مجله پژوهش نفت
پیاپی 126 (آذر و دی 1401)

Permeability is one of the most important parameters in hydrocarbon reservoirs. It is beneficial to have a correct understanding of the permeability and its distribution in the production management process. Due to the limitations, the coring process is performed on a small number of wells in the field, while most of the wells are subjected to well logging operations. Therefore, finding a way to estimate the characteristics of the reservoir by well-logging and modeling it on the field is a valuable technique. Therefore, in this study, the multilayer perceptron artificial neural network method (error back propagation) has been used to estimate the permeability of different parts of the Shurijeh Formation in the Kopeh Dagh sedimentary basin. Sonic logs, neutrons, density and the results of the formation evaluation, including porosity and saturation of useful water as input layer, and permeability data from core well analysis of two wells as output layer cells, were used to train the network. After training the network with the data of these two wells, the core analysis data of another well was used to test the network, which in the network test stage, a correlation coefficient of 98% for permeability was obtained. With the help of this neural network, permeability was estimated for other wells in the field that no core data were obtained from. After estimating the permeability using neural network, its distribution and expansion were determined using Sequential Gaussian Simulation algorithm (SGS) in the field scale. According to the obtained model, the sandstone areas, which are mainly in zones B and D, are separated as reservoir areas and also the central and northwestern areas of the field, due to the higher average permeability, are areas prone to further excavations of the field.

Hydraulic fracturing is used as one of the most common methods of enhanced recovery oil and gas wells for the production of unconventional reservoirs as well as more production in reservoirs. Determining the direction of fracture propagation and the geometry of the fracture network created by the hydraulic fracturing process plays an important role in increasing the permeability of the reservoir. In this research, in order to better understand the mechanism of formation and expansion of cracks in the process of hydraulic fracturing, acoustic emission (AE) has been used. Experiments were performed on concrete block specimens under true triaxial stress conditions and acoustic emission monitoring was performed simultaneously with the injection of fluid into the specimen. In order to investigate the crack deflection behaviors in the hydraulic fracturing process and the effect of natural fractures on the formation, pre-cracked specimens have been used. The results of this study show the pre-manufactured crack reduce the fracture pressure of the specimens and the magnitude of the difference in horizontal stresses during the experiments clearly affects the crack propagation path during the hydraulic fracturing process. Acoustic emission monitoring during hydraulic fracturing experiments is a useful result, and answer in the analysis of the hydraulic fracturing process and the analysis of acoustic emission data show that the type of cracks created are often tensile cracks.

Equipment failure, repairs and maintenance play a decisive role in the availability of the entire system. This study presents a practical solution for analyzing equipment repairs and maintenance time and predicting equipment behavior. Expert experience has been used to estimate failure time and equipment repair time; therefore, this study has focused on estimating the repair time and repair rate of equipping the main lubricating oil pump in the gas turbine power generation system with the approach of entering human experience (HE). In the next step, an analysis of the Equipment›s Annual Availability Forecast is performed over a period of 20 years, thus, the critical years of the equipment are determined in terms of downtime by evaluating and reviewing the annual availability. For this purpose, a database of human knowledge and experience has been simulated to estimate the repair times used using fuzzy logic, and the whole process of repair times has been simulated by designing a neural-fuzzy system; which is used to estimate and predict equipment repair time. Then, the annual availability, time-dependent repair rate and other availability indicators are calculated using the Monte Carlo simulation method. The target model is the main lubricating oil pump system of the gas turbine unit of Abadan refinery in Iran. According to the results, applying preventive repairs at optimal intervals of 150 to 160 days, has a significant effect on increasing the availability of equipment and leads to a reduction in additional periodic inspections. Also, the minimum and the maximum system availability is predicted to be 96% and 99%, respectively.

One of the significant challenges in studying the oil and gas fields is assessing the fractured reservoirs. The existence of such fractures plays a crucial role in productivity and the amount of final offtake from hydrocarbon reservoirs, specifically, in carbonate formation. Hence, presenting a practical method in the identification and determination of fractures intensity is necessary. Image log tools are the first informative references in studying the fractured reservoir by which geologists can identify the details of layers, faults, fractures, and lithological status. FMI (Fullbore formation microimager) image logs are powerful tools in identifying fractures in wellbore surroundings. FMI image logs are indirect images with high resolution from wellbores. This study aims to employ the FMI image logs and optimal seismic attributes in assessing the fracture distribution in the field that we are about to study. To reach the target, 3D post-stack seismic data and FMI image log data of three wells (A, B, and C) that existed in the field are deployed. Two out of three wells (A and B) are utilized for determination and identification, and the third well (C) is utilized for validation which such identifications are carried out by “Geolog” software. As a result, fractures were identified in well A with a total of 152 open and closed fractures with a general trend of northeast-southwest, in well B with a total of 235 open and closed fractures with a general trend of north-south, and zones with high fracture density were determined. Therefore, with the usage of optimal seismic attributes in the “Hampson Russell”, the fracture density in the whole field was determined. Moreover, by using the distribution of fracture density, the field faults were determined. The results show that using the integration of FMI Image logs and seismic attributes is a practical method in studying and assessing the fracture distribution in fractured reservoirs.

The Gorgan Plain is located in the north of Iran and has mud volcanoes and high-pressure zones. There is limited information on Miocene and older sediments in this area and the available data on the Pliocene and younger sediments do not provide a complete understanding of the geology of these areas. The absence of marker fossils in the sediments has led to ambiguities in age determination and study of layers expansion in the study area. The Cenozoic sediments of Gorgan Plain which include Paleocene-Miocene sediments and Cheleken (Lower Pliocene), Akchagyl (Upper Pliocene), Apsheron (Lower Pleistocene), Baku (Upper Pleistocene) and Neo-Caspian (Holocene) formations, are mainly composed of sandstone and mudstone. This study shows that the thick Pliocene-Present sediments of Gorgan Plain were deposited at high rates, which was simultaneous with the increase in orogenic activity in the region and the gradual separation of the South Caspian Basin from open waters and the change of the sedimentary environment from marine to fluvial-deltaic. Sedimentation rates of Cenozoic deposits increase towards the coastal parts of the Gorgan Plain. The lowest sedimentation rates are in the Paleocene-Eocene deposits, and the highest rates are in the Baku Formation. High sedimentation rate is one of the controlling factors in the formation of mud volcanos and high-pressure zones in the region, and these rates can have a significant impact on the processes and elements of the petroleum system.

A significant amount of Iranian hydrocarbon resources is produced from fractured reservoirs with tight rock matrices. The structure of pores in these reservoirs is so complex. Very tiny pores and throats in nanometer sizes are responsible for reserving hydrocarbons. By understanding the structure of porous media and examining fluid flow inside these nanometer pores, we can better understand the porous media›s behaviour on larger scales. Investigating fluid flow in reservoir rocks requires three-dimensional structures with appropriate accuracy. However, using conventional methods to reconstruct a porous medium is expensive. On the other hand, as these structures become more complex, the ability of these methods to reconstruct pore network models decreases significantly. In recent years, with the advance in computer science, especially artificial intelligence, a new gate has been opened for reconstructing complex structures such as tight reservoir rocks. By implementing machine learning methods, three-dimensional pore-scale models can be created with high accuracy. The petrophysical properties of rocks can be calculated from them. One of these methods is the generative adversarial network (GAN), which has proven to reconstruct the pore structure of rocks. This study uses a GAN with convolutional layers to reconstruct the images obtained from FIB-SEM of a tight reservoir rock at the pore scale. Different realizations of the pore space are reconstructed by the trained GAN. The porosity and permeability of the reconstructed images are very close to the properties in the actual FIB-SEM image and have a deviation of 1.07% and 5.24%, respectively. It can be seen that GANs have a high capacity in rock reconstruction at the pore scale, especially for tight reservoirs.

Carbon dioxide (CO2) gas flooding has long been regarded as a popular method of improving oil recovery as it can reduce the carbon footprint in the atmosphere through carbon storage and CO2 sequestration. Miscible flooding is considered the most efficient way to reach the maximum oil recovery factor. However, not only do not all oil reservoirs experience pressures above miscibility but also due to difficulty in retaining reservoir pressure in the desired region, numerous miscible flooding operations experience pressure decline below minimum miscibility pressure (MMP). In these circumstances, a near-miscible process seems to be attainable and practical compared with a miscible injection. In the current study, we exclusively focus on pore-scale near-miscible CO2-oil displacement. In this regard, the effective near-miscible region is determined based on the available criteria in the literature. Then at the lower-pressure limit of the defined near-miscible region, Phase-Field coupled with the Navier-Stokes equation as the numerical approach is implemented to investigate the CO2-Oil displacement by capturing the diffusive interface properties and hydrodynamic properties of fluids. Quantitative analysis of results, to better realize the pore-scale mechanism of oil recovery demonstrated that if the pressure conditions are maintained throughout the modeling in the effective near-miscible pressure region, almost significant amounts of by-passed oil in the pores from small to large to be recovered and the oil recovery increased from 50% to more than 90% approaching the results of miscible gas injection. This outcome can accentuate the significance of near-miscible CO2-EOR in operation applications.

Nowadays, oil companies should minimize the cost of oil and gas production. This is where the concept of well and reservoir monitoring and management can be vital for oil companies. Performing pressure transient analysis is an effective way to evaluate the dynamic behavior of wells and reservoirs, as it has a larger radius of investigation than other methods, but these tests such as drill stem tests are costly and they are carried out at regular intervals and require special equipment. On the other hand, there is great attention to the use of artificial lift methods, especially the use of electrical submersible pumps in Iranian production fields and wells, to maintain the optimal production rate. These pumps are equipped with sensors and gauges that by using the potential of these sensors and checking the monitoring data, it is easier to analyze the pressure transient tests and check the dynamic performance of the well and reservoir. These sensors, which are installed in a unit called the downhole monitoring equipment under the electrical motor of the pump, record parameters such as intake pressure, discharge pressure, intake temperature, discharge temperature, etc. We can investigate the dynamic behavior of the well and reservoir in a certain period by analyzing the intake pressure versus time. This paper aims to use the data monitored by an electrical submersible pump from one of the southwestern fields of Iran and using the well-testing approach, so the dynamic behavior of the well and the reservoir are analyzed, and the key indicators and the performance of the well and the reservoir are examined and evaluated. After that, the result was compared with the information obtained from the drill stem test analysis.
By observing a very small percentage difference of about less than 8% between the results of the drill stem test and the electrical submersible pump test obtained, we can realize the usefulness of this approach.

The growing demand of the world for energy has caused to exploit the non-renewable resources using best techniques to enhance the recovery. Gas injection is one of the most common techniques in the exploitation of reservoir’s hydrocarbon. In addition, carbon dioxide is used widely for gas injection processes in oil reservoirs due to the good results obtained by executing numerous field projects. It should be noted that there exist problems in the process of gas injection into reservoirs. Low density and viscosity of gas is caused unfavorable movement of gas in the porous environment as well as early gas breakthrough in production wells. These factors cause gravitational separation as well as fingering phenomenon in reservoirs. To reduce gas injection problems, foam is replaced instead of gas as injecting material. Implementing the foam injection techniques would result better sweeping efficiency than solely gas flooding since foam has a higher apparent viscosity than gas. Fingering and early breakthrough of gas are reduced by foam injection in oilfields. In this research, by using deionized water at atmospheric temperature and pressure, in the first step, the foaming ability of the designed solutions was investigated. At a critical concentration of 0.24% (weight percent of surfactant), the effect of various parameters on the foaming ability were investigated. In addition, the stability of these solutions were measured based on the foam half-life and the optimal parameters of the different solution were determined to be injected into the micromodel. At the end, the solution obtained from the optimal parameters was prepared for running the injection scenarios. Then the amount of produced oil for different solutions was evaluated by micromodel experiments. It should be stated that presence of silica nanoparticles increased the half-life of the foam by about 25%. In addition, adding the Xanthan gum polymer to injecting foam structure along with the silica nanoparticle increased foam half-life to about 60%.

One of the most important concerns of the drilling industry is reducing the negative impacts of drilling cuttings of oil-gas wells. The increasing growth of the oil industry and drilling of oil and gas wells led to large amounts of drilling waste. In this study, drilling cuttings in concrete as a substitute for part of the cement used in the construction industry has been investigated. In this study, the possibility of using drilling cuttings of formations of Bangistan group, including siltstone and sandstone, in concrete production has been evaluated as a substitute for part of the cement in concrete. For this purpose, laboratory studies have been conducted to quantify the compressive strength of concrete samples. The optimum use of drilling cuttings is 25%, which it reduces the total strength of concrete samples by 34%. In addition, the effect of air ash and silica fume additives on improving the compressive strength of concrete samples containing drilling cuttings has been investigated. The test results showed that the addition of these materials had a significant effect on increasing the compressive strength of concrete samples containing 25% of drilling cuttings so that the lost compressive strength is reduced from 34% to less than 2%.