This paper presents a method for making prudent business and technical decisions for surfactant flooding projects in spite of the uncertainty associated with reservoir properties, surfactant flood performance, and oil price forecasts. A hypothetical surfactant flooding project is used to demonstrate this method and each algorithm is explained in detail.
This method optimizes the operational decisions of a surfactant flood and presents the economic assessment of the project. The accuracy of this assessment depends on the project. The accuracy of this assessment depends on the uncertainty in the project but this method quantifies both the affect of this uncertainty and the financial risk.
Surfactant flooding is used as the example in this study but the algorithms can be applied to all oil and gas development projects.
In the process of implementing a commercial surfactant flooding project, decisions must be made on both financial and technical issues. The principal financial questions are obvious:
Should we implement the project?
What measure of profitability should we design the project to maximize?
Designing the project to maximize profitability raises several engineering questions, for example:
What are the optimal sizes of the surfactant and polymer slugs?
What is the optimal spacing of wells?
It is difficult to establish optimal decisions because none of the existing surfactant flooding mathematical models can generate an exact prediction of the performance of a surfactant flood. Even if a perfect model existed, it would require data on reservoir description and surfactant properties that are not known exactly. Furthermore, even if the oil production profile could be predicted exactly, the profitability of the project would still be uncertain because future costs, oil prices and tax rates cannot be predicted exactly. Clearly, a method is required that helps us make optimal decisions in spite of uncertainty.
Earlier studies have recognized the need to apply optimization techniques to surfactant flooding. Ramirez et at and Fathi and Ramirez determined optimal surfactant concentration profiles for different kinds of surfactant. Harwell et al considered chromatographic movement of surfactant in porous media to determine optimal concentration and slug size. Vinatieri and Fleming used optimization to determine the composition of surfactant that would minimize interfacial tension (IFT). Brown and Smith have illustrated the uncertainties in surfactant flooding. These studies have concentrated either on the optimization problem or on the uncertainty involved. This study represents an effort to consider these two issues simultaneously.
The method presented here consists of three parts: a surfactant flooding mathematical model, an economics model, and algorithms that identify the optimal engineering decisions for the surfactant flooding model while accounting for uncertainty in the models and their input data. Each of these three parts are discussed but the emphasis is on the third part - how to optimize in spite of uncertainty. These algorithms will work with any models for surfactant flooding and economics.
This approach can be extended to other development projects by replacing the surfactant flooding model with a mathematical model that represents the appropriate recovery process.