Abstract
Decline curve analysis (DCA) is a curve fitting procedure used for analyzing declining production rates and forecasting future performance of oil and gas wells. Certain types of standard curves are fit, based on the past production performance which are then extrapolated to predict the future well performance. It is the basic tool for estimating recoverable reserves and can be used when the production history is long enough that a decline trend can be identified.
One of the key technologies used in the process is an automated DCA methodology which can be automatically run on a machine without any human interaction and with minimum errors. The traditional DCA is derived by Arp's equation, where three types of decline curves have been identified: exponential, hyperbolic, and harmonic.
In this research work, authors have developed a python code that apply DCA methods to wells in an unbiased, systematic, and automated manner. This method contrasts with manual DCA, which was the widespread practice of the industry. As industry has entered digital transformation era, it is imperative that digital capabilities be coupled with conventional knowledge of DCA. This can be used as a handy tool to generate type curves for fitting decline curves and to further analyze using business intelligence (BI) tools, for a batch of oil wells under study.
