Although gas desorption is a known phenomenon, modeling fluid flow in tight gas reservoirs often ignores the governing desorption effect, assuming that viscous transport is the predominant controller, resulting in an erroneous prediction of mass transport and fluid flow calculations. Thus, developing a new model accommodating all the major contributing forces in such a medium is essential. This work introduces a new comprehensive flow model suitable for tight unconventional reservoirs, including viscous, inertia, diffusion, and sorption forces, to account for fluid transport. Based on Langmuir law and Knudsen diffusion effect, three models were generated and compared with different known models using synthetic data. The model was solved and analyzed for different scenario cases, and parametric studies were conducted to evaluate the desorption effect on different reservoir types using MATLAB. Results show that the contribution of the sorption mechanism to the flow increases with the reducing permeability of the medium and lower viscosity of the flowing fluid and an additional pressure drop up to 10 psi was quantified.

A model for single-phase fluid flow in tight UCRs was previously produced by modifying the flow Forchheimer’s equation. The new modification addresses the fluid transport phenomena into three scales incorporating a diffusion term. In this study, a new liner model, numerically solved, has been developed and deployed for a gas huff and puff project. The new model has been numerically validated and verified using synthetic data and huff and puff case study. Ideally, the new model suits fluid flow in tight UCRs. The modified Forchheimer’s model presented is solved using the MATLAB numerical method for linear multiphase flow. For the huff & puff case, very simple profiles and flow dynamics of the main flow parameters have been established and a thorough parametric analysis and verifications were performed. It has been observed that the diffusion system becomes more prominent in regulating flow velocity with low permeability of the formation rock and low viscosity of the flowing fluid. The findings indicate a behavioral alignment with a previous hypothesis that matches actual reservoir behavior.

The intriguing aspect of this study is to include illustrative and realistic well-based matrix to efficiently evaluate, characterize and develop Unconventional reservoirs (UCRs). This research targets a newly assessment in redefining UCRs, and developing a well-based tool to evaluate, characterize and predict the performance of tight UCRs. In this study, permeability and viscosity are used to develop the Unconventionality Index (UI) to reflect the combined causes of low mobility from UCRs. Machine learning is applied to synthesize a novel comprehensive understanding of UCRs modeling. A distinct pattern is developed for to distinguish between UCRs and CRs to show the Recovery Factor (RF) / UI dependency. Consequently, to establish such relationship, data from major UCRs producers were examined and utilized. In addition, UCRs classification matrix has been developed utilizing actual UCRs data from different reservoirs. Furthermore, a unique Unconventionality Index has been established to classify UCRs, determine reasons of unconventionality and ascertain efficient method/s of development. Subsequently, a correlation between different rock and fluid properties incorporating UI and recovery factor has been attained.

The need for a tool to predict transport phenomena in petroleum reservoirs, especially the unconventional tight ones is placing more stringent demand on establishing more realistic models beyond the currently used viscous and inertial dependent models. In the past, diffusion forces in conventional reservoirs flow models has been ignored as the viscous transport thought to be