Iranian Journal of Oil and Gas Science and Technology

Abstract Rock-Eval pyrolysis is a thermal method petroleum geologists use to evaluate source rock characteristics and obtain geochemistry parameters. However, there are misconceptions and misuses in exceptional cases that could lead to erroneous conclusions after using the Rock-Eval pyrolysis data to evaluate the properties of organic matter. However, a cross-plot of petroleum potential (S₂) versus total organic carbon (TOC) is a useful tool for solving issues and checking the accuracy of the geochemistry parameters. The graph provides the correction criteria for the S₂, hydrogen index (HI), and kerogen types. As well as the graph measures the adsorption of hydrocarbon by the mineral matrix. In addition, this article demonstrates a manner based on the data plot of S₂ versus TOC to detect bitumen or hydrocarbon contaminations. Based on our knowledge about the Garau formation as a possible source rock in the petroleum geology of Iran, a geochemistry study by Rock-Eval VI pyrolysis and LECO carbon analyzer has been conducted on many rock samples collected from different outcrops in the Lurestan province, Aligudarz region, from southwest of Iran, High Zagros. Plotting the data on a cross plot of S₂ versus TOC, drawing the regression line, and finding the regression equation are the best methods for determining the actual values of S₂ and HI parameters and bitumen/hydrocarbon contamination. Contamination creates a y-intercept in the graph of S₂ versus TOC, making geochemistry data unreliable in two study locations. The S₂ and HI data unrealistically increase, while the T_max values decline and reduce the thermal maturity of the organic matter from its actual status. The y-intercept of the graphs is removed, and the corresponding values are subtracted from the HI and S₂ to skip the effect of contamination and obtain the actual geochemistry parameters. The cause of contamination in the Garau formation is the adhesion of heavy bitumen to organic facies due to the covalent bonds between carbon and hydrogen ions.

Abstract This study uses the kernel principal component analysis (KPCA) feature extraction method for facies classification to extract new features from the measured well logs. After applying the principal component analysis (PCA) and KPCA feature extraction approaches, the classification was made using three robust classifiers: multilayer perceptron neural network (MLP), support vector machine (SVM), and random forest (RF). Finally, the predicted results for the test data that were not included in the training process were evaluated with the F1 score criterion. The PCA method did not significantly affect the classification performance due to the nonlinear structure of the facies. Our results show that the KPCA improves the performance of facies classification. Compared with the conventional approach based on well-log data, our new approach improves the classification accuracy for each classifier algorithm. In the RF results, the classification accuracy has increased by about 6%, while using the KPCA feature extraction approach raises classification accuracy from 52% to 58% compared to the original well-log data.

Abstract Stimulation of hydrocarbon wells with matrix acidizing operation is among the most common operations to stimulate the formation, remove the skin, and improve the productivity index. However, equipment corrosion, including casings, is one of the most critical concerns. In the present paper, the influence of the magnetic field on the corrosion behavior of drilling casing in 1.5 M (5 wt %) HCl was investigated in various conditions using potentiodynamic polarization (PDP) and weight loss (WL) measurements. The Taguchi experimental design (L-18 array) was utilized to model the impacts of magnetic field intensity, elapsed time, magnetization time, and temperature on the corrosion rate. The experimental results showed that the passing of acid through a magnetic field reduced the corrosion rate of N-80 carbon steel in HCl by up to 96%. Consequently, magnetized acid could reduce the effects of corrosion on matrix acidizing operations as a green corrosion inhibitor.

Abstract Fishing operations are one of the most essential parts of drilling operations. If the fishing operation fails, the other direction should be considered to continue drilling and reach the desired depth, which can be achieved using sidetracking operations. Long-term fishing operations increase the cost and time of the drilling operation; therefore, we should try to have a successful fishing operation in the shortest possible time. It can be stated that the execution of the fishing operation is economical as long as the costs are less or at least equal to the cost of the sidetracking operation. Therefore, the optimal fishing time must be determined to make the drilling operation economical. Many statistical analysis methods have been used to determine the optimal time, but they are not popular due to insufficient accuracy and time-consuming calculations. This study used a machine-learning (ML) model with a regression algorithm to estimate the optimal time for fishing operations in the Gachsaran oil field. The fishing cost rate and depth as input data were first collected and categorized based on different sections of the Gachsaran oil field to calculate the optimal fishing time. Then, the sidetracking cost was predicted by the machine learning model, and this cost was equated to the fishing cost in the worst conditions. As a result, the optimal fishing time was calculated for each section. The result showed that the model could estimate the cost of sidetracking with an error of less than 2%. Using the designed model and the input data of the Gachsaran oil field, considering the optimal fishing time, it was possible to save $1 million and 16 h in drilling a well.

Abstract Chlorine is a toxic and oxidizing gas used to purify drinking water in Iran. There has been no research on the effects of the gas or the explosion of the tanks, which could cause irreparable damage to people and the surrounding area. No such study has been carried out in the city of Abadan. To this end, Areal Locations of Hazardous Atmospheres (ALOHA) software, a computer program that helps professionals understand what will happen during a hazardous release, such as a chemical or fire, allows them to make plans to keep people safe. Thus, it was used to assess the magnitude of the gas release, the various risk zones, and the population at risk. In the event of damage to the 1-inch outlet valve of the tank, the gas release could be lethal up to a radius of 2 km, could be effective up to a radius of 6.2 km, and could be felt up to 10 km away. Due to the probability of occurrence and the location of the station in the wind direction, gas could reach many residents within a 5 km radius of the station. Therefore, as indicated by the results of the failure mode and effects analysis (FMEA) model evaluation, the implementation of preventive measures is strongly recommended in order to avoid gas release in settling tanks.

Abstract In steel production plants, such as those manufacturing sheets, pipes, and round bars, raw materials are annealed in preheating furnaces at approximately 1200 °C before undergoing hot deformation. Substantial oxidation and loss of raw steel materials occur in preheating furnaces, resulting in significant economic losses. A potential solution to reduce losses in this scenario is the application of protective ceramic coatings. This research investigates the effect of a ceramic coating based on Al₂O₃–SiO₂ on the oxidation behavior of steel sheets. The industrial-scale impact of the coating on the oxidation of steel slabs is also examined. The coating was applied using a spray method with slurry ceramic materials dispersed through a compressed air flow. Thickness measurement tests, scanning electron microscopy, and energy dispersive X-ray spectroscopy (EDS) analysis were conducted to evaluate the kinetics, microstructure, and oxidation behavior of the coatings. The findings indicated that the oxidation kinetics for uncoated steel sheets followed a parabolic trend, while the kinetics for ceramic-coated samples exhibited a slower logarithmic behavior. The application of the coating resulted in a reduction of the oxide layer thickness by less than 30% compared to the uncoated samples, attributed to a lower diffusion coefficient in the coated samples. Applying the ceramic coating on ST52 slabs in industrial tests led to a significant reduction in the oxide layer thickness and a more straightforward peel of the oxide layers. This showed that using such ceramic coating for materials in preheating furnaces could effectively reduce oxidation losses and enhance the mechanical quality of final products.