Forecasting oil well performance in tight formation using the connected reservoir storage model

Abstract

Tight reservoir development requires complex fracture networks to economically produce hydrocarbons. The accurate prediction of a production well regarding this type of reservoir is not only critical for reservoir management but also challenging due to multiple and compound flow mechanisms. For wells producing from tight formations, curve fitting based forecasting models are destined to fail in the long term because they do not capture flow mechanisms or the physics governing fluid flow in the reservoir. Based on diffusivity theory, this paper presents the Connected Reservoir Storage Model (CRSM) employing only production rate and bottomhole flowing pressure (BHFP) to derive the reservoir behavior and can be used to predict production performance under variable operating conditions. This approach is easy to implement as the CRSM can be determined with no foreknowledge of reservoir geometry or reservoir parameters. The novelty of the CRSM is utilizing only production data to characterize extreme tight formations for performance forecasting under variable operational conditions. This paper will demonstrate the ability of the CRSM to predict production performance and compare the result to two classical empirical techniques currently used in unconventional reservoir predictions, and the validation is done via numerical simulation. Based on the CRSM, this paper also reveals the interrelated paradigms among well performance, increasing reservoir connected volume, and rate forecasts with uncertainties.