A prediction model for new well deliverability in an underground gas storage facility using production data. (April 2023)

• Record Type: Journal Article
• Authors: Liu, Xianshan; Tang, Huan; Zhang, Dongxu; Geng, Shaoyang; Wu, Gang; Li, Chengyong; Liu, Shudong
• Is Part Of: Journal of energy storage. Volume 60(2023)
• Publication Date: 2023-04
• Subjects: Dual-porous medium -- Petrophysical parameters interpretation -- Tight gas reservoir storage facility (TGRSF) -- Deliverability prediction -- Advanced production decline analysis; Energy storage -- Periodicals; Energy storage -- Research -- Periodicals; 621.3126
• DOI: 10.1016/j.est.2023.106649
• Languages: English
• ISSNs: 2352-152X

Abstract

Abstract: Natural gas is a naturally occurring fluid mixture consisting mainly of methane. Natural gas flows in the porous medium of a tight gas reservoir storage facility (TGRSF), factors such as a short operating period, high gas flow velocity, and small gas drainage radius could lead to apparent changes in the rock's physical properties, such as pressure and permeability. Porous media with changing petrophysical parameters will experience changes in fluid flow. The failure of existing methods to consider these factors has led to significant errors in predicting the deliverability of a new well in a TGRSF. In this study, a prediction model for the deliverability of new wells in a TGRSF that considers the stress-sensitive effect of permeability is proposed. The semi-analytical solution of the model is achieved via mathematical methods, such as the Laplace transform and perturbation theory, and verified by an industrial simulator (Ecrin-KAPPA). The solution results show that the dimensionless pseudo-pressure has a linear relationship with the dimensionless time in the late asymptotic solution. The material balance time is redefined based on this linear relationship, which can be used to calculate the equivalent relationship between producing at a constant rate and a variable rate. Combined with the equivalent relationship, we draw a series of typical curves to fit the petrophysical parameters within the gas drainage radius of existing wells and extract the petrophysical parameters of the new well. The model successfully predicts the deliverability of a new well (QK3) in China's TGRSF, with a peak deliverability of 18.76 million m 3 . The research results obtained in this paper can provide theoretical guidance for the optimal design of the number of new wells and required investments in the TGRSF. Highlights: A new prediction model was proposed for new well deliverability in a tight gas reservoir storage facility (TGRSF). The new model considers factors such as a short operating period, high gas flow velocity, and a small gas drainage radius. Pressure-normalized rate integral curves are plotted according to the model. Only the production data are required to determine the unsolved petrophysical parameters by fitting the typical curves. Petrophysical parameters near the new well are extracted from the existing profile to predict a new well's deliverability.