Abstract
To perform an optimization study for a green field (newly discovered field), one must collect the information from different parts of the field and integrate these data as accurately as possible in order to construct the reservoir image. Once the image, or alternate images, are constructed, reservoir simulation allows prediction of dynamic performance of the reservoir. As field development progresses, more information becomes available, enabling us to continually update and, if needed, correct the reservoir description. The simulator can then be used to perform a variety of exercises or scenarios, with the goal of optimizing field development and operation strategies. We are often confronted with important questions related to the most efficient well spacing and location, the optimum number of wells needed, the size of the production facility needed, the optimum production strategies, the location of the external boundaries, the intrinsic reservoir properties, the predominant recovery mechanism, the best time and location to employ infill drilling and the best time and type of the improved recovery technique we should implement. These are some of the critical questions we may need to answer.
A reservoir simulation study is the only practical means by which we can design and run tests to address these questions in sufficient detail. From this perspective, reservoir simulation is a powerful screening tool. The magnitude, time and complexity of a reservoir simulation problem depends in part on the available computational environment. For instance, simple material balance calculations are now routinely performed on desktop personal computers, while running a field-scale three-dimensional simulator may call for the use of a supercomputer and may take many days to finish. We must also take into account the storage requirements and limitations, CPU time demand and the general architecture of the machine. The problem arises when there is a large amount of data available with a study objective that requires running several scenarios incorporating millions of grid cells. This will limit the applicability of reservoir simulation as it will be computationally very inefficient. For example, determining the optimum well locations in a field that will result in the most efficient production rate scenario requires a large number of simulation runs which can make it very inefficient. This is because one will have to consider multiple well scenarios in multiple realizations. The main purpose of this paper is to use a novel methodology known as the Fast Marching Method (FMM) to find the optimum well locations in a green oil field that will result in the most efficient production rate scenario. The concept of radius of investigation is fundamental to well test analysis. The current well test analysis relies on analytical solutions based on homogeneous or layered reservoirs. The FMM will enable us to calculate the radius of investigation or pressure front as a function of time without running any simulation and with a high degree of accuracy. The calculations can be done in a matter of seconds for multi-millions of cells.