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Abstract Decline curve analysis (DCA) serves as a fundamental technique in the realm of oil and gas production, particularly for assessing the diminishing production rates of wells and projecting their future performance. This method entails fitting standard curves to historical production data, enabling the identification of trends that reflect the decline in production over time. By extrapolating these established curves, DCA facilitates the prediction of future well performance. It stands as a pivotal tool in estimating recoverable reserves, especially applicable in scenarios where the production history spans a sufficiently long duration to discern discernible decline patterns. An integral technology employed in this process involves an automated Decline Curve Analysis (DCA) methodology, capable of autonomously executing on a machine with minimal human intervention and reduced error rates. The traditional DCA, rooted in Arp's equation, distinguishes three primary types of decline curves: exponential, hyperbolic, and harmonic. This research endeavor encompasses the development of a Python-based framework by the authors and comparison of different methods, aimed at applying Decline Curve Analysis (DCA) methods to wells in a manner characterized by impartiality, systematic execution, and automation. Various methodologies for automating decline curve analysis were explored, and their outcomes were meticulously compared. This innovative approach stands in stark contrast to the conventional practice of manual DCA, which has historically dominated the industry. As the oil and gas sector transitions into the digital transformation era, the fusion of digital capabilities with traditional expertise in DCA becomes increasingly imperative. Embracing this synergy facilitates not only enhanced efficiency and accuracy but also fosters a deeper understanding of reservoir behavior and production dynamics.
Nukala et al. (Tue,) studied this question.