Chemical flooding is one of the most difficult enhanced oil recovery methods and was considered a high-risk process in the past. Some reasons are low and uncertain oil price, high chemical prices, lack of confidence in performance of the chemical flooding process, long project life, and reservoir and process uncertainties. However, with significant improvement in simulation and optimization tools and high oil price, chemical flooding is feasible in terms of economical and carefully implemented design. Optimization of chemical floods requires complex integration of reservoir, chemical, economics properties and also drilling and production strategies. Many of these variables are uncertain parameters and many simulations are required to capture the effect of the uncertain and decision variables. These simulations could become very expensive and may not be feasible to consider all of the required simulation models. The goal of this research is the development of a methodology for optimization and design of chemical flooding of candidate oil reservoirs. We performed a comprehensive sensitivity study of reservoir and fluid properties that have significant influence on the oil production during the chemical flooding by performing a series of reservoir simulation runs. For performing the reservoir simulation runs, this study used the UT_IRSP platform and the multiphase, multicomponent, chemical flooding simulator called UTCHEM. During the study, UT_IRSP and UTCHEM have been modified by adding new modules, functions and variables. For example, a deviated well module was implemented in UTCHEM to study deviated wells. Deviated well module allows the users to introduce deviated wells in reservoir and import the well locations similar to Eclipse or CMG simulators. A time-dependent well schedule module was implemented in the UT_IRSP framework. This enhancement allows the well placement optimization studies to find the best time to add new wells, and change the status of the well for example from a producer to an injector in order to have an optimum development plan. An advanced post processing module was added to UT_IRSP in order to design, screen, and optimize complex cases for chemical enhanced oil recovery processes such as investigating the well patterns, well spacing, and type of the well (horizontal vs. vertical wells). An experimental design and response surface methodology with integrated economic model were utilized in this study to obtain the optimum design under uncertainties and have an optimal combination of the decision variables. This methodology is based on applying multi-regression analysis and ANOVA (analysis of variance) between the objective function (i.e. dependent variable, which is net present value (NPV) in chemical flooding) and other uncertain and process variables (independent variables). The economic analysis model used the discounted cash flow method to calculate net present value at the economic life of process, internal rate of return, and growth rate of return for each simulation case. Also the optimizer, OptQuest, is launched with a goal of maximizing the mean NPV. The range and the risk associated with the optimum design was studied using Monte Carlo simulation of objective function of the response variable and other independent variables. This methodology was applied for complex chemical flood cases such as well placement, change of status of wells as a function of time or well pattern and well spacing to investigate the best well scenario from recovery and economics point of view. / text

Chemical Enhanced Oil Recovery methods have been growing in popularity as a result of the depletion of conventional oil reservoirs and high oil prices. These processes are significantly more complex when compared to waterflooding and require detailed engineering design before field-scale implementation. Coreflood experiments that have been performed on reservoir rock are invaluable for obtaining parameters that can be used for field-scale flooding simulations. However, the design used in these floods may not always scale to the field due to heterogeneities, chemical retention, mixing and dispersion effects. Reservoir simulators can be used to identify an optimum design that accounts for these effects but uncertainties in reservoir properties can still cause poor project results if it not properly accounted for. Different reservoirs will be investigated in this study, including more unconventional applications of chemical flooding such as a 3md high-temperature, carbonate reservoir and a heterogeneous sandstone reservoir with very high initial oil saturation. The goal of the research presented here is to investigate the impact that select reservoir uncertainties can have on the success of the pilot and to propose methods to reduce the sensitivity to these parameters. This research highlights the importance of good mobility control in all the case studies, which is shown to have a significant impact on the economics of the project. It was also demonstrated that a slug design with good mobility control is less sensitive to uncertainties in the relative permeability parameters. The research also demonstrates that for a low-permeability reservoir, surfactant propagation can have a significant impact on the economics of a Surfactant-Polymer Flood. In addition to mobilizing residual oil and increasing oil recovery, the surfactant enhances the relative permeability and this has a significant impact on increasing the injectivity and reducing the project life. Injecting a high concentration of surfactant also makes the design less sensitive to uncertainties in adsorption. Finally, it was demonstrated that for a heterogeneous reservoir with high initial oil saturation, optimizing the salinity gradient will significantly increase the oil recovery and will also make the process less sensitive to uncertainties in the cation exchange capacity. / text

Operators can improve a reservoir’s value by optimizing it in a more holistic manner, or over its entire life cycle. This thesis developed approaches to life cycle optimization, with emphasis on accessible technical and economic modeling techniques for production. The challenges of life cycle optimization are properly scheduling the times at which the operator should switch from one recovery phase to the next, along with determining other field design parameters such as well spacing and injection pressures for waterflooding and enhanced oil recovery processes. To deliver the most value, the operator needs to produce from a reservoir the greatest quantity of oil, at a relatively low cost, reasonably soon, and ideally at a time when the oil price is high. This is quite a tall order, as these goals are often in conflict. This thesis extended existing research regarding lifecycle optimization, first modeling production from a reservoir using an exponential decline model and assuming the oil price’s behavior can be approximated with mean-reverting processes. Implications of operating and capital costs potentially being correlated with the oil price were also examined. Finally, a mean-reverting price model that forecasts the mean oil price as increasing and described by a logistic model was proposed to accommodate both recent price forecasts and economic reality. As exponential decline models are more appropriate for characterizing existing production history rather than making a priori predictions, a geologic-parameter-based model was developed using a tank model for primary recovery and a model based on Koval theory and parameterizing a reservoir in terms of flow capacity and storage capacity for waterflooding and CO2 flooding. This model was adapted from existing theory to account for situations where a waterflood has incompletely swept a reservoir at the start of CO2 flooding. Analytical expressions were also derived for estimating injection rates into a formation parameterized by flow capacity and storage capacity. The geologic-parameter-based model was combined with economic assumptions and optimized using a genetic algorithm. This optimization suggested an operator should switch from primary recovery to a CO2 flood with a large WAG ratio relatively early in the reservoir’s life. / text

The effect of brine ionic composition on oil recovery was studied for a limestone reservoir rock at a high temperature. Contact angle, imbibition, core flood and ion analysis were used to find the brines that improve oil recovery and the associated mechanisms. Contact angle experiments showed that modified seawater containing Mg[superscript 2+] and SO4[superscript 2-] and diluted seawater change aged oil-wet calcite plates to more water-wet conditions. Seawater with Ca[superscript 2+], but without Mg[superscript 2+] or SO₄[superscript 2-] was unsuccessful in changing calcite wettability. Modified seawater containing Mg[superscript 2+] and SO₄[superscript 2-], and diluted seawater spontaneously imbibe into the originally oil-wet limestone cores. Modified seawater containing extra SO₄[superscript 2-] and diluted seawater improve oil recovery from 40% OOIP (for formation brine waterflood) to about 80% OOIP in both secondary and tertiary modes. The residual oil saturation to modified brine injection is approximately 20%. Multi ion exchange and mineral dissolution are responsible for desorption of organic acid groups which lead to more water-wet conditions. Further research is needed for scale-up of these mechanisms from cores to reservoirs. / text

Polymer flooding is a commercial enhanced oil recovery (EOR) method used to increase the sweep efficiency of water floods. Hydrolyzed polyacrylamide (HPAM), a synthetic commercial polymer, is widely used in commercial polymer floods and it is also used for mobility control of chemical floods using surfactants such as surfactant-polymer flooding and alkaline-surfactant-polymer flooding. The increase in the degree of hydrolysis of HPAM at elevated temperature or pH with time affects the polymer solution viscosity and its adsorption on rock surfaces. A polymer hydrolysis model based on published laboratory data was implemented in UTCHEM, a chemical EOR simulator, in order to assess the effect of hydrolysis on reservoir performance. Both 1D and 3D simulations were performed to validate the implementation of the model. The simulation results are consistent with the laboratory observations that show an increase in polymer solution viscosity as hydrolysis progresses. The numerical results indicate that hydrolysis occurs very rapidly and impacts the near wellbore region polymer injectivity. / text

Chemical flooding has been studied for 50 years. However, never have the conditions encouraging its growth been as good as right now. Those conditions being new, improved technology and oil prices high enough to make implementation economical. The objective of this work was to develop economical, robust chemical formulations and processes that recover oil in field pilots when properly implemented. This experimental study goes through the process of testing surfactants to achieve optimal phase behavior, coreflooding with the best chemical formulations, improving the formulation and testing it in more corefloods, and then finally recommending the formulation to be tested in a field pilot. The target reservoir contains a light (34° API, 10 cP), non-reactive oil at about 22° C. The formation is a moderate permeability (50 - 300 mD) sandstone with a high clay content (up to 13%). Different surfactants and surfactant mixtures were tested with the oil including alkyl benzene sulfonates (ABS), Guerbet alcohol sulfates (GAS), alkyl propoxy sulfates, and internal olefin sulfonates (IOS). The best formulation contained 0.75% TDA -13PO-SO₄, 0.25% C₂₀₋₂₄ IOS, 0.75% isobutanol (IBA), 1% Na₂CO₃, all which are mixed in a softened fresh water from a supply well. Corefloods recovered 93% of residual oil from reservoir cores. Core flood experiments were also done with the alkali sodium carbonate to measure the effluent pH in a Bentheimer sandstone core with a cation exchange capacity (CEC) of 2 meq/100g. Floods at frontal velocities of 100, 10, and 0.33 ft/D were performed with 0.3 pore volume slugs of 0.7% Na₂CO₃ at 86° C. The effluent was analyzed for ions and pH breakthrough. It was found that the pH breakthrough occurred before surfactant breakthrough would be expected as desired although the pH was lower at a frontal velocity of 0.33 ft/D than at the higher velocities. The Na₂CO₃ consumption was 0.244, 0.238, and 0.207 meq/100 g rock at velocities of 100, 10, and 0.33 ft/D, respectively. In addition, a no-alkaline formulation consisting of a new large hydrophobe ether carboxylate surfactant mixed with an internal olefin sulfonate was tested on an active oil and it successfully recovered 99% of the waterflood remaining oil from an Ottawa sand pack with no salinity gradient and no alkali. The final residual oil saturation after the chemical flood (S[subscript orc]) was only 0.005 / text

In this study, the co-optimization of carbon dioxide sequestration and enhanced oil recovery and the co-optimization of carbon dioxide sequestration and methane recovery studies were discussed. Carbon dioxide emissions in the atmosphere are one of the reasons of global warming and can be decreased by capturing and storing carbon dioxide. Our aim in this study is to maximize the amount of carbon dioxide sequestered to decrease carbon dioxide emissions in the atmosphere and maximize the oil or methane recovery to increase profit or to make a project profitable. Experimental design and response surface methodology are used to co-optimize the carbon dioxide sequestration and enhanced oil recovery and carbon dioxide sequestration and methane recovery. At the end of this study, under which circumstances these projects are profitable and under which circumstances carbon dioxide sequestration can be maximized, are given. / text

Despite bold steps taken worldwide for the replacement or the reduction of the world’s dependence on fossil fuels, economic and societal realities suggest that a transition to alternative energy forms will be, at best, gradual. It also appears that exploration for new reserves is becoming increasingly more difficult both from a technical and an economic point of view, despite the advent of new technologies. These trends place renewed emphasis on maximizing oil recovery from known fields. In this sense, low-cost and reliable enhanced oil recovery (EOR) methods have a strong role to play. The goal of this dissertation is to explore, using computational simulations, the feasibility of the, so-called, seismic or elastic-wave EOR method, and to provide the mathematical/computational framework under which the method can be systematically assessed, and its feasibility evaluated, on a reservoir-specific basis. A central question is whether elastic waves can generate sufficient motion to increase oil mobility in previously bypassed reservoir zones, and thus lead to increased production rates, and to the recovery of otherwise unexploited oil. To address the many questions surrounding the feasibility of the elastic-wave EOR method, we formulate an inverse source problem, whereby we seek to determine the excitations (wave sources) one needs to prescribe in order to induce an a priori selected maximization mobility outcome to a previously well-characterized reservoir. In the industry’s parlance, we attempt to address questions of the form: how does one shake a reservoir?, or what is the “resonance” frequency of a reservoir?. We discuss first the case of wellbore wave sources, but conclude that surface sources have a better chance of focusing energy to a given reservoir. We, then, discuss a partial-differential-equation-constrained optimization approach for resolving the inverse source problem associated with surface sources, and present a numerical algorithm that robustly provides the necessary excitations that maximize a mobility metric in the reservoir. To this end, we form a Lagrangian encompassing the maximization goal and the underlying physics of the problem, expressed through the side imposition of the governing partial differential equations. We seek to satisfy the first-order optimality conditions, whose vanishing gives rise to a systematic process that, in turn, leads to the prescription of the wave source signals. We explore different (indirect) mobility metrics (kinetic energy or acceleration field maximization), and report numerical experiments under three different settings: (a) targeted formations within one-dimensional multi-layered elastic solids system of semi-infinite extent; (b) targeted formations embedded in a two-dimensional semi-infinite heterogeneous elastic solid medium; and (c) targeted poroelastic formations embedded within elastic heterogeneous surroundings in one dimension. The numerical experiments, employing hypothetical subsurface formation models subjected to, initially unknown, ground surface wave sources, demonstrate that the numerical optimizer leads robustly to optimal loading signals and the illumination of the target formations. Thus, we demonstrate that the theoretical framework for the elastic wave EOR method developed in this dissertation can systematically address the application of the method on a reservoir-specific basis. From an application point of view and based on the numerical experiments reported herein, for shallow reservoirs there is strong promise for increased production. The case of deeper reservoirs can only be addressed with further research that builds on the findings of this work, as we report in the last chapter. / text

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