively predict oil recovery factor based on pattern recognition and artificial intelligence. To facilitate this, the use of artificial neural network (ANN), least square support vector machine (LSSVM) modelling and gene expression programming (GEP) are adopted

Formulation Graphs for Mapping Structure-Composition of Battery Electrolytes to Device Performance

Advanced computational methods are being actively sought for addressing the challenges associated with discovery and development of new combinatorial material such as formulations. A widely adopted approach involves domain informed high-throughput screening of individual components that can be combined into a formulation. This manages to accelerate the discovery of new compounds for a target application but still leave the process of identifying the right 'formulation' from the shortlisted chemical space largely a laboratory experiment-driven process. We report a deep learning model, Formulation Graph Convolution Network (F-GCN), that can map structure-composition relationship of the individual components to the property of liquid formulation as whole. Multiple GCNs are assembled in parallel that featurize formulation constituents domain-intuitively on the fly. The resulting molecular descriptors are scaled based on respective constituent's molar percentage in the formulation, followed by formalizing into a combined descriptor that represents a complete formulation to an external learning architecture. The use case of proposed formulation learning model is demonstrated for battery electrolytes by training and testing it on two exemplary datasets representing electrolyte formulations vs battery performance -- one dataset is sourced from literature about Li/Cu half-cells, while the other is obtained by lab-experiments related to lithium-iodide full-cell chemistry. The model is shown to predict the performance metrics like Coulombic Efficiency (CE) and specific capacity of new electrolyte formulations with lowest reported errors. The best performing F-GCN model uses molecular descriptors derived from molecular graphs that are informed with HOMO-LUMO and electric moment properties of the molecules using a knowledge transfer technique.Comment: 35 pages, 10 figure

Rigorous Connectionist Models to Predict Carbon Dioxide Solubility in Various Ionic Liquids

Estimating the solubility of carbon dioxide in ionic liquids, using reliable models, is of paramount importance from both environmental and economic points of view. In this regard, the current research aims at evaluating the performance of two data-driven techniques, namely multilayer perceptron (MLP) and gene expression programming (GEP), for predicting the solubility of carbon dioxide (CO2) in ionic liquids (ILs) as the function of pressure, temperature, and four thermodynamical parameters of the ionic liquid. To develop the above techniques, 744 experimental data points derived from the literature including 13 ILs were used (80% of the points for training and 20% for validation). Two backpropagation-based methods, namely Levenberg–Marquardt (LM) and Bayesian Regularization (BR), were applied to optimize the MLP algorithm. Various statistical and graphical assessments were applied to check the credibility of the developed techniques. The results were then compared with those calculated using Peng–Robinson (PR) or Soave–Redlich–Kwong (SRK) equations of state (EoS). The highest coefficient of determination (R2 = 0.9965) and the lowest root mean square error (RMSE = 0.0116) were recorded for the MLP-LMA model on the full dataset (with a negligible difference to the MLP-BR model). The comparison of results from this model with the vastly applied thermodynamic equation of state models revealed slightly better performance, but the EoS approaches also performed well with R2 from 0.984 up to 0.996. Lastly, the newly established correlation based on the GEP model exhibited very satisfactory results with overall values of R2 = 0.9896 and RMSE = 0.0201.publishedVersio

General method for prediction of thermal conductivity for well-characterized hydrocarbon mixtures and fuels up to extreme conditions using entropy scaling

A general and efficient technique is developed to predict the thermal conductivity of well-characterized hydrocarbon mixtures, rocket propellant (RP) fuels, and jet fuels up to high temperatures and high pressures (HTHP). The technique is based upon entropy scaling using the group contribution method coupled with the Perturbed-Chain Statistical Associating Fluid Theory (PC-SAFT) equation of state. The mixture number averaged molecular weight and hydrogen to carbon ratio are used to define a single pseudo-component to represent the compounds in a well-characterized hydrocarbon mixture or fuel. With these two input parameters, thermal conductivity predictions are less accurate when the mixture contains significant amounts of iso-alkanes, but the predictions improve when a single thermal conductivity data point at a reference condition is used to fit one model parameter. For eleven binary mixtures and three ternary mixtures at conditions from 288 to 360 K and up to 4,500 bar, thermal conductivities are predicted with mean absolute percent deviations (MAPDs) of 16.0 and 3.0% using the two-parameter and three-parameter models, respectively. Thermal conductivities are predicted for three RP fuels and three jet fuels at conditions from 293 to 598 K and up to 700 bar with MAPDs of 14.3 and 2.0% using the two-parameter and three-parameter models, respectively

Building Robust Machine Learning Models for Small Chemical Science Data: The Case of Shear Viscosity

Shear viscosity, though being a fundamental property of all liquids, is computationally expensive to estimate from equilibrium molecular dynamics simulations. Recently, Machine Learning (ML) methods have been used to augment molecular simulations in many contexts, thus showing promise to estimate viscosity too in a relatively inexpensive manner. However, ML methods face significant challenges like overfitting when the size of the data set is small, as is the case with viscosity. In this work, we train several ML models to predict the shear viscosity of a Lennard-Jones (LJ) fluid, with particular emphasis on addressing issues arising from a small data set. Specifically, the issues related to model selection, performance estimation and uncertainty quantification were investigated. First, we show that the widely used performance estimation procedure of using a single unseen data set shows a wide variability on small data sets. In this context, the common practice of using Cross validation (CV) to select the hyperparameters (model selection) can be adapted to estimate the generalization error (performance estimation) as well. We compare two simple CV procedures for their ability to do both model selection and performance estimation, and find that k-fold CV based procedure shows a lower variance of error estimates. We discuss the role of performance metrics in training and evaluation. Finally, Gaussian Process Regression (GPR) and ensemble methods were used to estimate the uncertainty on individual predictions. The uncertainty estimates from GPR were also used to construct an applicability domain using which the ML models provided more reliable predictions on another small data set generated in this work. Overall, the procedures prescribed in this work, together, lead to robust ML models for small data sets.Comment: main: 17 pages, 11 figures ; SI: 55 pages, 29 figures ; to be submitted to Journal of Chemical Physic

Development and application of spectroscopy techniques for monitoring hydrate and corrosion risks

Pipelines are used to transport hydrocarbons from production wells to different locations for various purposes (e.g. processing, refinery, power generation, etc.) and CO2-rich fluids from the emission sources for disposal in suitable geological storage sites. The presence of water in such hydrocarbon and/or CO2 transport pipelines may result in corrosion, ice and/or gas hydrate formation and even pipeline blockage, so the fluid system should meet certain dehydration and/or inhibition requirements. This work describes the development and application of different spectroscopic (UV-VIS and NIR) methods for identifying and controlling flow assurance issues relating to gas hydrate and corrosion. For hydrates, a Fourier Transform Near-Infrared (FTNIR) spectroscopy method using chemometric models was developed to measure the concentration of main hydrocarbon gases (Methane through butanes) under in-situ pressure (up to 13.8 MPa). This approach was then used for detecting initial signs of hydrate formation based on reduction in the concentration of some preferential components in the gas phase. Furthermore, injection of hydrate inhibitors based on the calculated/measured hydrate phase boundary, water cut, pressure and temperature conditions, and the amount of inhibitor lost to non-aqueous phases is a commonly used method for avoiding gas hydrates problems. Thus, it is crucial to monitor salt and inhibitor concentration in the fluids along the pipeline and/or downstream in order to optimise the injection rate. To address this requirement, a novel method was developed by combining UV and NIR spectra to predict the concentration of salt and hydrate inhibitors (THIs and KHIs) simultaneously in aqueous solutions. In the case of corrosion, the potential of visible spectroscopic technique was investigated for determining the pH in CO2/ CO2-rich mixtures saturated water, and CO2/ CO2-rich mixtures saturated NaCl solutions at pressures up to 15 MPa and temperature ranges from 293.15 to 323.15 K. Furthermore, we described and evaluated a model that uses a robust thermodynamic basis for describing the solubility of gases in the aqueous phase and Pitzer’s theory for determining the activity coefficients of the ionic species involved. The model was tested in concentrated NaCl solutions under CO2 pressure at realistic industrial operating temperatures. The developed spectroscopic techniques were experimentally evaluated at lab conditions. Results show that these techniques can be applied to detect initial signs of hydrate formation, to optimise hydrate inhibitor injection rate, and to measure the pH of CO2 saturated H2O/brine systems in the downhole/wellbore/pipeline region

Estimation of in-situ fluid properties from the combined interpretation of nuclear, dielectric, optical, and magnetic resonance measurements

During the last few decades, the quantification of hydrocarbon pore volume from borehole measurements has been widely studied for reservoir descriptions. Relatively less effort has been devoted to estimating in-situ fluid properties because (1) acquiring fluid samples is expensive, (2) reservoir fluids are a complex mixture of various miscible and non-miscible phases, and (3) they depend on environmental factors such as temperature and pressure. This dissertation investigates the properties of fluid mixtures based on various manifestations of their electromagnetic properties from the MHz to the THz frequency ranges. A variety of fluids, including water, alcohol, alkane, aromatics, cyclics, ether, and their mixtures, are analyzed with both laboratory experiments and numerical simulations. A new method is introduced to quantify in-situ hydrocarbon properties from borehole nuclear measurements. The inversion-based estimation method allows depth-continuous assessment of compositional gradients at in-situ conditions and provides thermodynamically consistent interpretations of reservoir fluids that depend greatly on phase behavior. Applications of this interpretation method to measurements acquired in two field examples, including one in a gas-oil transition zone, yielded reliable and verifiable hydrocarbon compositions. Dielectric properties of polar liquid mixtures were analyzed in the frequency range from 20 MHz to 20 GHz at ambient conditions. The Havriliak-Negami (HN) model was adapted for the estimation of dielectric permittivity and relaxation time. These experimental dielectric properties were compared to Molecular Dynamics (MD) simulations. Additionally, thermodynamic properties, including excess enthalpy, density, number of hydrogen bonds, and effective self-diffusion coefficient, were computed to cross-validate experimental results. Properties predicted from MD simulations are in excellent agreement with experimental measurements. The three most common optical spectroscopy techniques, i.e. Near Infrared (NIR), Infrared, and Raman, were applied for the estimation of compositions and physical properties of liquid mixtures. Several analytical techniques, including Principal Component Analysis (PCA), Radial Basis Functions (RBF), Partial Least-Squares Regression (PLSR), and Artificial Neural Networks (ANN), were separately implemented for each spectrum to build correlations between spectral data and properties of liquid mixtures. Results show that the proposed methods yield prediction errors from 1.5% to 22.2% smaller than those obtained with standard multivariate methods. Furthermore, the errors can be decreased by combining NIR, Infrared, and Raman spectroscopy measurements. Lastly, the ¹H NMR longitudinal relaxation properties of various liquid mixtures were examined with the objective of detecting individual components. Relaxation times and diffusion coefficients obtained via MD simulations for these mixtures are in agreement with experimental data. Also, the ¹H-¹H dipole-dipole relaxations for fluid mixtures were decomposed into the relaxations emanate from the intramolecular and intermolecular interactions. The quantification of intermolecular interactions between the same molecules and different molecules reveals how much each component contributes to the total NMR longitudinal relaxation of the mixture as well as the level of interactions between different fluids. Both experimental and numerical simulation results documented in this dissertation indicate that selecting measurement techniques that can capture the physical property of interest and maximize the physical contrasts between different components is important for reliable and accurate in-situ fluid identificationPetroleum and Geosystems Engineerin