RESUMEN
Rock typing techniques have relied on either electrical or hydraulic properties. The study introduces a novel approach for reservoir rock typing, the hydraulic-electric index (HEI), which combines the strengths of traditional electrical and hydraulic rock typing methods to characterize carbonate reservoirs more accurately. By normalizing the ratio of permeability and formation resistivity factor (K/FRF) with respect to porosity, the HEI method is applied to two datasets of carbonate core samples: dataset 1 consists of 112 carbonate core samples from the Tensleep formation in the Bighorn basin of Wyoming and Montana, and dataset 2 includes 81 carbonate core samples from the Asmari formation in the south-west of Iran. Statistical analysis confirms the effectiveness of the HEI in predicting permeability, with high determination coefficients for both datasets (resulting in determination coefficients (R2) of 0.965 and 0.904 for dataset 1 and dataset 2, respectively). The results classify the rock samples into distinct rock types, nine rock types for dataset 1 and four rock types for dataset 2, and demonstrate the HEI ability to capture the relationship between hydraulic conductivity and electrical resistivity in carbonate reservoir rocks. Applying the HEI method to the validation dataset yielded highly accurate permeability predictions, with average of determination coefficients of 0.883 and 0.859 for dataset 1 and dataset 2, respectively. Validation of the HEI method further confirms (20% of the dataset was set aside for validation, while the remaining 80% was used for the rock typing approach (5 folds)) its accuracy in predicting permeability, highlighting its robust predictive capacity for estimating permeability in carbonate reservoirs.
RESUMEN
Permeability modelling is considered a complex task in reservoir characterization and a key component of reservoir simulation. A common method for permeability modelling involves performing static rock typing (SRT) using routine core analysis data and developing simple fitting-based mathematical relations that link permeability to reservoir rock porosity. In the case of carbonate reservoirs, which are associated with high heterogeneities, fitting-based approaches may fail due to porosity-permeability data scattering. Accurate modelling of permeability using petrophysical well log data seems more promising since they comprise a vast array of information about the intrinsic properties of the geological formations. Furthermore, well log data exhibit continuity throughout the entire reservoir interval, whereas core data are discrete and limited in availability and coverage. In this research work, porosity, permeability and log data of two oil wells from a tight carbonate reservoir were used to predict permeability at un-cored intervals. Machine learning (ML) and fitting models were used to develop predictive models. Then, the developed ML models were compared to exponential and statistical fitting modelling approaches. The integrated ML permeability model based on Random Forest method performed significantly superior to exponential and statistical fitting-based methods. Accordingly, for horizontal and vertical permeability of test samples, the Root Mean Squared Error (RMSE) values were 3.7 and 4.5 for well 2, and 1.7 and 0.86 for well 4, respectively. Hence, using log data, permeability modelling was improved as it incorporates more comprehensive reservoir rock physics. The outcomes of this reach work can be used to improve the distribution of both horizontal and vertical permeability in the 3D model for future dynamic reservoir simulations in such a complex and heterogeneous reservoir system.
RESUMEN
One of the serious problems in the oil industry is precipitation and deposition of asphaltenes in the different oil production stages including formation, wellbore, production tubing, flow lines, and separation units. This phenomenon causes a dramatic increase in the cost of oil production, processing, and transferring. Thus, it seems to be very necessary to use the removing methods for precipitated asphaltenes in different crude oil production and transferring stages. In this study, the ability of microorganisms for biodegradation of precipitated asphaltenes was investigated. For this purpose, four bacterial consortiums were isolated from oil-contaminated soil, crude oil, reservoir water, and oil sludge samples of an oil field located in the southwest of Iran. Based on the results of the designed experiments, by using response surface methodology (RSM) and central composite design, the bacterial consortiums were cultured in the flasks. Three levels of temperatures, salinity, pH, and initial asphaltene concentration as the substrate were considered as the parameters of culture medium and incubated growth mediums for 60 days. The maximum asphaltene biodegradation was 46.41% caused by the crude oil consortium including Staphylococcus saprophyticus sp. and Bacillus cereus sp. at 45 °C, salinity 160 g·L-1, pH 6.5, and 25 g·L-1 initial asphaltene concentration. Also, it was observed that the negative or positive impacts of culture media conditions such as temperature and salinity on asphaltene degradation depended on the type of the available bacterial consortium. The carbon-hydrogen-nitrogen-sulfur analysis showed that carbon, hydrogen, nitrogen, and in some cases, the sulfur in biodegraded samples are less than in control samples. Moreover, Fourier transform infrared analysis indicated that the alkyne groups were less resistant to biodegradation and were eliminated thoroughly after 2 months of incubation. In addition, alkane components were partially removed in treated asphaltene fraction. The parameters of culture medium were optimized by RSM, and besides, their effects on the performance of bacteria in the asphaltene biodegradation process were discussed. The validity of some available kinetic models to describe the behavior of the studied bacteria consortium was investigated, and it was observed that Tessier, Moser, and Contois models accurately predict the values of asphaltenes and biomass concentration at 30, 45, and 60 °C, respectively.