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The origin of solving reservoir production problems using optimization theories can be traced back to Lee and Aronofsky (1958).
The complexity of adjoint-based algorithms in optimal well placement is similar to solving reservoir dynamic equations, which is the major drawback of the method.
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Applied reservoir simulation is the use of these programs to solve reservoir flow problems.
Artificial neural networks (ANN) have recently been introduced to solve reservoir characterization problems dealing with the complex underlying relationships inherent in well log data.
The Multiscale Finite-Volume (MsFV) method has been developed over the last decade to efficiently solve large reservoir models.
The diffusivity equation in the model is solved by source functions, which has been widely practiced in solving the unsteady reservoir flow problems to analyze transient pressure behaviors of horizontal and multilateral wells.
Reservoir-simulation technology, as a useful tool of reservoir development, has the capability to provide us with an economic means to solve complex reservoir-engineering problems with efficiency.
In practice, the optimal flow split would be very difficult to predict, as one would first need to know the structure of the reservoir from solving an inverse problem based on tracer tests.
Therefore, the knowledge of pressure volume temperature (PVT) relationships and other physical and chemical properties of gases are essential for solving problems in natural gas reservoir engineering.
As the first step to underline the utility of the GT RL method in solving complex multi-agent multi-reservoir problems without a need for developing compound objectives and weight assignment, the proposed method is applied to a hypothetical three-agent three-reservoir system.
Open image in new window Fig. 3 Flowchart for solving numerical model of low-permeability reservoir.
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