Efficient Optimization and Robust Value Quantification of Enhanced Oil Recovery Strategies

With an increasing demand for hydrocarbon reservoir produces such as oil, etc., and difficulties in finding green oil fields, the use of Enhanced Oil Recovery (EOR) methods such as polymer, Smart water, and solvent flooding for further development of existing fields can not be overemphasized. For reservoir profitability and reduced environmental impact, it is crucial to consider appropriate well control settings of EOR methods for given reservoir characterization. Moreover, finding appropriate well settings requires solving a constrained optimization problem with suitable numerical solution methods. Conventionally, the solution method requires many iterations involving several computationally demanding function evaluations before convergence to the appropriate near optimum. The major subject of this thesis is to develop an efficient and accurate solution method for constrained optimization problems associated with EOR methods for their value quantifications and ranking in the face of reservoir uncertainties. The first contribution of the thesis develops a solution method based on the inexact line search method (with Ensemble Based Optimization (EnOpt) for approximate gradient computation) for robust constrained optimization problems associated with polymer, Smart water, and solvent flooding. Here, the objective function is the expectation of the Net Present Value (NPV) function over given geological realizations. For a given set of well settings, the NPV function is defined based on the EOR simulation model, which follows from an appropriate extension of the black-oil model. The developed solution method is used to find the economic benefits and also the ranking of EOR methods for different oil reservoirs developed to mimic North Sea reservoirs. Performing the entire optimization routine in a transformed domain along with truncations has been a common practice for handling simple linear constraints in reservoir optimization. Aside from the fact that this method has a negative impact on the quality of gradient computation, it is complicated to use for non-linear constraints. The second contribution of this thesis proposes a technique based on the exterior penalty method for handling general linear and non-linear constraints in reservoir optimization problems to improve gradient computation quality by the EnOpt method for efficient and improved optimization algorithm. Because of the computationally expensive NPV function due to the costly reservoir simulation of EOR methods, the solution method for the underlying EOR optimization problem becomes inefficient, especially for large reservoir problems. To speedup the overall computation of the solution method, this thesis introduces a novel full order model (FOM)-based certified adaptive machine learning optimization procedures to locally approximate the expensive NPV function. A supervised feedforward deep neural network (DNN) algorithm is employed to locally create surrogate model. In the FOM-based optimization algorithm of this study, several FOM NPV function evaluations are required by the EnOpt method to approximate the gradient function at each (outer) iteration until convergence. To limit the number FOM-based evaluations, we consider building surrogate models locally to replace the FOM based NPV function at each outer iteration and proceed with an inner optimization routine until convergence. We adapt the surrogate model using some FOM-based criterion where necessary until convergence. The demonstration of methodology for polymer optimization problem on a benchmark model results in an improved optimum and found to be more efficient compared to using the full order model optimization procedures

Aircraft Modeling and Simulation

Various aerodynamics, structural dynamics, and control design and experimental studies are presented with the aim of advancing green and morphing aircraft research. The results obtained with an in-house CFD code are compared and validated with those of two NASA codes. The aerodynamical model of the UAS-S45 morphing wing as well as the structural model of a morphing winglet are presented. A new design methodology for oleo-pneumatic landing gear drop impact dynamics is presented as well as its experimental validation. The design of a nonlinear dynamic inversion (NDI)-based disturbance rejection control on a tailless aircraft is presented, including its validation using wind tunnel tests

Adaptive machine learning-based surrogate modeling to accelerate PDE-constrained optimization in enhanced oil recovery

In this contribution, we develop an efficient surrogate modeling framework for simulation-based optimization of enhanced oil recovery, where we particularly focus on polymer flooding. The computational approach is based on an adaptive training procedure of a neural network that directly approximates an input-output map of the underlying PDE-constrained optimization problem. The training process thereby focuses on the construction of an accurate surrogate model solely related to the optimization path of an outer iterative optimization loop. True evaluations of the objective function are used to finally obtain certified results. Numerical experiments are given to evaluate the accuracy and efficiency of the approach for a heterogeneous five-spot benchmark problem.publishedVersio

A Comprehensive Study Of Esterification Of Free Fatty Acid To Biodiesel In a Simulated Moving Bed System

Simulated Moving Bed (SMB) systems are used for separations that are difficult using traditional separation techniques. Due to the advantage of adsorption-based chromatographic separation, SMB has shown promising application in petrochemical and sugar industries, and of late, for chiral drug separations. In recent years, the concept of integration of reaction and in-situ separation in a single unit has achieved considerable attention. The simulated moving bed reactor (SMBR) couples both these unit operations bringing down the operation costs while improving the process performance, particularly for products that require mild operating conditions. However, its application has been limited due to complexity of the SMBR process. Hence, to successfully implement a reaction in SMB, a detailed understanding of the design and operating conditions of the SMBR corresponding to that particular reaction process is necessary. Biodiesel has emerged has a viable alternative to petroleum-based diesel as a renewable energy source in recent years. Biodiesel can be produced by esterification of free fatty acids (present in large amounts in waste oil) with alcohol. The reaction is equilibrium-limited, and hence, to achieve high purity, additional purification steps increases the production cost. Therefore, combining reaction and separation in SMBR to produce high purity biodiesel is quite promising in terms of bringing down the production cost. In this work, the reversible esterification reaction of oleic acid with methanol catalyzed by Amberlyst 15 resin to form methyl oleate (biodiesel) in SMBR has been investigated both theoretically and experimentally. First, the adsorption and kinetic constants were determined for the biodiesel synthesis reaction by performing experiments in a single column packed with Amberlyst 15, which acts as both adsorbent and catalyst. Thereafter, a rigorous model was used to describe the dynamic behaviour of multi-column SMBR followed by experimental verification of the mathematical model. Sensitivity analysis is done to determine robustness of the model. Finally, a few simple multi-objective optimization problems were solved that included both existing and design-stage SMBRs using non-dominated sorting genetic algorithm (NSGA). Pareto-optimal solutions were obtained in both cases, and moreover, it was found that the performance of the SMBR could be improved significantly under optimal operating conditions

A unified metaheuristic and system-theoretic framework for petroleum reservoir management

With phenomenal rise in world population as well as robust economic growth in China, India and other emerging economies; the global demand for energy continues to grow in monumental proportions. Owing to its wide end-use capabilities, petroleum is without doubt, the world’s number one energy resource. The present demand for oil and credible future forecasts – which point to the fact that the demand is expected to increase in the coming decades – make it imperative that the E&P industry must device means to improve the present low recovery factor of hydrocarbon reservoirs. Efficiently tailored model-based optimization, estimation and control techniques within the ambit of a closed-loop reservoir management framework can play a significant role in achieving this objective. In this thesis, some fundamental reservoir engineering problems such as field development planning, production scheduling and control are formulated into different optimization problems. In this regard, field development optimization identifies the well placements that best maximizes hydrocarbon recovery, while production optimization identifies reservoir well-settings that maximizes total oil recovery or asset value, and finally, the implementation of a predictive controller algorithm which computes corrected well controls that minimizes the difference between actual outputs and simulated (or optimal) reference trajectory. We employ either deterministic or metaheuristic optimization algorithms, such that the choice of algorithm is purely based on the peculiarity of the underlying optimization problem. Altogether, we present a unified metaheuristic and system-theoretic framework for petroleum reservoir management. The proposed framework is essentially a closed-loop reservoir management approach with four key elements, namely: a new metaheuristic technique for field development optimization, a gradient-based adjoint formulation for well rates control, an effective predictive control strategy for tracking the gradient-based optimal production trajectory and an efficient model-updating (or history matching) – where well production data are used to systematically recalibrate reservoir model parameters in order to minimize the mismatch between actual and simulated measurements. Central to all of these problems is the use of white-box reservoir models which are employed in the well placement optimization and production settings optimization. However, a simple data-driven black-box model which results from the linearization of an identified nonlinear model is employed in the predictive controller algorithm. The benefits and efficiency of the approach in our work is demonstrated through the maximization of the NPV of waterflooded reservoir models that are subject to production and geological uncertainty. Our procedure provides an improvement in the NPV, and importantly, the predictive control algorithm ensures that this improved NPV are attainable as nearly as possible in practice

System Design and Optimization of CO2 Storage in Deep Saline Aquifers

Optimization of waterflooding sweep efficiency has been widely applied in reservoir engineering to improve hydrocarbon recovery while delaying water breakthrough and minimizing the bypassed oil in reservoirs. We develop a new framework to optimize flooding sweep efficiency in geologic formations with heterogeneous properties and demonstrate its application to waterflooding and geological CO2 sequestration problems. The new method focuses on equalizing and delaying (under constant total injected volume) the breakthrough time of the injected fluid at production wells. For application to CO2 sequestration where producers may not be present, we introduce the concept of pseudo production wells that have insignificant production rates (with negligible effect on the overall flow regime) for quantification of hypothetical breakthrough curves that can be used for optimization purpose. We apply the new method to waterflooding and CO2 sequestration optimization using two heterogeneous reservoir models. We show that in water flooding experiments, the proposed method improves the sweep efficiency by delaying the field breakthrough and equalizing breakthrough times in all production wells. In this case, the optimization results in increased oil recovery and decreased water production. We apply a modified version of the proposed algorithm to geologic CO2 sequestration problems to maximize the storage capacity of aquifers by enhancing the residual and dissolution trapping. The results from applying the proposed approach to optimization of geologic CO2 storage problems illustrate the effectiveness of the algorithm in improving residual and solubility trapping by increasing the contact between available fresh brine and the injected CO2 plume through a more uniform distribution of CO2 in the aquifer