Acid fracturing stimulation is one of the preferred methods to improve well productivity in carbonate reservoirs. Acid is injected into the fractured zone after a starter fracture is created in the near wellbore area by viscous fluid (pad). This results in propagation of a two-wing crack away from the perforations with simultaneous dissolution etching of the created surfaces. If the created etched surface is non-uniform, then after the treatment ends and the fracture face closes, a high conductivity path may remain in the formation, connected to the well. The important factors controlling the effectiveness of acid fracturing are the etched-fracture penetration and conductivity. In this research, I use the distributed volumetric sources (DVS) method to calculate gas production from a well stimulated by acid fracturing. The novel concept realized in this research is that, during the production process, the conductivity of the acid created fracture changes. I use the Nierode - Kruk correlation to describe this effect as a function of effective closure stress that in turn is determined from the flowing bottomhole pressure and minimum horizontal stress. By combining the well productivity calculation from the DVS method taking into account varying fracture conductivity with gas material balance, I obtain an improved model of gas production. The model is then used to not only forecast production from acid fractured wells but also to evaluate the known production history of such wells. Based on the concepts discussed above, I have developed a program called "Gas Acid" which is useful to optimize acid fracturing treatments and also suitable to infer created fracture parameters from known production history. The "Gas Acid" program has been validated with data from two Saudi Aramco gas wells. It was found that the production forecast obtained from the "Gas Acid" program matches the actual production history with reasonable accuracy and the remaining discrepancy could be resolved by taking into account refinement of the material balance. The refinement became necessary, because the "Gas Acid" program was developed for dry gas but the reservoir fluids in the field examples were classified as retrograde gas and wet gas. When accounting for the additional mass of gas "hidden" in the produced condensate, the match of forecast and actual data was improved considerably.

Productivity index (PI) is an important indicator of a well?s production capacity. For conventional reservoirs, well productivity is usually calculated using the pressure response of the reservoir in its pseudosteady-state period. There are numerous studies for different well completion schemes which developed correlations for pseudosteady-state productivity index for specific cases, such as horizontal wells and fractured wells. Most of the developed models for complex well completion schemes use some approximations for productivity index calculation and they have some limitations in use. Furthermore, as the petroleum industry goes toward producing lower quality reservoirs like low- and ultra low-permeability reservoirs, the period of transient flow covers a larger part of the well lifetime and these pseudosteady-state productivity calculations become less applicable in prediction of the reservoir?s production behavior. The Distributed Volumetric Sources (DVS) method seems able to fill this gap. Our method is able to predict the productivity index of a general well completion scheme for transient as well as pseudosteady-state flow periods. In this study, we focus on a typical well completion scheme ? vertical well intersected by a vertical fracture of finite conductivity. Parametric study is performed by varying the proppant pack permeability with a linear distribution, varying fracture width with an elliptical distribution and varying fracture height with an elliptical distribution. The details of hydraulic fracture are integrated into the calculation of well productivity. By combining the well productivity with gas material balance, production forecasting of the hydraulically fractured wells could be easily obtained. The result of production forecasting could be used to aid in decision making of choosing the best stimulation treatment. Field examples are presented to illustrate the application of this technology for production modeling the complicated reservoir cases involving fracture stimulation.