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An automated EoS tuning procedure using global optimization and physical constraints

Kanakaki Eirini-Maria

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Summary

The sampling process of petroleum fluids is a fundamental step in the development of a reservoir, as the subsequent laboratory PVT analysis provides a plethora of information regarding the thermodynamic behavior of a fluid. Due to the high cost of laboratory experiments, these are usually performed within a specific range of conditions (pressure and temperature) imposed by the reservoir itself. For this reason, a mathematical tool is required that can computationally predict the values of the required properties under a wide range of conditions expected to be encountered during the exploitation of the field both in the reservoir and in the wells. The most commonly used mathematical tool are the Equations of State (EoSs), the accuracy of which when applied to petroleum fluids is limited and can be optimized only if the equations are adjusted so that their predictions can adequately match the available measured PVT study values.In this Master Thesis, the algorithms for simulating the Constant Composition Expansion test, the Differential Liberation test and the Separator test, which are performed to characterize reservoir fluids, were developed in the Matlab programming environment from scratch. It is important to mention that the key elements of the simulation of these PVT tests, which are the stability, flash and saturation pressure algorithms, were also developed in Matlab. The automated EoS tuning procedure was then performed using a global optimization method, the pattern search method, instead of a gradient-based method that does not always guarantee to find a global minimum and can get stuck at a local minimum. In addition, physical constraints increasing the physical soundness of the model’s estimations were imposed. To test how efficient this approach is, one synthetic fluid and two real reservoir fluids were employed and the superiority of the pattern search method over the conventional gradient-based optimization method was confirmed.

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