Application of Seismic Attribute Technique to estimate the 3D model of Hydraulic Flow Units: A case study of a gas field in Iran

One of the most important steps in evaluation and development of hydrocarbon reservoirs is the mapping of their characteristics. Nowadays, Seismic Attribute Technique is used to build parameters of hydrocarbon reservoirs in inter-well spaces. One of these parameters is the Flow Zone Index (FZI) that has a significant effect on different stages of evaluation, completion, primary and secondary production, reservoir modeling and reservoir management. The aim of this study is to introduce an equation using seismic attribute and FZI log in wells and then generalize it to predict FZI throughout the reservoir. For this purpose, acoustic impedance (AI) volume as an external attribute was created while internal attributes were computed from seismic data. After that, The best set of attributes was determined using stepwise regression after which seismic attributes were applied to multi-attribute analysis to predict FZI. Then, the attribute map resulted from multi-attribute analysis was used to interpret the spatial distribution of the gas bearing carbonate layers. Finally, the optimum number of Hydraulic Flow Units (HFU) was determined by analyzing the break point in the plot of cumulative frequency of FZI for wells and was generalized all over the reservoir by using the 3D HFU model. The results demonstrated that multi-attribute analysis was a striking technique for HFU estimation in hydrocarbon reservoirs that reduces cost and increases rate of success in hydrocarbon exploration. Distribution of producible hydrocarbon zones along with the seismic lines around the reservoir was characterized by studying this model which can help us in choosing the location of new wells and more economical drilling operations.     

Jitter and metastability analysis and modeling of multilevel bang-bang phase detector

Bang-bang phase detector (BBPD) is one of the essential blocks in the phase-locked loop and clock and data recovery that are used in transceivers. But BBPD has the metastability problem as data change in timing window. It suffers from not only metastability failure but also quantization noise, which causes output jitter. In this paper, the novel model is presented to evaluate the effect of both metastability and jitter on ML-BBPD, and also, it is shown that multilevel BBPD (ML-BBPD) has the improved quantization noise in comparison with the Alexander BBPD. In this model, it is shown that by increasing the oversampling ratio, the quantization noise is decreased, and with the metastability effect and the increment of quantization steps, the characteristic curve of the ML-BBPD becomes more smoothed. Also, the output jitter of ML-BBPD, in which metastability failure is diminished, is modeled. The error function in the model is simulated at system level and compares with the results achieved from simulation at circuit level to prove the validity of the proposed model. The simulation is done in TSMC 65-nm CMOS technology under 1-V supply voltage to compare the characteristic of ML-BBPD for various number of sampling clocks.     

Mathematical modelling of a hydrocyclone for the down-hole oil–water separation (DOWS)

In this study, a mathematical model is developed to predict the efficiency of a down-hole oil–water separation hydrocyclone. In the proposed model, the separation efficiency is determined based on droplet trajectory of a single oil droplet through the continuous-phase. The droplet trajectory model is developed using a Lagrangian approach in which single droplets are traced in the continuous-phase. The droplet trajectory model uses the swirling flow of the continuous-phase to trace the oil droplets. By applying the droplet trajectory, a trial and error approach is used to determine the size of the oil droplet that reaches the reverse flow region, where they can be separated. The required input for the proposed model is hydrocyclone geometry, fluid properties, inlet droplet size distribution and operational conditions at the down hole. The model is capable of predicting the hydrocyclone hydrodynamic flow field, namely, the axial, tangential and radial velocity distributions of the continuous-phase. The model was then applied for some case studies from the field tested DOWS systems which exist in the literature. The results show that the proposed model can predict well the split ratio and separation efficiency of the hydrocyclone. Moreover, the results of the proposed model can be used as a preliminary evaluation for installing a down-hole oil–water separation hydrocyclone system in a producing well.     

Study of droplet behaviour along a pulsed liquid–liquid extraction column in the presence of nanoparticles

In this article, droplet size and its distribution along a pulsed liquid–liquid extraction column, is studied where SiO₂ nanoparticles with concentrations of 0.01, 0.05 and 0.1 vol.% and different hydrophobicities are applied to the dispersed phase. Using ultrasonication, nanoparticles were dispersed in kerosene as the base fluid. Nanofluids' stability was ensured using a UV–vis spectrophotometer. Some 22,000 droplets were measured by photographic technique and results were compared with systems containing no‐nanoparticles (Water–Acetic acid–Kerosene). Addition of nanoparticles changed the droplet shape from ellipsoidal to spherical. Also, there was a marked influence on droplet breakage and droplet coalescence at 0.01 vol.%, and 0.05 vol.% or higher volume fractions, respectively. © 2012 Canadian Society for Chemical Engineering     

Polymeric mixed matrix membranes containing zeolites as a filler for gas separation applications: A review

Polymeric membrane technology has received extensive attention in the field of gas separation, recently. However, the tradeoff between permeability and selectivity is one of the biggest problems faced by pure polymer membranes, which greatly limits their further application in the chemical and petrochemical industries. To enhance gas separation performances, recent works have focused on improving polymeric membranes selectivity and permeability by fabricating mixed matrix membranes (MMMs). Inorganic zeolite materials distributed in the organic polymer matrix enhance the separation performance of the membranes well beyond the intrinsic properties of the polymer matrix. This concept combines the advantages of both components: high selectivity of zeolite molecular sieve, and mechanical integrity as well as economical processability of the polymeric materials. In this paper gas permeation mechanism through polymeric and zeolitic membranes, material selection for MMMs and their interaction with each other were reviewed. Also, interfacial morphology between zeolite and polymer in MMMs and modification methods of this interfacial region were discussed. In addition, the effect of different parameters such as zeolite loading, zeolite pore size, zeolite particle size, etc. on gas permeation tests through MMMs was critically reviewed.     

Prediction of thiophene removal from diesel using [BMIM][AlCl4] in EDS Process: GA-ANFIS and PSO-ANFIS modeling

In this study, the treatment capability of 1-butyl-3-methylimidazolium tetrachloroaluminate [BMIM] [AlCl4] ionic liquid was investigated in a diesel fuel containing thiophene. The effects of operating parameters including the volumetric ratio of ionic liquid to model fuel, extraction temperature and time have been studied. Then, a neuro-fuzzy inference system (ANFIS) tuned by particle swarm optimization (PSO) algorithm has been developed to predict the sulphur removal from diesel fuel using [BMIM][AlCl4] in EDS process. The outputs were in a good agreement with the experimental data, and the model tuned by PSO predicts the data better than ANFIS and ANFIS tuned by GA.     

Common FABP4 Genetic Variants and Plasma Levels of Fatty Acid Binding Protein 4 in Older Adults

We examined common variants in the fatty acid binding protein 4 gene (FABP4) and plasma levels of FABP4 in adults aged 65 and older from the Cardiovascular Health Study. We genotyped rs16909187, rs1054135, rs16909192, rs10808846, rs7018409, rs2290201, and rs6992708 and measured circulating FABP4 levels among 3190 European Americans and 660 African Americans. Among European Americans, the minor alleles of six single nucleotide polymorphisms (SNP) were associated with lower FABP4 levels (all p ≤ 0.01). Among African Americans, the SNP with the lowest minor allele frequency was associated with lower FABP4 levels (p = 0.015). The C-A haplotype of rs16909192 and rs2290201 was associated with lower FABP4 levels in both European Americans (frequency = 16 %; p = 0.001) and African Americans (frequency = 8 %; p = 0.04). The haplotype combined a SNP in the first intron with one in the 3′untranslated region. However, the alleles associated with lower FABP4 levels were associated with higher fasting glucose in meta-analyses from the MAGIC consortium. These results demonstrate associations of common SNP and haplotypes in the FABP4 gene with lower plasma FABP4 but higher fasting glucose levels.     

Fatty Acid Triangulation in Albumins Using a Landmark Spin Label

Several spatial correlations of up to six fatty acid (FA) binding sites in albumins were found by double electron‐electron resonance (DEER). A strategy was used that combines spin‐labeling and spin‐probing techniques in electron paramagnetic resonance (EPR) spectroscopy. This is here achieved by introducing an additional covalent landmark spin (LS) label to the self‐assembled system of EPR‐active, paramagnetic stearic acid derivatives and albumins. Therefore, a cysteine specific, paramagnetic LS that was attached to the albumin surface at a unique position (Cys34) provides a fixed topological reference point for monitoring statistical ligand uptake. We propose that the determination of nanoscale distance distributions emerging between the LS and EPR‐active fatty acid derivatives generally allows for the direct observation of individually occupied binding sites in solution. Essentially, several binding pockets, groups of them and evidence for ligand‐induced allosteric modulation can be traced from such FA‐LS interspin correlations. Experimental results were substantiated with theoretical predictions from molecular dynamics (MD) simulations. It was observed that all binding sites in an albumin ensemble may be statistically filled even at the lowest level of ligand loading. This approach generally bears the potential for mapping occupation states of individual ligand binding sites in proteins using such spin‐labeled ligands.     

Performance prediction of a specific wear rate in epoxy nanocomposites with various composition content of polytetrafluoroethylen (PTFE), graphite,short carbon fibers (CF) and nano-TiO2 using adaptive neuro-fuzzy inference system (ANFIS)

Specific wear rate of composite materials plays a significant role in industry. The processes to measure it are both time and cost consuming. It is essential to suggest a modeling method to predict and analyze the effectiveness of parameters of specific wear rate. Nowadays, computational methods such as Artificial Neural Network (ANN), Fuzzy Inference System (FIS) and adaptive neuro-fuzzy inference system (ANFIS) are mainly considered as applicable tools from modeling point of view. ANFIS present integrate performance of neural network (NN) and fuzzy system (FS). Present paper investigates performance prediction of a specific wear rate of epoxy composites with various composition using ANFIS. The obtained results showed that ANFIS is a powerful tool in modeling specific wear rate. The obtained mean of squared error (MSE) for testing sets in present paper obtained 0.0071.