This work proposes a new asymptotic-perturbative solution for transient pores collapse hysteresis modeling in depletion-dependent oil reservoirs with compaction effects during alternating loading/unloading cycles. The nonlinear hydraulic diffusivity equation (NHDE) is perturbed through a first-order expansion technique using the depletion-dependent permeability, k(p) as a perturbation parameter, ϵ. A new hysteretic deviation factor is presented for flow (drawdown) and well-shut (buildup) periods. This factor represents the permeability deviation during the drawdown and its partial restoration in the buildup period. The model calibration is performed by a porous media numerical oil flow simulator named IMEX®, and the results presented high accuracy for the drawdown and buildup of hysteretic and non-hysteretic cases (when the pores collapse did not occur yet). The practical uses of the model developed in this work are related to identifying flow regimes and hysteresis responses in pressure-sensitive reservoirs, estimating buildup pressure, and oil flow rates specification to prevent severe hysteretic behavior and history matching during reservoir surveillance. Two case studies related to different reservoir layers are researched, and for both cases, the results from the log–log plots analysis have shown that the infinite-acting-radial oil flow (1/2 value of the slope of the pseudo-pressure derivative) was identified. In addition, the log–log analysis also shows that the shut-in pressure has an influence on permeability loss. The main advantages of the solution derived in this work are the simple implementation, practical graphical analysis of the pores collapse hysteresis effect, the possibility of simulating different boundary conditions and well-reservoir settings, and the requirements of only a few pressure and permeability field data to input in the deviation factor. The solution proposed can be applied to choose the production time to shut the well and monitor the adequate oil flow rate during the production curve.