The advent of smart well technology, which is the use of down hole sensors to adjust well controls (i.e. injection rate, bottomhole pressure, etc.), has allowed the possibility to control a field in all stages of the production. This possibility holds great promise in better managing enhanced oil recovery (EOR) processes, especially in terms of applying optimization techniques. However, some procedures for optimizing EOR processes are not based on the physics of the process, which may lead to erroneous results. In addition, optimization of EOR processes can be difficult, and limited, if there is no access to the simulator code for computation of the adjoints used for optimization. This research describes the development of a general procedure for designing an initial starting point for a surfactant flood optimization. The method does not rely on a simulator's adjoint computation or on external computing of adjoints for optimization. The reservoir simulator used for this research was Schlumberger's Eclipse 100, and optimization was accomplished through use of a program written in Matlab. Utility of the approach is demonstrated by using it to optimize the process net present value (NPV) of a 5-spot surfactant flood (320-acres) and incorporating the optimization solution into a probabilistic geological and economic setting. This thesis includes a general procedure for optimizing a surfactant flood and provides groundwork for optimizing other EOR techniques. This research is useful because it takes the optimal solution and calculates a probability of success for possible NPVs. This is very important when accessing risk in a business scenario, because projects that have unknown probability of success are most likely to be abandoned as uneconomic. This thesis also illustrates possible NPVs if the optimal solution was used.

Spontaneous imbibition of water into Fontainebleau Sandstone matrix because of capillary gradient is an important mechanism for oil recovery from Fontainebleau Sandstone reservoirs. Spontaneous imbibition characteristics of Fontainebleau Sandstone core were determined by measuring the Wettability Index of four Fontainebleau Sandstone core samples under laboratory conditions. This was done by utilizing a combination of a Benchtop Relative Permeameter Flooding System and Amott Cups. The specimen had a diameter of 38mm and a height of 47mm. Permeability and porosity of the cores varied from 12 to 14 mD and 10 to 14% respectively. The fluids and chemicals used were kerosene, synthetic brine and Sodium dodecyl sulphate. Amott’s method was used to measure the wettability index. This method consists of four steps: (1) brine flooding, (2) spontaneous imbibition of brine, (3) kerosene flooding, (4) spontaneous imbibition of kerosene. One core was saturated with kerosene and then flooded with brine, followed by spontaneous imbibition of brine. Similarly, another core was saturated with brine and then flooded with kerosene, followed by spontaneous imbibition of kerosene. Similar procedures were used for other two cores except the addition of surfactant to the synthetic brine. All cores were then cleaned and re-saturated for spontaneous imbibition of kerosene and brine. All Experiments were performed under laboratory temperature conditions. Oil and water wettability values were obtained along with secondary and tertiary oil recoveries. These values were used to calculate the wettability index of Fontainebleau sandstone cores. Spontaneous imbibition characteristics of the cores obtained from the experimental data indicate that Fontainebleau Sandstone formation is a potential candidate for Secondary and Tertiary oil recovery by water injection and spontaneous imbibition.

Recently, oil prices and oil demand are rising and are projected to continue to rise over the long term. These trends create great potential for enhanced oil recovery methods that could improve the recovery efficiency of reservoirs all over the world. The greatest challenges for enhanced oil recovery involve the technical uncertainty with design and performance, and the high financial risk. Pilot tests can help mitigate the risk associated with such projects; however, there is a question about the value of information from the tests. Decision support can provide information about the value of an enhanced oil recovery project, which can assist with alleviating financial risk and create more potential opportunities for the technology. The first objective of this study is to create a new simplified method for modeling oil production histories of enhanced oil recovery methods. The method is designed to satisfy three criteria: 1) it allows for quick simulations based on only a few physically meaningful input parameters; 2) it can create almost any potential type of realistic production history that may be realized during a project; and 3) it applies to all nonthermal enhanced oil recovery methods, including surfactant-polymer, alkali-surfactant polymer, and CO₂ floods. The developed method is capable of creating realistic curves with only four unique parameters. The second objective is to evaluate the predictive method against data from pilot and field scale projects. The evaluations demonstrate that the method can fit most realistic production histories as well as provided ranges for the input parameters. A sensitivity analysis is also performed to assist with determining how all of the parameters involved with the predictive method and the economic model influence the forecasted value for a project. The analysis suggests that the price of oil, change in oil saturation, and the size of the reservoir are the most influential parameters. The final objective is to establish a method for a decision analysis that determines the value of information of a pilot for enhanced oil recovery. The analysis uses the predictive method and economic model for determining economic utilities for every potential outcome. It uses a decision-based method to ensure that the non-informative prior probability distributions have an unbiased, consistent, and rational starting point. A simple example demonstrating the process is discussed and it is used to show that a pilot test provides some valuable information when there is minimal prior information. For future work it is recommended that more evaluations are performed, the decision analysis is expanded to include more input parameters, and a rational and logical method is developed for determining likelihood functions from existing information. / text