This paper describes two techniques for hyperbolic decline parameter identification. The first technique uses a genetic algorithm in the optimization procedure. The genetic algorithms are potentially useful in solving optimization problems when the objective function contains irregularities. The second technique uses linear regression for fitting a decline curve to data. The method weights equally the production rates during curve fitting, resulting in a stable solution. Consequently, the results are reproducible for a wide range of applications. Both methods were tested against field and literature data, demonstrating rapid, stable convergence and reproducible curves.

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