RESUMEN
Polymers have been used effectively in the Oil & Gas Industry for a variety of field applications, such as enhanced oil recovery (EOR), well conformance, mobility control, and others. Polymer intermolecular interactions with the porous rock, in particular, formation clogging and the associated alterations to permeability, is a common problem in the industry. In this work, fluorescent polymers and single-molecule imaging are presented for the first time to assess the dynamic interaction and transport behavior of polymer molecules utilizing a microfluidic device. Pore-scale simulations are performed to replicate the experimental observations. The microfluidic chip, also known as a "Reservoir-on-a-Chip" functions as a 2D surrogate to evaluate the flow processes that take place at the pore-scale. The pore-throat sizes of an oil-bearing reservoir rock, which range from 2 to 10 nm, are taken into consideration while designing the microfluidic chip. Using soft lithography, we created the micromodel from polydimethylsiloxane (PDMS). The conventional use of tracers to monitor polymers has a restriction due to the tendency of polymer and tracer molecules to segregate. For the first time, we develop a novel microscopy method to observe the dynamic behavior of polymer pore-clogging and unclogging processes. We provide direct dynamic observations of polymer molecules during their transport within the aqueous phase and their clustering and accumulations. Pore-scale simulations were carried out to simulate the phenomena using a finite-element simulation tool. The simulations revealed a decline in flow conductivity over time within the flow channels that experienced polymer accumulation and retention, which is consistent with the experimental observation of polymer retention. The performed single-phase flow simulations allowed us to assess the flow behavior of the tagged polymer molecules within the aqueous phase. Additionally, both experimental observation and numerical simulations are used to evaluate the retention mechanisms that emerge during flow and how they affect apparent permeability. This work provides new insights to assessing the mechanisms of polymer retention in porous media.
RESUMEN
Reservoir stimulation is a widely used technique in the oil and gas industry for increasing the productivity of hydrocarbon reservoirs, most notably in carbonate formations. This work aims to develop an optimization workflow under uncertainty for matrix acidizing. A reactive transport model is implemented in a finite-element framework to simulate the initiation and propagation of dissolution channels in porous carbonate rock. The model is verified using an analytical solution. We utilize surrogate modeling based on polynomial chaos expansion (PCE) and Sobol indices to identify the most significant parameters. We investigate the effect of varying 12 identified parameters on the efficiency of the stimulation process using dimensionless groups, including the Damköhler, Peclet, and acid capacity numbers. Furthermore, the surrogate model reproduces the physics-based results accurately, including the dissolution channels, the pore volume to breakthrough, and the effective permeability of the stimulated rock. The developed workflow assesses how uncertainties propagate to the model's response, where the surrogate model is used to calculate the univariate effect. The global sensitivity analysis shows that the acid capacity number is the most significant parameter for the pore volume to breakthrough with the highest Sobol index. The marginal effect calculated for the individual parameter confirms the results from Sobol indices. This work provides a systematic workflow for uncertainty analysis and optimization applied to the processes of rock stimulation. Characterizing the impact of uncertainty provides physical insights and a better understanding of the matrix acidizing process.
RESUMEN
In this study, a novel experimental setup is proposed for which a column filled with glass beads and parallelepiped-shaped limestone beams is used to reconstruct a multiple fracture limestone media. The proposed setup produces asymmetric breakthrough curves (BTCs) that are consistent with the shape expected from the past field and lab-scale studies. Three transport experiments have been conducted under fast, medium, and slow flow velocity conditions. The research focuses on parameter and state estimation using Bayesian inference via Markov Chain Monte Carlo (MCMC) sampler, investigating the degree to which three models of transport through fractured media can reproduce the experimental results under the three flow conditions. The first transport model, named ADE, is based on the equivalent porous medium (EPM) approach and corresponds to the linear advection dispersion equation (ADE). The second model, named FOMIM (first-order mobile immobile), is based on the mobile/immobile approach and uses the dual porosity model with a linear first-order transfer between mobile and immobile regions. The third model, named NLMIM (non-linear mobile-immobile), uses a nonlinear transfer function between these two regions. The results of the three models show that almost all the unknown model input parameters can be well-estimated with narrow confidence intervals using the MCMC method. With respect to state estimation, the ADE model fails to reproduce correctly the tail of the BTCs observed under slow and medium flow conditions. The FOMIM model improves the tailing of the BTCs, but significant discrepancies remain between simulated and measured concentrations. The NLMIM model with velocity-dependent parameters is the only model that captures BTCs under all three conditions of slow, medium, and fast flow velocities.
Asunto(s)
Carbonato de Calcio , Modelos Teóricos , Teorema de Bayes , Método de Montecarlo , Porosidad , Movimientos del AguaRESUMEN
Coastal aquifers are a vital water source for the more than one billion people living in coastal regions around the globe. Due to the intensity of economic activities and density of population, these aquifers are highly susceptible not only to seawater intrusion, but also to anthropogenic contamination, which may contaminate the aquifer and submarine groundwater discharge. Identification and localization of contaminant source characteristics are needed to reduce contamination. The techniques of contaminant source identification are based on numerical models that require the knowledge of the hydrodynamic properties of aquifers. Thus, the challenging topic of contaminant source and aquifer characterization (CSAC) is widely developed in the literature. However, most of the existing studies are concerned with inland aquifers with relatively uniform groundwater flow. Coastal aquifers are influenced by density-driven seawater intrusion, tidal forces, and water injection and abstraction wells. These phenomena create complex flow and transport patterns, which render the CSAC especially challenging and may explain why CSAC has never been addressed in coastal settings. The presented study aims to provide an efficient methodology for the simultaneous identification of contaminant source characteristics and aquifer hydraulic conductivity in coastal aquifers. For this purpose, the study employs numerical modeling of density-dependent flow and multiple-species solute transport, to develop trained and validated artificial neural network metamodels, and then employs these metamodels in a version of the ensemble Kalman filter (EnKF) termed the 'constrained restart dual EnKF (CRD-EnKF)' algorithm. We show that this variant of the EnKF can be successfully applied to CSAC in the complex setting of coastal aquifers. Furthermore, the study analyzes the influence of common issues in CSAC monitoring, such as the effect of non-ideal monitoring network distributions, measurement errors, and multi-level vs. single level monitoring wells.