A comprehensive review of deterministic decline curve analysis for oil and gas reservoirs

Abstract

Decline curve analysis (DCA) is widely used to estimate the ultimate recovery of oil and gas reservoirs. Unlike numerical simulation, material balance, and advanced production analysis, the only required data is the production history, which is why it is considered the easiest, fastest, and less computationally intensive prediction tool for production performance. Several DCA models have been developed to simulate the proper production decline trend for conventional and unconventional wells. Each one has its structure, advantages, and limitations. The motivation was to improve the goodness of fitting and reliability of prediction for different reservoir types with different declining modes.DCA suffers from several uncertainties. Production data size, production data quality, estimating each model's parameters, and the assumptions of the used decline curve model are types of these uncertainties. Other challenges are related to the characteristics of the well and/or the reservoir such as the driving mechanisms, the flow regimes through the production life, and the operation conditions. Most of these challenges exist in unconventional reservoirs, especially shale gas.In this research, the DCA models and approaches for conventional and unconventional wells were summarized, reviewed, and criticized. The main characteristics of each model were presented. Also, the ranges of each model's parameters were addressed. Moreover, recommendations for effective uncertainty evaluation and DCA application were introduced.