Carbonate rock are characterized by complicity of pore system. Variety of pore types associate with Variety of flow characteristics. The main characteristics affected by varying rock pore types are; Porosity-permeability relationship, Capillary pressure and relative permeability, and Archie cementation factor (m).
Many pore systems could be existed in heterogeneous carbonate. Discriminate the different pore types in vertical and lateral directions through heterogeneous carbonate are a difficult task but essential for reliable evaluation and effective management of carbonate reservoirs. Conventional log response and traditional log interpretations do not have the ability to discriminate the different pore types. So, discriminating rock pore type and flow units through heterogeneous carbonate, require integration for all available data. There are no universal applicable techniques to integrate the available data for recognizing the different pore type and flow units through heterogeneous rock.
This paper introduces an approach to correlate the conventional log data with the production results of the old wells which were completed on heterogeneous Miocene carbonate reservoirs. A series of cross plots for the perforated intervals of highest and lowest productivity wells were constructed. These cross plot show a relationship between the well productivity and the position of log data on such cross plots. Based on this observation a relation between rock quality or productivity and conventional log data was established. The recently drilled wells data such as mobility from Modular dynamic tester (MDT), pressure transient analyses and core data confirm this relation.
This approach enhances understating the Miocene carbonate behavior and improves reservoir management by:
Accurate reservoir evaluation and select perforated intervals
Effective well completion and stimulation
Effective managements the Miocene carbonate gas injection projects.
On other hand, Layering simulation models for the Miocene carbonate reservoirs based on flow characteristic enhance the model reliability and prediction of the reservoirs behavior under different depletion schema.
The majority of carbonate reservoirs in the Gulf of Suez are Located on the offshore and adjacent onshore areas of the Western side. Gulf of Suez carbonate reservoirs are the shallowest oil- bearing reservoirs through the Gulf fields. It lies at depths range from 500 mt vss to 1000 mt vss. The Production history of these reservoirs extends for periods up to 100 years. Generally, Gulf of Suez carbonates reservoirs contain a huge number of IOIP and produce the majority of the oil produced from its fields. Fig. 1 shows the relative location of the main fields of Carbonate producers. The Gulf of Suez carbonates reservoirs represent an example of highly heterogeneous carbonate reservoirs where the masses carbonate rocks admixture with evaporates. It exhibits a high degree of heterogeneity in vertical and horizontal direction.
During the last ten years, the General Petroleum Company (GPC) carried out extensive operation activities on the carbonate reservoirs. These operations are varying from new drilling wells for exploration, to infill drilling wells and recompilation for development. The main problem in the management of these reservoirs could be listed as Discrepancies between the conventionally log interpretation and production test results
Highly contrast of wells productivity; a well oil rate could reach to 6000 bld while off set well has very low productivity.
Difficult to predict well productivity before testing
Non -uniformly fluids distribution; higher interval could produce water while lower interval in offset well could produce oil with minimum water cut.
Channeling of injected fluids due to preferential fracturing caused by excessive injection rates
Its impossible to defined OWC or OGC all over the reservoir
Poor or inadequate completions and stimulations
Poor reservoir sweep efficiency in contacting oil throughout the reservoir as well as in the nearby well regions
Limited data availability and poor data quality