نشریه زمین شناسی نفت ایران
سال ششم شماره 1 (پیاپی 611، بهار و تابستان 1395)

Electro-facies analysis is a sure method in clustering petrophysical logs analysis that can well indicates the changes of geologic charactereristics of the different lithostratigraphic units as well. Petrographic studies of 120 prepared thin sections of the upper parts of the Sarvak Formation in studied oilfield¡ Southwestern Iran¡ has resulted in definition of 6 microfacies sedimentary types. These data suggested that the middle Creataceous sediments in the studied area were predominantly deposited in two subenviornments of patch organic barrier and lagoon corresponding to inner and shallow a homoclinal ramp. In addition¡ using petrophysical logs in the studied well¡ 10 electrical facies were determined. By analyzing the data petrophysical in Geolog software¡ 10 electerical facies were optimized and then their number was reduced to six. According to good adaptation of optimized electerical facies with sedimentary microfacies¡ we would generalized obtained dresults to all parts of the Sarvak Formation. Study of porosity changes in the electrical diagrams such as CGR corrected Gamma¡ and sonic curves in mentioned well¡ indicated a significant reduction in facies porosity of 1 to 6. In addition¡ the results showed that the reservoir quality reductionin studied microfacies is corresponded to Bioclastic Rudist debris floatstone/ Rudstone¡ Benthic foraminifera Rudist debris wackestone / packstone¡ Benthic Foraminifera peloid Grainstone/Packstone¡ with High diversity benthic foraminifera wackestone / packstone¡ Peloid bioclastic wackestone /packstone with Low diversity benthic Foraminifera and Mudestone/Wackestone¡ respectively. In this study¡ we concluded that comparison and correlation sedimentary facies with electric microfacies provide an efficient method to study the quality of the hydrocarbon reservoir system that especially can be used in other wells with no core.

In this study, biostratigraphy, depositional environment and sequence stratigraphy of the Sarvak Formation at Fahliyan Anticline was studied. 8 species of benthic foraminifera (4 genera) and 8 species of planktonic foraminifera (11 genera) in the study area were recognized. 6 biozones have been recognized by distribution of the foraminifera, which in stratigraphic order are: Favusella washitensis Zone, Orbitolina-Alveolinids Assemblage Zone, Rudist debris Zone, Oligostegina flood Zone, Whiteinella archaeocretacea Zone and Helvetoglobotruncana helvetica Zone. On the basis of these, the age of Albian–Turonian was considered for the Sarvak Formation. Based on petrography and analysis of microfacies features 9 different microfacies types have been recognized, which can be grouped into 3 depositional environments: lagoon, shoal and open marine. The Sarvak Formation represents sedimentation on a carbonate ramp. Sequence stratigraphy analysis led to identification of 4 third-order sequences.

In this study the relationship between petrographic studies (microfacies, environment of deposition and diagenesis) and petrophysical data of core analysis in order to identify reservoir quality of the Jahrum Formation in well no. 11 at the Golkhary oil field. The Golkhary oil field is located at the west of QatarKazerun fault, in between Binak and Nargesi oil fields. The Jahrum Formation consists of limestone, dolomite limestone and dolomite. Petrographical studies accomplish in two parts microfacies studies led to the recognition of 9 microfacies that were deposited in 3 facies belt tidal flat, lagoon and open marine environment. In diagenesis studies the most important factors included bioturbation, micritization, mechanical compaction, secondary porosity, secondary anhydrite cement, calcite burial cement, stylolites and solution seams, replacement dolomite, dolomite cement and hydrocarbon shows. Porosities identified are intercrystalline, intergrain, intrafossil and intragrain, moldic, fracture, solution along stylolite, solution enlarge and shelter. The results of this study shows that Grain supported MF3 (Bioclast Nummulitidae Rotalia wackestone/ packstone/ grainstone) and MF4 (Bioclast Nummulitidae Orbitolites packstone/ grainstone/ floatstone) due to the presence in the environment energetic, micrite absence, lack of widespread cement, presence of effective porosities includes intergrain porosity and intercrystalline porosity identified are reservoir microfacies. Petrophysical data with high levels of permeability and effective porosity between these two microfacies is the confirmation of the results of petrographic studies.

Alkhalij field located offshore Qatar, was discovered in 1991 and put into production in 1997. The field is original in many aspects. It is one of the few carbonate reservoir with stratigraphic closure, the oil is being trapped in upper part of a monocline due to a lateral variation in reservoir facies. In addition, the reservoir lies within the capillary transition zone and consists of a succession of highly conductive oil bearing layers (drains) in between matrix layers of poor permeability. High- resolution 3D seismic has revealed additional area developed, which were previously considered to be uneconomic. Resent 4D seimic pilot has given encouraging result to utilize Seismic Technologies to analyse the evolution of the Al Khalij oilfield over time. Studies shows, this field possibly extended toward Reshdat oil field.

Studying of mud loss in Asmari formation is very important because it consists most of drilling expenses. Considering that studied oil field encounters severe mud loss in Asmari formation, therefore the purpose of this study is recognition of the lost circulation zones and illustrating the mud loss distribution in Asmari formation. The mud loss maps in Asmari field were plotted in RMS software using moving average algorithm method. For this purpose, the data of 363 wells in gachsaran oil field was processed after data preparation, for mapping and 3D modeling of 11 different zones in Asmari formation. The data processing includes different stages such as elimination of outliers, normal transformation, drawing the histogram, variography and estimation and modeling. In this research, the geostatistical kriging method was also used for estimation and 3D modeling of mud loss in Asmari formation so that the output of geostatistical modeling method shows the localized and better results. Consequently, by applying and analysis of results, the 2D and 3D models of mud loss in Asmari formation were demonstrated. By simulation and modeling of mud loss and its comparison with reservoir fault modeling and production indexes plots, it was identified that the dominant mud losses are related to fault zone fractures and in minor cases the increasing of mud weight is the reason of mud loss. Applying appropriate operations such as under balance drilling (UBD) and suitable well placement, use of drilling mud with proper mud weight in severe mud loss points, use of NIF and MMH especial drilling muds with lowest formation damage, or a combination of these methods are suggested for mud loss control in critical points of the oil field.

Estimation of formation water saturation (Sw) using log data is an important approach in the seismic exploration and characterization of a hydrocarbon reservoir. Therefore, it seems that the proper prediction/simulation of Sw is essential. The first objective of this study was to develop a predictive model for Sw estimation based on hybrid cluster analysis with piecewise nonlinear regression, and after that, using the developed model, Sw was simulated by the Monte Carlo simulation (MCS). In order to achieve objectives of this study, a group of 909 data points was used for model construction and 302 data points were employed for assessment of model. The obtained results of MCS modeling indicated that this approach is capable of simulating Sw ranges with a good level of accuracy. The mean of simulated Sw by MCS was obtained as 0.28 m, while this value was achieved as 0.29 m for the measured one. Furthermore, a sensitivity analysis was also conducted to investigate the effects of model inputs on the output of the system. The analysis demonstrated that RHOB is the most influential parameter on Sw among all model inputs. It is noticeable that the proposed hybrid cluster analysis with piecewise nonlinear regression and MCS models should be utilized only in the studied area and the direct use of them in the other conditions is not recommended.