The Cretaceous carbonates of Sarvak formation formed large hydrocarbon reservoirs in the South-west region of Iran. The studied field is a tight carbonate reservoir in which several exploration wells have been drilled, and is in the process of development. Since, only few wells have core data, therefore it was decided to integrate the available core and log data using new methods that describe the carbonates heterogeneity more precise. 3D modeling of permeability is an essential part of building robust dynamic model for proper reservoir management and making reliable predictions. A good definition of reservoir rock types (RRT) could relate somehow better geological modes to dynamic models. Rock typing by flow-zone-index (FZI) and rock-quality-index (RQI) values proved to be an effective technique to develop porosity-permeability transforms for RRTs in a reservoir model. RRTs were defined based on the core derived FZI through some mathematical and statistical approaches. Permeability estimation using artificial neural network approach (ANN) was then made through a two-step process. In the first step, FZI log was estimated from a trained neural network using the standard suite of logs as input (Gamma ray, Sonic, Density, Neutron porosity) and FZI-core as output in a subset of cored wells (Key wells). In the second step, individual trained neural networks implemented porosity-log and FZI-log from the first step to predict permeability-log for each RRT. Validation of the predictive capability of the method in two cored wells (Blind-test wells) that are located in the field proved the estimation technique to be robust and was found to be valuable to supplement core data in the prediction of log-permeability in the entire reservoir wells. For the sake of comparison between the result of this work and the work which was based on the integration of sedimentological, petrographical, and diagenetic study, the results were found to be in good agreement for most of the log interval. However, the predictions of the ANN approach in the regions where core data are not available are better and it follows the log property variation logically. Introduction Rock typing is a process for the classification of reservoir rocks into distinct units. These units are deposited under similar geological conditions and undergoes through similar diagenetic alterations. If the rocks are properly classified and defined, a given rock type is imprinted by a unique porosity/permeability correlation, capillary pressure profile, and set of relative permeability curves. In addition, the true dynamic characteristics of the reservoir will be captured in the reservoir simulation model as a more reliable permeability model is used1–3. On the classical plot, the relationship between permeability and porosity is not causal. Whereas porosity is generally independent of grain size, permeability is strongly dependent on grain size. Hence, traditional plot cannot be used reliably to estimate accurate permeability from porosity. Several investigators1–4 have noted the inadequacy of classical approach and have proposed alternative models for relating porosity to permeability specifically for using in carbonates reservoir. From the classical approach it can be concluded that for any given rock type, the different porosity/permeability relationships are evidence of the existence of different hydraulic units. In fact, several investigators5–7 had come to similar conclusions about porosity/ permeability relationships.

Improving oil production is one of the most challenging subjects in carbonate reservoirs, especially in the case of thin formations when reservoir pressure is close to saturation pressure with undesired well locations. In this study water injection was used to mitigate gas production in a thin reservoir with high gas oil ratio for the purpose of optimizing oil production. The studied field is a cretaceous oil bearing reservoir composed of tightly packed limestone characterized by high porosity but poor permeability with a thickness of 55-65 meters throughout the reservoir. The matrix permeabilities and porosity are in the range of 0.01-150md and 5-35 percent respectively. The oil gravity is 21.5 degree API and reservoir pressure of 1700psia which is close to bubble point pressure of 1492psia. The produced wells were drilled in top layers of the reservoir. A full field model was constructed to determine the optimal production strategy and applied reservoir management with available produced well locations. Two possible scenarios; namely, natural depletion and water injection were compared. Results indicated that water injection yields better recoveries than natural depletion. Different scenarios of injection well location, well orientation and mechanism of injection were considered. Horizontal injection and production wells located at same layer were found to maintain reservoir pressure, prevent gas production, and increase oil recovery. Depleted regions near the producers were found to play a major rule on the success of the project. The enhancement of oil recovery was improved to 37 percent in the case of water injection with the implementation of proper reservoir management.