Iranian Journal of Oil & Gas Science and Technology
Volume:7 Issue: 3, Summer 2018

The aim of seismic inversion is mapping all of the subsurface structures from seismic data. Due to the band-limited nature of the seismic data, it is difficult to find a unique solution for seismic inversion. Deterministic methods of seismic inversion are based on try and error techniques and provide a smooth map of elastic properties, while stochastic methods produce high-resolution maps of elastic properties with the same probability. The current paper studies a stochastic method of seismic inversion which was applied to one of the Persian Gulf oilfields. Joint posterior distribution of elastic properties was calculated using Bayesian principle; then a sequential Gaussian simulation technique was performed to decompose the global probability function of elastic properties into some local probability functions at each trace location. The sampling of the local probability functions was performed, and two hundred realizations of the elastic properties were generated. The results of the stochastic inversion were found to be capable of modeling heterogeneities of the reservoir. The generated realizations provided the possibility to uncertainties assessment by calculating the variance of the elastic properties. It was found out that the uncertainty increased in locations far away from the well. Moreover, stochastic inversion, unlike deterministic one, was found to be capable of detecting thin beds (3.5 to 5.7 m) embedded within the reservoir.

Water flooding is one of the most influential methods for pressure maintenance and enhanced oil recovery. However, water flooding is likely to develop the formation of oilfield scale. Scale formation in reservoirs, due to the mixing of injection water and formation water, could cause formation damage and production limit. Therefore, it is necessary to simulate the compatibility of brine and injection water. Scale prediction is performed using many thermodynamic and/or kinetic based models. In this study, simulations with speciation (ion pairing) are studied, which is a thermodynamic based tool. The utilization of reservoir conditions, formation water analysis, and sea water analysis as the inputs in this method resulted to the accurate prediction of potential scales. In this study, the factors impacting on the scale potential such as pH, temperature, and mixing ratio were also investigated. The obtained results showed that calcite and aragonite were the major scale potential to precipitate. Finally, the results illustrated the important effect of pH and temperature on different scales formation.

In this paper, an advanced analysis of a novel hybrid compression-absorption refrigeration system (HCARS) for natural gas dew point control unit in a gas refinery is presented. This unit separates the heavy hydrocarbon molecules in the natural gas, which is traditionally carried out by natural gas cooling in a compression refrigeration cycle (CRS). The power input required for the refrigeration cycle compressors is usually provided by gas turbines. The low efficiency of gas turbines and the excessive power required for running the CRS compressors have made it crucial to investigate different means to decrease the energy consumption of this cooling system.
The waste heat of gas turbines flue gas can be recovered and utilized as the heating source for running an absorption refrigeration system (ARS) to provide part of the needed cooling load; hence, a hybrid compression absorption refrigeration system (HCARS) is launched. In this work, the application of HCARS is extended to the Fajr-e-Jam gas refinery currently operating with a CRS, and an advanced exergetic analysis of the proposed ARS is performed to further improve the proposed system. The effect of different variables on the performance of the proposed HCARS is also inspected. The proposed system and these analyses are novel for the gas refinery dew point control unit. Real CRS operational data are utilized in all the investigations, and proper means are presented for the validation of the simulation results.
The proposed system resulted in 63% additional cooling capacity of the HCARS (12550 KW) in comparison to the current CRS (7670 kW) for the equal natural gas consumption, which overall saves about 50000 SCMD of natural gas. Based on the exergy analysis of all the equipment, the exergy efficiency of the proposed ARS is 0.155. In addition, the parametric study of the effects of the gas turbine flue gas exit temperature and flow rate, ambient temperature, partial load operation of CRS, absorption solution flow rate, and concentration on the HCARS performance is carried out. These studies should provide the information needed for operating the proposed system in different situations

In this work, new correlations are proposed to predict the products yield of delayed coking as a function of CCR and temperature based on the experimental results. For this purpose, selected Iranian vacuum residues with Conradson carbon residue (CCR) values between 13.40-22.19 wt.% were heated at a 10 °C/min heating rate and thermally cracked in a temperature range of 400-500 °C in a laboratory batch atmospheric delayed coking reactor for 2 hours. The amount of distillate (C5+-500 °C) and coke yield were measured in all the experiments, and the gas (C1-C4) product yield was calculated based on mass balance between products and feedstock in each experiment. According to the developed functions, products yield changes with CCR value linearly and is a power function of temperature. The further investigation of the results show that by a 1 wt.% increase in CCR value, the distillate yield decreases by about 2.1 wt.%, but the amount of coke and gas yields rise by 1.2 wt.% and 0.9 wt.% respectively.

Random or incoherent noise is an important type of seismic noise, which can seriously affect the quality of the data. Therefore, decreasing the level of this category of noises is necessary for increasing the signal-to-noise ratio (SNR) of seismic records. Random noises and other events overlap each other in time domain, which makes it difficult to attenuate them from seismic records. In this research, a new technique is produced, by joining FX deconvolution (FXD) and a special kind of median filter in order to suppress random noise from seismic records. The technique is operated in some stages; firstly, FXD is tried to eliminate the Gaussian noise, and the median filter is fixed to diminish the spike-like noise. The synthetic dataset and field data examples (from an oil field in the southwest of Iran) have been employed to demonstrate that random noise reduction can be attained, while the signal content will not be destroyed considerably. The final results indicate the authority of the proposed strategy in suppressing random noises, whereas signal information is almost protected during the filtering.

The rate of penetration (ROP) is one of the vital parameters which directly affects the drilling time and costs. There are various parameters that influence the drilling rate; they include weight on bit, rotational speed, mud weight, bit type, formation type, and bit hydraulic. Several approaches, including mathematical models and artificial intelligence have been proposed to predict the rate of penetration. Previous research has showed that artificial intelligence such as neural network and adaptive neuro-fuzzy inference system are superior to conventional methods in the prediction of drilling rate. On the other hand, many complicated analytical ROP models have also been developed during recent years that are able to predict drilling rate with a high degree of accuracy. Therefore, comparing different approaches to find the most accurate model and assess the conditions in which each model works well can be highly effective in reducing drilling time as well as drilling cost. In this study, Hareland-Rampersad (HR) model, Bourgoyne and Young (BY) model, and an adaptive-neuro-fuzzy inference system (ANFIS) are employed to predict the drilling rate in the South Pars gas field (SP) offshore of Iran, and their results are compared to find the best ROP-prediction model for each formation. A database covering the drilling parameters, sonic log data, and modular dynamic test data collected from several drilling sites in SP are used to construct the mentioned models for each formation. The results show that when a large amount of data is available, the ANFIS is more accurate than the other approaches in predicting drilling rate. In the case of ROP models, BY model works considerably better than HR model for the majority of the formations. However, in formations where some drilling parameters are constant, but formation strength is variable, HR model shows better prediction performance than BY model.