• Energy Research

Abstract As ultra-tight porous media that include organic contents, shale gas resources are technically known as complex systems having various mechanisms that impact storage and flow. The slippage, Knudsen diffusion, the process of desorption, an adsorbed layer that affects apparent permeability, and solute gas in kerogen are recognized to be the most important ones. However, simultaneous effects of multi-mechanism flow and storage, and influences of scattered organic contents on shale gas flow behaviour are not well-understood yet. According to the mass conservation law, a basic mathematical model has been developed to investigate, step-by-step, the effects of different changes that are introduced, and examine whether patterns of how kerogen is distributed affect the production plateaus. The discretization of the second-order nonlinear Partial Differential Equation (PDE) that is evolved results in a certain number of nonlinear simultaneous algebraic equations, which are conventionally solved with the application of Newton’s method. To overcome the inherent difficulties of the initial guess, the derivations, and the inversion of the Jacobian matrix, a new application of Particle Swarm Optimization (PSO) as a nonlinear solver was applied to extract the anticipated pressure profile for each step in time outside the bounds of the reference equations. The results show that not only can the PSO effectively meet the required criteria, but also it performed faster than conventional techniques, especially in cases with a larger number of grids that encompass more phenomena. It was further revealed that the insertion of a multi-mechanism apparent permeability model in which the pore radius is also a pressure-dependent parameter causes the lower rate of production. A higher level of production has been recorded after including storage terms of adsorption and solute gas in kerogens. Although different patterns of kerogen distribution have finally overlapped, the different taken trend of each production profile underlines the impact of kerogen distribution as an important parameter within the procedure of history matching.

Abstract Added values to project economy from condensate sales and gas deliverability loss due to condensate blockage are the distinctive differences between gas condensate and dry gas reservoirs. To estimate the added value, one needs to obtain condensate to gas ratio (CGR); however, this needs special pressure–volume–temperature (PVT) experimental study and field tests. In the absence of experimental studies during early period of field exploration, techniques which correlate such a parameter would be of interest for engineers. In this work, the developed model inspired from a new intelligent scheme known as “least square support vector machine (LSSVM)” to monitor condensate gas ratio (CGR) in retrograde condensate gas reservoirs. The proposed approach is conducted to the laboratorial data from Iranian oil fields and reported in literature has been implemented to mature and test this approach. The generated results from the LSSVM model were compared to the addressed real data and generated results of conventional correlation and fuzzy logic models. Making judgements between the generated outcomes of our model and the another course of action proves that the least square support vector machine model estimate condensate gas ratio more accurately in comparison with the conventional applied approaches. It worth mentioning that, least square support vector machine do not have any conceptual errors like as over-fitting issue while artificial neural networks suffer from many local minima solutions. Outcomes of this research could couple with the commercial production softwares for condensate gas reservoirs for different goals such as production optimization and facilitate design.