Single-phase inflow performance relationship in stress-sensitive reservoirs

  For stress-sensitive reservoirs, understanding the characteristics of the inflow performance relationship is vital for evaluating the performance of a well and designing an optimal stimulation. In this study, a reservoir simulator was used to establish the inflow performance relationship of a well for a wide variety of reservoirs and wellbore conditions. First, a base case was simulated using typical reservoir, wellbore, and fluid parameters. Subsequently, variations from the base case were investigated. The results of the simulation indicate that the dimensionless inflow performance relationship in the stress-sensitive reservoir is similar to the Vogel-type inflow performance relationship, which is used for evaluating the productivity of a vertical well in a solution-gas-drive reservoir. Unlike the two-phase flow in a solution-gas-drive reservoir, the nonlinear characteristic of the inflow performance relationship in stress-sensitive reservoirs is caused by stress-dependent permeability. Furthermore, the stress sensitivity level is the only parameter that affects the nonlinearity coefficient of the dimensionless inflow performance relationship equation. The nonlinearity coefficient was plotted against the stress sensitivity index, and the nonlinearity coefficient was found to be linearly proportional to the stress sensitivity index. This study provides a realistic and less expensive methodology to evaluate the reservoir productivity of stress-sensitive reservoirs when the reservoir stress sensitivity level is known and to predict the reservoir stress sensitivity level when the inflow performance relationship of the stress-sensitive reservoirs is known.Cited as: Wang, F., Gong, R., Huang, Z., Meng, Q., Zhang, Q., Zhan, S. Single-phase inflow performance relationship in stress-sensitive reservoirs. Advances in Geo-Energy Research, 2021, 5(2): 202-211, doi: 10.46690/ager.2021.02.0

History Matching and Production Prediction of Steam Drive Reservoir Based on Data-Space Inversion Method

Recently, a data-space inversion (DSI) method has been proposed and successfully applied for the history matching and production optimization for conventional waterflooding reservoir. Under Bayesian framework, DSI can directly and effectively obtain posterior flow predictions without inverting any geological parameters of reservoir model. In this paper, we integrate the numerical simulation model with DSI method for rapid history matching and production prediction for steam flooding reservoir. Based on the finite volume method, a numerical simulation model is established and it is used to provide production data samples for DSI by the simulation of ensemble geological models. DSI-based production prediction model is then established and get trained by the historical data through the random maximum likelihood principle. The posterior production estimation can be obtained fast by training the DSI-based model with history data, but without any posterior geological parameters. The proposed method is applied for history matching and estimating production performance prediction in some numerical examples and a field case, and the results prove its effectiveness and reliability