An improvement of water saturation calculation using well logs and laboratory data in carbonate hydrocarbon reservoirs

 





^*نگارنده رابط:           تلفن: 3395509-0273         دورنگار: 3395509-0273        amoradzadeh@shahroodut.ac.ir E-mail:





One of the most important parameters in reservoir characterization is hydrocarbon saturation. It is assumed that all void spaces in a reservoir consist of water and hydrocarbon, therefore: Sh = 1-S_w, wherein Sh and Sw are hydrocarbon and water saturation respectively. Since estimation of hydrocarbon saturation is not generally an easy task, it is recommended to determine the water saturation and predict the saturation of hydrocarbon using the above equation. The estimation of this important reservoir petrophysical parametere (i.e. S_w) is commonly determined by various electrical and prosity logs using the following formula (Moradzadeh and Ghavami, 2001) for a clean (non shaly) formation:
where R_t is the corrected total electrical resistivity of formation obtained from resistivity logs, is porosity of the rock obtained by porosity logs such as sonic or density, R_w is the water resistivity obtained from self potential (SP) logs or production tests, is a constant which depends on rock type and tortuosity of the fluid path, and m and n are unknown cementation and saturation exponents which need to be determined by petrophysical studies using core data of each formation. The value of  is 0.6 for unconsolidated sandstones, 0.8 for consolidated sandstones and 1 for carbonates (Kamel and Mabrouk, 2002).
The porosity can be quite accurately determined from nuclear or acoustic logs. The uninvaded resistivity Rt is more difficult to obtain because all resistivity measurements are influenced by the resistivity of nearby layers and by the resistivity of the invaded zone in the immediate vicinity of the borehole wall. Generally, Rt is calculated through a combination of shallow, medium and deep resistivities from induction or laterolog tools.
Using of this method is not possible in shaly (unclean) and heterogeneous formations. In shaly sands, the presence of clay adds an additional conductivity. This additional conductivity will cause an error in water saturation estimation. In this cases varios models of water saturation has been proposed (Worthington,1985; Kamel and Mabrouk,2003; Alimoradi, et al., 2011). In carbonate reservoirs, wide range and irregular distribution of pore sizes change the rock conductivity and adversely affects the precision of Archie’s formula in Sw estimation. The effect of pore size and pore distribution in the evaluation of water saturation in these kind of rocks were studied in few research work in which the proposed equation of Lucia (2007) is perhaps the most significant contribution in this regards. As these methods are essenstialy based on well logs data and some assumption that may not be correct in some real practical cases, so in some situations the results of these methods are not so accurate.
As carbonate rock forms the most promineint hydrocarbon reservoirs in the world and in particular in the Middel East, therefore an accurate water saturation estimation method is highly required. In these reservoir rocks it seems a method based on the core data, hydraulic flow unit concept, capillary pressure and Leverett function which is established for clastic (sandstone) reservoir could be effective to estimate Sw preciesly. 
In this paper it is attempt to use a set of high accuracy laboratory core tests data to present an improved method for calculation of water saturation within a carbonate reservoir in the southern part of Iran. This method is implemented in the following 10 steps, once the reservoir carbonate rocks were classified using hydraulic flow units concept.
1. Calculation of J functions by Garrouch (1999) formula for all lab data using permeability (k), porosity ( ) and mercury injection capillary pressure (Pc) data of each rock sample as well as surface tension ( ) and contact angle ( ) of the fluid in lab condition.
2. Calculation of mean hydraulic radius for each core sample (RQI).
3. Plotting Swir from Pc data against RQI and deriving the best equation. This can be used to predict Swir for any given permeability (k) and porosity ( ) data.
4. Calculation of  by normalizing Sw;  = (Sw – Swir)/(1 – Swir)
5. Plotting J function against  and deriving equation of the straight line on a log-log plot for each hydraulic unit of the reservoir.
6. Calculation of height above free water level for the reservoir.
7. Calculation of Pc and then J functions using k, , and values of the reservoir for any given height above oil-water or gas-oil contact.
8. Determination of  from the J versus  relationship derived in setp 5 and J function values determined in step 7.
9. Calculation of reservoir Sw, using  of step 8. (Sw= .(1- Swir)+Swir))
10. Comparison of the calculated Sw (from step 9) with already available log Sw values.
In this study it has been shown how water saturation values within a well are estimated by integration of mercury injection capillary pressure data, classification of carbonate rocks using hydraulic flow units, and also relation between the Levert function and normalized water saturation. The obtained results and its comparison with those of well logs data indicates that the presented method is capable enough to determine water saturation in carbonate hydrocarbon reservoir precisely.