Use of Artificial Intelligence in Well-Test Interpretation

This paper describes techniques for the identification of well-test interpretation models from pressure-derivative data. Artificial intelligence (Al) techniques are used to separate the derivative response from signal and differentiation noise, and a rule-based recognition system characterizes the response using a symbolic representation. The computer's choice of a model is based on the pressure-derivative curve and simulates the visual diagnosis performed by a human expert. The reasoning involved in such a diagnosis uses a symbolic representation of the derivative curve.