K Rezaeeparto; H Rahimpour Bonab; A Kadkhodaie; M Arian; E Hajikazemi
Abstract
Dariyan Formation (Aptian-Albian) is one of the main reservoir units in the South West of Iran which is equivalent to Shuaiba Fm,. Since the first step in the evaluation study of reservoir rock is studying the microfacies, depositional environments and diagenesis processes, in this research we have discussed ...
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Dariyan Formation (Aptian-Albian) is one of the main reservoir units in the South West of Iran which is equivalent to Shuaiba Fm,. Since the first step in the evaluation study of reservoir rock is studying the microfacies, depositional environments and diagenesis processes, in this research we have discussed these parameters in Dariyan reservoir in Salman oil field A, B, C, D and E wells. On the basis of petrographic studies classification of carbonate rocks by Dunham’s method and nomination of microfacies by Flugel’s classification was done and 7 basic microfacies have been identified: Mudstone, Mudstone to fossiliferous wackestone, Orbitolinid wackestone to packstone, Bioclast packstone to grainstone with restricted microfauna, Echinoderm wackestone to packstone, Planktonic wackestone to packstone, Mudstone to Planktonic/echinoderm wackestone, that belong to open marine sedimentary environment, shoal and lagoon. The study of vertical and lateral facies changes and comparing them with modern and ancient sedimentary environments show that Dariyan Formation in this area is deposited in a leeward mud dominated carbonate ramp. Due to numerous diagenetic processes observed in the formation, of their direct impact on the quality of the reservoir to the conclusion that micritization, cementation, compaction (mechanical), neomorphism, and pyritification to block the pores and pore through in the reservoir porosity and permeability decrease while, compaction (chemical), dolomitization, and fracture caused voids and communication between them. Finally, the porosity and permeability of the reservoir increased and led to higher quality.
S Gholipour; A Kadkhodaie; M Makkipour; A .R Abadi chalaksaraee
Abstract
Total organic carbon content is one of the important parameters to evaluate the geochemical properties of oil- and gas-producing layers. In this study, total organic carbon content in the hydrocarbon-bearing formations was evaluated using log data in three stages. In the first stage, we used artificial ...
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Total organic carbon content is one of the important parameters to evaluate the geochemical properties of oil- and gas-producing layers. In this study, total organic carbon content in the hydrocarbon-bearing formations was evaluated using log data in three stages. In the first stage, we used artificial neural network to calculate the organic carbon content. In the second stage, total organic carbon was calculated by using ΔLogR computational method. Finally in the last stage, well log data were classified into a set of electrofacies, which were performed using the most efficient clustering analysis method, i.e. MRGC method. Based on cluster validity tests, this method is the best to cluster petrophysical data in certain electrofacies. Cluster analysis was employed for classification of data from both neural network and ΔLogR methods. The results showed that intelligent systems are more appropriate than traditional techniques which are based on ΔLogR approaches, and also have higher accuracy. The proposed method has been presented with a case study from the Azadegan oilfield.
Y Noorian; S.R Moussavi Harami; A Kadkhodaie; A Kadkhodaie; S.A.A Abdolahi Mousavi
Abstract
Rock typing by different techniques is useful to interpret the zoning of reservoir. As the hydraulic flow unit has an effective connectionbetween the geological and petrophysical conditions, recognition of distribution quality of flow units in a reservoir can be useful for separation of reservoir into ...
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Rock typing by different techniques is useful to interpret the zoning of reservoir. As the hydraulic flow unit has an effective connectionbetween the geological and petrophysical conditions, recognition of distribution quality of flow units in a reservoir can be useful for separation of reservoir into different units with different reservoir conditions. In this study, by using of flow zone indicator (FZI), six flow units has been determined based on the porosity and permeability data in both wells A and B of the Bangestan reservoir in the MansouriOil Field. The main statistical parameters of Porosity and permeability data for each of flow units (HFU) shows HFU3andHFU1 have the highest and HFU5 and HFU6 have the lowest reservoir quality in the oil field. In order to cluster validity, silhouette approach was used to hierarchical and K-means clustering methods and finally hierarchical clustering was selected as the best method and the porosity and permeability data for both wells were included in the six clusters (RT). Comparison between Hierarchical clustering method with the flow units and microfacies indicated acceptable results for both methods.
