Petroleum Engineering – Exploration
Ziba Hosseini; Sajjad Gharechelou; Asadollah Mahboubi; Reza Moussavi-Harami; Ali Kadkhodaie-Ilkhchi; Mohsen Zeinali
Abstract
The conjugation of two or more Artificial Intelligent (AI) models used to design a single model that has increased in popularity over the recent years for exploration of hydrocarbon reservoirs. In this research, we have successfully predicted shear wave velocity (Vs) with higher accuracy through the ...
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The conjugation of two or more Artificial Intelligent (AI) models used to design a single model that has increased in popularity over the recent years for exploration of hydrocarbon reservoirs. In this research, we have successfully predicted shear wave velocity (Vs) with higher accuracy through the integration of statistical and AI models using petrophysical data in a mixed carbonate-siliciclastic heterogeneous reservoir. In the designed code for multi-model, first Multivariate Linear Regression (MLR) is used to select the more relevant input variables from petrophysical data using weight coefficients of a suggested function. The most influential petrophysical data (Vp, NPHI, RHOB) are passed to Ant colony optimization (ACOR) for training and establishing initial connection weights and biases of back propagation (BP) algorithm. Afterward, BP training algorithm is applied for final weights and acceptable prediction of shear wave velocity. This novel methodology is illustrated by using a case study from the mixed carbonate-siliciclastic reservoir from one of the Iranian oilfields. Results show that the proposed integrated modeling can sufficiently improve the performance of Vs estimation, and is a method applicable to mixed heterogeneous intervals with complicated diagenetic overprints. Furthermore, predicted Vs from this model is well correlated with lithology, facies and diagenesis variations in the formation. Meanwhile, the developed AI multi-model can serve as an effective approach for estimation of rock elastic properties. More accurate prediction of rock elastic properties in several wells could reduce uncertainty of exploration and save plenty of time and cost for oil industries.
Petroleum Engineering – Reservoir
Ali Kadkhodaie-Ilkhchi; Rahim Kadkhodaie-Ilkhchi
Abstract
Carbonate reservoirs rock typing plays a pivotal role in the construction of reservoir static models and volumetric calculations. The procedure for rock type determination starts with the determination of depositional and diagenetic rock types through petrographic studies of the thin sections prepared ...
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Carbonate reservoirs rock typing plays a pivotal role in the construction of reservoir static models and volumetric calculations. The procedure for rock type determination starts with the determination of depositional and diagenetic rock types through petrographic studies of the thin sections prepared from core plugs and cuttings. In the second step of rock typing study, electrofacies are determined based on the classification of well log responses using an appropriate clustering algorithm. The well logs used for electrofacies determination include porosity logs (NPHI, DT, and RHOB), lithodensity log (PEF), and gamma ray log. The third step deals with flow unit determination and pore size distribution analysis. To this end, flow zone indicator (FZI) is calculated from available core analysis data. Through the application of appropriate cutoffs to FZI values, reservoir rock types are classified for the studying interval. In the last step, representative capillary pressure and relative permeability curves are assigned to the reservoir rock types (RRT) based upon a detailed analysis of available laboratory data. Through the analysis of drill stem test (DST) and GDT (gas down to) and ODT (oil down to) data, necessary adjustments are made on the generated PC curves so that they are representative of reservoir conditions. Via the estimation of permeability by using a suitable method, RRT log is generated throughout the logged interval. Finally, by making a link between RRT’s and an appropriate set of seismic attributes, a cube of reservoir rock types is generated in time or depth domain. The current paper reviews different reservoir rock typing approaches from geology to seismic and dynamic and proposes an integrated rock typing workflow for worldwide carbonate reservoirs.
Petroleum Engineering
Reza Mohebian; Mohammad Ali Riahi; Ali Kadkhodaie-Ilkhchi
Abstract
Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major ...
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Intelligent reservoir characterization using seismic attributes and hydraulic flow units has a vital role in the description of oil and gas traps. The predicted model allows an accurate understanding of the reservoir quality, especially at the un-cored well location. This study was conducted in two major steps. In the first step, the survey compared different intelligent techniques to discover an optimum relationship between well logs and seismic data. For this purpose, three intelligent systems, including probabilistic neural network (PNN),fuzzy logic (FL), and adaptive neuro-fuzzy inference systems (ANFIS)were usedto predict flow zone index (FZI). Well derived FZI logs from three wells were employed to estimate intelligent models in the Arab (Surmeh) reservoir. The validation of the produced models was examined by another well. Optimal seismic attributes for the estimation of FZI include acoustic impedance, integrated absolute amplitude, and average frequency. The results revealed that the ANFIS method performed better than the other systems and showed a remarkable reduction in the measured errors. In the second part of the study, the FZI 3D model was created by using the ANFIS system.The integrated approach introduced in the current survey illustrated that the extracted flow units from intelligent models compromise well with well-logs. Based on the results obtained, the intelligent systems are powerful techniques to predict flow units from seismic data (seismic attributes) for distant well location. Finally, it was shown that ANFIS method was efficient in highlighting high and low-quality flow units in the Arab (Surmeh) reservoir, the Iranian offshore gas field.