The depletion of oil reserves and higher oil prices has made chemical enhanced oil recovery (EOR) methods more attractive in recent years. Because of geological heterogeneity, unfavorable mobility ratio, and capillary forces, conventional oil recovery (including water flooding) leaves behind much oil in reservoir, often as much as 70% OOIP (original oil in place). Surfactant/polymer flooding targets these bypassed oil left after waterflood by reducing water mobility and oil/water interfacial tension. The complexity and uncertainty of reservoir characterization make the design and implementation of a robust and effective surfactant/polymer flooding to be quite challenging. Accurate numerical simulation prior to the field surfactant/polymer flooding is essential for a successful design and implementation of surfactant/polymer flooding. A recently developed unified polymer viscosity model was implemented into our existing polymer module within our in-house reservoir simulator, the Implicit Parallel Accurate Reservoir Simulator (IPARS). The new viscosity model is capable of simulating not only the Newtonian and shear-thinning rheology of polymer solution but also the shear-thickening behavior, which may occur near the wellbore with high injection rates when high molecular weight Partially Hydrolyzed Acrylamide (HPAM) polymers are injected. We have added a full capability of surfactant/polymer flooding to TRCHEM module of IPARS using a simplified but mechanistic and user-friendly approach for modeling surfactant/water/oil phase behavior. The features of surfactant module include: 1) surfactant component transport in porous media; 2) surfactant adsorption on the rock; 3) surfactant/oil/water phase behavior transitioned with salinity of Type II(-), Type III, and Type II(+) phase behaviors; 4) compositional microemulsion phase viscosity correlation and 5) relative permeabilities based on the trapping number. With the parallel capability of IPARS, commercial scale simulation of surfactant/polymer flooding becomes practical and affordable. Several numerical examples are presented in this dissertation. The results of surfactant/polymer flood are verified by comparing with the results obtained from UTCHEM, a three-dimensional chemical flood simulator developed at the University of Texas at Austin. The parallel capability and scalability are also demonstrated. / text

Surfactant formulations consisting of surfactant, alkali, polymer, and electrolyte have been developed using well defined screening processes established through experimentation in labs around the world. Due to recent advances in chemical enhanced oil recovery, surfactants can be used to extend the life of mature reservoirs with increasingly diverse conditions. High temperatures, complex geochemistry, or high clay content can provide significant challenges when developing formulations for chemical flooding. Through careful selection and screening of surfactants and chemicals, oil recovery of greater than 90% can be achieved in laboratory corefloods despite these difficulties. The objective of this research was to determine the ideal surfactant formulation using a sulfate surfactant for a reservoir with high clay content at 85 ºC. Advances in our laboratory have shown sulfate surfactants to be stable under specific conditions at elevated temperature. In addition, new methods of synthesizing surfactants have yielded a vast array of structures and iterations of novel surfactants to test for EOR applicability. Experiments contained within include surfactant screening both with and without the presence of crude oil and evaluation of polymer and microemulsion viscosity. From these results, a series of corefloods were performed in Berea and reservoir corefloods that yielded oil recovery of 90% and above with low surfactant retention. / text

Reservoir pressure and solution gas can significantly alter the microemulsion phase behavior and the design of a surfactant-polymer flood. This dissertation shows how to predict changes in microemulsion phase behavior from dead oil at atmospheric pressure to live crude at reservoir pressure. Our method requires obtaining only a few glass pipette measurements of microemulsion phase behavior at atmospheric pressure. The key finding is that at reservoir pressure the optimum solubilization ratio and the logarithm of optimal salinity behave linearly with equivalent alkane carbon number (EACN). These trends are predicted from the experimental data at atmospheric pressure based on density calculations of pure components using the Peng-Robinson equation-of-state (PREOS). We show that predictions of the optimum conditions for live oil are in good agreement with the few experimental measurements that are available in the literature. We also present new measurements at atmospheric pressure to verify the established trends. The experiments show that while pressure induces a phase transition from upper microemulsion (Winsor Type II+) to lower microemulsion (Winsor Type II-), solution gas does the opposite. An increase in pressure decreases the optimum solubilization ratio and shifts the optimum salinity to a larger value. Adding methane to dead oil at constant pressure does the reverse. Thus, these effects are coupled and both must be taken into account. We show using a numerical simulator that these changes in the optimum conditions can impact oil recovery if not accounted for in the SP design. / text

Surfactant-polymer (SP) and alkali-surfactant-polymer (ASP) flooding has gained little traction among different tertiary recovery strategies such as thermal and miscible gas flooding; however, many mature onshore reservoirs could be potential candidates. More than four decades of research has detailed technical challenges and successes through laboratory experimentation, chemical flood simulation, and some pilot projects, which have provided technical screening procedures to efficiently filter unfeasible projects. Therefore, technical understanding seems sufficient to advance projects through early development stages; however, a project value identification and realization process ultimately dictates project implementation in the oil and gas industry, with technical feasibility merely supporting overall valuation and project feasibility. A quick early screening methodology integrating important project valuation criteria can efficiently assess large numbers of projects. The relatively few studies detailing chemical flooding valuation from just an economic standpoint reflects the need for an integrated process-oriented framework for quick early screening valuation of chemical flooding opportunities. This study develops an integrated process-oriented framework for early screening and valuation, with an overall objective to quickly filter unfeasible projects based on valuation criteria, rather than technical feasibility alone. A reservoir-to-market model was developed, integrating information from laboratory experiments (phase behavior, core flood), field analogues (well performance and layout), facilities, rigs, costs, scheduling, and economics. Recently published ASP flood data of the central Xing2 area in Daqing, China was used for model inputs. A reservoir-to-market benchmark model for a typical mature onshore field was successfully built and tested, and could value projects using standard economic metrics (net present value, internal rate of return, value investment ratio, unit technical cost, and payback period). Model simplification was achieved through global sensitivity analysis. Using a mean-reversion oil price model, the oil price accounted for 98% of the total sensitivity. . Model efficiency was achieved through discretization of input parameter uncertainties, which sped the screening process. Decision-making between model alternatives given information and different states of nature was performed through decision-tree techniques based on overall project valuation. Overall, this study was novel and provided benefit as a robust, integrated process-oriented framework for chemical EOR project screening, valuation, and decision-making. / text

This thesis presents a framework for history matching and model calibration of surfactant-polymer (SP) flooding. At first, a high-fidelity mechanistic SP flood model is constructed by performing extensive lab-scale experiments on Berea cores. Then, incorporating Sobol based sensitivity analysis, polynomial chaos expansion based surrogate modelling (PCE-proxy) and Genetic algorithm based inverse optimization, an optimized model parameter set is determined by minimizing the miss-fit between PCE-proxy response and experimental observations for quantities of interests such as cumulative oil recovery and pressure profile. The epistemic uncertainty in PCE-proxy is quantified using a Gaussian regression process called Kriging. The framework is then extended to Bayesian calibration where the posterior of model parameters is inferred by directly sampling from it using Markov chain Monte Carlo (MCMC). Finally, a stochastic multi-objective optimization problem is posed under uncertainties in model parameters and oil price which is solved using a variant of Bayesian global optimization routine. <br>

Surfactant related recovery processes are of increasing interest and importance because of high oil prices and the urge to meet energy demand. High oil prices and the accompanying revival of EOR operations have provided academia and industry with great opportunities to test alkaline surfactant polymer (ASP) methods on a field scale and to develop novel surfactant systems that can improve the performance of such EOR processes. This dissertation intends to discuss both opportunities through two unique projects, the development of novel surfactants for EOR applications and the design for an alkaline/surfactant/polymer (ASP) field pilot. In Section I of this dissertation, a novel series of anionic Gemini surfactants are carefully synthesized and systematically investigated. The remarkable abilities of Gemini surfactants to influence oil-water interfaces and aqueous solution properties are fully demonstrated. These surfactants are shown to have great potential for application in EOR processes. A wide range of Gemini structures (C₁₄ to C₂₄ chain length, -C2- and -C4- spacers, sulfate and carboxylate head groups) was synthesized and shown to have high aqueous solubility, with Krafft points below 20°C. The critical micelle concentrations (CMC) for these new molecules are measured to be orders of magnitude lower than their conventional counterparts. The significantly more negative Gibbs free energy for Gemini surfactant drives the micellization process and results in ultralow CMC. An adsorption study of Gemini surfactants at air-water and solid-water interfaces shows their superior surface activity from tighter molecular packing, and attractive characteristics of low adsorption loss at the solid surface. All anionic Gemini surfactants synthesized have an extraordinary tolerance to salinity and/or hardness. No phase separation or precipitation occurs in the aqueous stability tests, even in the presence of extremely high concentrations of mono- and/or di-valent ions. Moreover, ultra-low IFT values are reached under these conditions for Type I microemulsion systems, at very low surfactant concentrations. The stronger molecular interaction between the Gemini and conventional surfactants offers synergy that promotes aqueous stability and interfacial activity. Gemini molecules with short spacers are capable of giving rise to high viscosities at fairly low concentrations. The rheological behavior can be explained by changes in the micellar structure. A molecular thermodynamic model is developed to study anionic Gemini surfactants aggregation behavior in solution. The model takes into account of the head group-counter-ion binding effect and utilizes two simplified solutions to the Poisson-Boltzmann equation. It properly predicts the CMC of the surfactants synthesized and can be easily expanded to investigate other factors of interest in the micellization process. Section II of this dissertation studies chemical formulation design and implementation for an oilfield where an alkaline/surfactant/polymer (ASP) pilot is being carried out. A four-step systematic design approach, composed of a) process and material selection; b) formulation optimization; c) coreflood validation; 4) lab-scale simulation, was successfully implemented and could be easily transferred to other EOR projects. The optimal chemical formulation recovered over 90% residual oil from Berea coreflood. Lab-scale simulation model accurately history matches the coreflood experiment and sets the foundation for pilot-scale numerical study. Different operating strategies are investigated using a pilot-scale model, as well as the sensitivities of project economics to various design parameters. A field execution plan is proposed based on the results of the simulation study. A surface facility conceptual design is put together based on the practical needs and conditions in the field. Key lessons learned throughout the project are summarized and are invaluable for planning and designing future pilot floods. / text

As easy target reservoirs are depleted around the world, the need for intelligent enhanced oil recovery (EOR) methods increases. The first part of this work is focused on modeling aspects of novel chemical EOR methods for naturally fractured reservoirs (NFR) involving wettability modification towards more water wet conditions. The wettability of preferentially oil wet carbonates can be modified to more water wet conditions using alkali and/or surfactant solutions. This helps the oil production by increasing the rate of spontaneous imbibition of water from fractures into the matrix. This novel method cannot be successfully implemented in the field unless all of the mechanisms involved in this process are fully understood. A wettability alteration model is developed and implemented in the chemical flooding simulator, UTCHEM. A combination of laboratory experimental results and modeling is then used to understand the mechanisms involved in this process and their relative importance. The second part of this work is focused on modeling surfactant/polymer floods using a fully implicit scheme. A fully implicit chemical flooding module with comprehensive oil/brine/surfactant phase behavior is developed and implemented in general purpose adaptive simulator, GPAS. GPAS is a fully implicit, parallel EOS compositional reservoir simulator developed at The University of Texas at Austin. The developed chemical flooding module is then validated against UTCHEM. / text

No description available.

Nanoparticulate drug delivery systems (NDDS) such as nanocrystals, nanosuspensions, solid-lipid nanoparticles often formulated for the bioavailability enhancement of poorly soluble drug candidates are stabilized by a mixture of excipients including surfactants and polymers. Most literature studies have focused on the interaction of excipients with the NDDS surfaces while ignoring the interaction of excipients in solution and the extent to which the solution-state interactions influence the affinity and capacity of adsorption. Mechanisms by which excipients stabilize NDDS and how this information can be utilized by formulators a priori to make a rational selection of excipients is not known. The goals of this dissertation work were (a) to determine the energetics of interactions between HPMC and model surfactants and the extent to which these solution-state interactions modulate the adsorption of these excipients onto solid surfaces, (b) to determine and characterize the structures of various aggregate species formed by the interaction between hydroxypropyl methylcellulose (HPMC) and model surfactants (nonionic and ionic) in solution-state, and (c) to extend these quantitative relationships to interpret probable mechanisms of mixed adsorption of excipients onto the model NDDS surface. A unique approach utilizing fluorescence, solution calorimetry and adsorption isotherms was applied to tease apart the effect of solution state interactions of polymer and surfactant on the extent of simultaneous adsorption of the two excipients on a model surface. The onset of aggregation and changes in aggregate structures were quantified by a fluorescence probe approach with successive addition of surfactant. In the presence of HPMC, the structures of the aggregates formed were much smaller with an aggregation number (Nagg) of 34 as compared to micelles (Nagg ~ 68) formed in the absence of HPMC. The strength of polymer-surfactant interactions was determined to be a function of ionic strength and hydrophobicity of surfactant. The nature of these structures was characterized using their solubilization power for a hydrophobic probe molecule. This was determined to be approximately 35% higher in the polymer-surfactant aggregates as compared to micelles alone and was attributed to a significant increase in the number of aggregates formed and the increased hydrophobic microenvironment within these aggregates at a given concentration of surfactant. The energetics of the adsorption of SDS, HPMC, and SDS-HPMC aggregate onto nanosuspensions of silica, which is the model solid surface were quantified. A strong adsorption enthalpy of 1.25 kJ/mol was determined for SDS adsorption onto silica in the presence of HPMC as compared to the negligible adsorption enthalpy of 0.1 kJ/mol for SDS alone on the silica surface. The solution depletion and HPMC/ELSD methods showed a marked increase in the adsorption of SDS onto silica in the presence of HPMC. However, at high SDS concentrations, a significant decrease in the adsorbed amount of HPMC onto silica was determined. This was further corroborated by the adsorption enthalpy that showed that the silica-HPMC-SDS aggregation process became less endothermic upon addition of SDS. This suggested that the decrease in adsorption of HPMC onto silica at high SDS concentrations was due to competitive adsorption of SDS-HPMC aggregates wherein SDS is displaced/desorbed from silica in the presence of HPMC. At low SDS concentrations, an increase in adsorption of SDS was due to cooperative adsorption wherein SDS is preferentially adsorbed onto silica in the presence of HPMC. This adsorption behavior confirmed the hypothesis that the solution-state interactions between pharmaceutical excipients such as polymers and surfactants would significantly impact the affinity and capacity of adsorption of these excipients on NDDS surfaces.

Understanding flow and transport in porous media is critical as it plays a central role in many biological, natural, and industrial processes. Such processes are not limited to one length or time scale; they occur over a wide span of scales from micron to Kilometers and microseconds to years. While field-scale simulation relies on a continuum description of the flow and transport, one must take into account transport processes occurring on much smaller scales. In doing so, pore-scale modeling is a powerful tool for shedding light on processes at small length and time scales.<br><br>In this work, we look into the multi-phase flow and transport through porous media at two different scales, namely pore- and Darcy scales. First, using direct numerical simulations, we study pore-scale Eulerian and Lagrangian statistics. We study the evolution of Lagrangian velocities for uniform injection of particles and numerically verify their relationship with the Eulerian velocity field. We show that for three porous media velocity, probability distributions change over a range of porosities from an exponential distribution to a Gaussian distribution. We thus model this behavior by using a power-exponential function and show that it can accurately represent the velocity distributions. Finally, using fully resolved velocity field and pore-geometry, we show that despite the randomness in the flow and pore space distributions, their two-point correlation functions decay extremely similarly.<br><br>Next, we extend our previous study to investigate the effect of viscoelastic fluids on particle dispersion, velocity distributions, and flow resistance in porous media. We show that long-term particle dispersion could not be modulated by using viscoelastic fluids in random porous media. However, flow resistance compared to the Newtonian case goes through three distinct regions depending on the strength of fluid elasticity. We also show that when elastic effects are strong, flow thickens and strongly fluctuates even in the absence of inertial forces.<br><br>Next, we focused our attention on flow and transport at the Darcy scale. In particular, we study a tertiary improved oil recovery technique called surfactant-polymer flooding. In this work, which has been done in collaboration with Purdue enhanced oil recovery lab, we aim at modeling coreflood experiments using 1D numerical simulations. To do so, we propose a framework in which various experiments need to be done to quantity surfactant phase behavior, polymer rheology, polymer effects on rock permeability, dispersion, and etc. Then, via a sensitivity study, we further reduce the parameter space of the problem to facilitate the model calibration process. Finally, we propose a multi-stage calibration algorithm in which two critically important parameters, namely peak pressure drop, and cumulative oil recovery factor, are matched with experimental data. To show the predictive capabilities of our framework, we numerically simulate two additional coreflood experiments and show good agreement with experimental data for both of our quantities of interest.<br><br>Lastly, we study the unstable displacement of non-aqueous phase liquids (e.g., oil) via a finite-size injection of surfactant-polymer slug in a 2-D domain with homogeneous and heterogeneous permeability fields. Unstable displacement could be detrimental to surfactant-polymer flood and thus is critically important to design it in a way that a piston-like displacement is achieved for maximum recovery. We study the effects of mobility ratio, finite-size length of surfactant-polymer slug, and heterogeneity on the effectiveness of such process by looking into recovery rate and breakthrough and removal times.