Iranian Journal of Oil and Gas Science and Technology

Abstract This study presents an advanced petrophysical evaluation of the Sarvak Formation in one of the major supergiant oil fields in Southwest Iran, achieved by integrating Nuclear Magnetic Resonance (NMR) log data with conventional well logs. NMR measurements from Well-A were analysed to extract critical reservoir properties—including total and effective porosity, and volumes of bound and free water—which significantly enhanced the accuracy of the petrophysical model. A multi-resolution graph-based clustering (MRGC) algorithm was developed to estimate NMR-derived parameters from conventional logs for the adjacent Well-B, where NMR data were unavailable. The MRGC model utilised gamma-ray, acoustic, density, neutron, and photoelectric logs to predict total and effective porosity, clay-bound water, irreducible water saturation, and other NMR-related parameters. The model was calibrated using data from Well-A and subsequently applied to Well-B, enabling NMR-informed petrophysical characterisation in the absence of direct measurements. The optimised petrophysical model demonstrated consistent reservoir characteristics across both wells. Average total porosity was 10.7% in Well-A and 12.2% in Well-B; effective porosity averaged 10.2% and 11.8%, respectively; clay volume was approximately 3.2% in Well-A and 3.6% in Well-B; and water saturation was 85% and 84%, respectively. Based on cutoff thresholds of 5% porosity, 15% clay volume, and 50% water saturation, net pay intervals were delineated, yielding approximately 31 m of productive zone out of 411 m in Well-A, and 30 m out of 380 m in Well-B. The NMR-augmented analysis provided more precise differentiation of hydrocarbon-bearing zones and proved more cost-effective than traditional log-based methods. This refined petrophysical workflow significantly improves reservoir characterisation, enhances the accuracy of hydrocarbon volume estimation, and supports more informed field development planning.

Abstract In recent years, the use of natural surfactants as surface active agents in chemical methods of oil recovery over chemical surfactants has been under consideration due to the absence of environmental problems. In this study, a new plant, Albizia julibressin (Albizia), was introduced as a natural surfactant. Our novelty resides in a unified approach that deals with the introduction of Albizia julibressin (Albizia) as a new natural surfactant, interpretation of the chemical EOR objectives, interface reactions, and the induced optimization to improve oil recovery. For this purpose, the plant was extracted using Soxhlet extraction method, aqueous base solutions and interfacial tension between natural surfactant aqueous solutions and kerosene as an oil phase were measured by pendant drop method. The critical micelle concentration structures formed by this material has been determined by interfacial tension tests and confirmed by electrical conductivity tests. The results show that Albizia extract at 3.5 wt% begins to form micelles structures, which is the critical concentration of Albizia plant micelles. At this concentration, the interfacial tension between the deionized water and the oil phase is reduced from 34 mN /m to 10 mN/m, which indicates a significant decrease in interfacial tension by this plant. Carbonate rock was employed to core flooding experiments in order to investigate the effect of Albizia extract (AE) on oil recovery. Also based on results, by using AE, wettability of oil-wet carbonate rocks, was altered from about 165.02◦ to 86.59◦. Finally, AE enhanced ultimate oil recovery about 11.6% of original oil in place in tertiary recovery for a carbonate rock.

Abstract The prediction of lithology is necessary in all areas of petroleum engineering. This means that to design a project in any branch of petroleum engineering, the lithology must be well known. Support vector machines (SVM’s) use an analytical approach to classification based on statistical learning theory, the principles of structural risk minimization, and empirical risk minimization. In this research, SVM classification method is used for lithology prediction from petrophysical well logs based on petrographic studies of core lithology in a heterogeneous carbonate reservoir in southwestern Iran. Data preparation including normalization and attribute selection was performed on the data. Well by well data separation technique was used for data partitioning so that the instances of each well were predicted against training the SVM with the other wells. The effect of different kernel functions on the SVM performance was deliberated. The results showed that the SVM performance in the lithology prediction of wells by applying well by well data partitioning technique is good, and that in two data separation cases, radial basis function (RBF) kernel gives a higher lithology misclassification rate compared with polynomial and normalized polynomial kernels. Moreover, the lithology misclassification rate associated with RBF kernel increases with an increasing training set size.

Abstract Fractured reservoirs contain about 85 and 90 percent of oil and gas resources respectively in Iran. A comprehensive study and investigation of fractures as the main factor affecting fluid flow or perhaps barrier seems necessary for reservoir development studies. High degrees of heterogeneity and sparseness of data have incapacitated conventional deterministic methods in fracture network modeling. Recently, simulated annealing (SA) has been applied to generate stochastic realizations of spatially correlated fracture networks by assuming that the elastic energy of fractures follows Boltzmann distribution. Although SA honors local variability, the objective function of geometrical fracture modeling is defined for homogeneous conditions. In this study, after the introduction of SA and the derivation of the energy function, a novel technique is presented to adjust the model with highly heterogeneous data for a fractured field from the southwest of Iran. To this end, the regular object-based model is combined with a grid-based technique to cover the heterogeneity of reservoir properties. The original SA algorithm is also modified by being constrained in different directions and weighting the energy function to make it appropriate for heterogeneous conditions. The simulation results of the presented approach are in good agreement with the observed field data.

Abstract Enhanced oil recovery using nitrogen injection is a commonly applied method for pressure maintenance in conventional reservoirs. Numerical simulations can be practiced for the prediction of a reservoir performance in the course of injection process; however, a detailed simulation might take up enormous computer processing time. In such cases, a simple statistical model may be a good approach to the preliminary prediction of the process without any application of numerical simulation. In the current work, seven rock/fluid reservoir properties are considered as screening parameters and those parameters having the most considerable effect on the process are determined using the combination of experimental design techniques and reservoir simulations. Therefore, the statistical significance of the main effects and interactions of screening parameters are analyzed utilizing statistical inference approaches. Finally, the influential parameters are employed to create a simple statistical model which allows the preliminary prediction of nitrogen injection in terms of a recovery factor without resorting to numerical simulations.