Probabilistic Decline Curve Analysis
Fully automated probabilistic DCA leveraging state-of-the-art machine learning and data assimilation approaches.
Fully automated probabilistic DCA leveraging state-of-the-art machine learning and data assimilation approaches.
Estimate P10-P50-P90 decline curves accounting for data noise and uncertainty
Automatically remove outliers for more robust and reliable decline curve analysis
Use traditional Arp’s decline models or many more advanced models
Use waterflood specific (WOR vs Np) or unconventional specific models (Duong, etc.)
Automatically detect well events and use superposition to build more reliable models
Fully automated model fitting, hundreds of wells can be fit in minutes
Correct decline curve analysis for changing bottom hole pressures
Perform DCA at well level, layer level or on arbitrary aggregations
Can run locally or on Tachyus Cloud, with no infrastructure footprint
Data can be exported easily to other visualization and analysis tools
Decline curve analysis (DCA) is one of the oldest methodologies used by production and reservoir engineers to predict future production, calculate reserves and estimate ultimate recovery, and it is fundamental to economic valuation of oil and gas assets. In practice, DCA is usually performed using deterministic tools that provide a unique solution ignoring the probabilistic nature of the production data due to measurement and back allocation errors.
Leveraging state-of-the-art machine learning and data assimilation approaches with traditional and advanced decline models , Tachyus’ Probabilistic Decline Curve Analysis (pDCA) creates an ensemble of models to automatically fit the historical data and estimate future production and reserves in a probabilistic manner providing P10-P50-P90 estimates of these entities. Additionally, Tachyus pDCA automatically identifies and removes outliers, improving the accuracy of the models, can also correct for BHP variations and results can be aggregated to arbitrary groups of wells.