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

Date

2018-12

Authors

Lee, Hyungjoo

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Abstract

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 identification

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