Laboratory Experiment and Multivariate Regression Analysis of N2 Huff-n-Puff in Developing Carbonate Reservoirs

Abstract

Efficient development is a hot topic for carbonate reservoirs. The efficacy of conventional waterflooding diminishes over time due to challenges such as viscous fingering, fluid retention, and significant horizontal heterogeneity. N2 huff-n-puff (N2-HNP) operations are the essential method to enhance oil recovery after waterflooding. Taking the X Block of Fuman Oilfields in China as an example, this study leveraged Gaussian Process-Ridge Regression to investigate the impact of parameters on development performance. First, the regression model is built based on experimental data. Univariate analysis explores the effect of single parameters, while multivariate analysis provides an equation for recovery. The parameter importance evaluation ultimately shows that the number of cycles and injection pore volume are the dominant factors controlling the development performance. The results show that (a) the recovery rises to a peak before declining with depth, showing optimal production performance at the middle depths, (b) the recovery exhibits a positive but rapidly saturating correlation with injected pore volume, which suggests an optimal range of 1.0–1.2 PV, (c) the recovery shows a nonlinear response to production pressure difference and its highest sensitivity defines the recommended optimal range of 7.0–9.0 MPa, and (d) the recovery exhibits a nonlinear increase with cycle number, characterized by a rapid initial rise followed by a plateau. This study fills the gap in multivariate regression analysis of parameters for this reservoir type and offers a systematic approach for optimizing the development of similar complex reservoirs worldwide with significant scientific and practical value.