Cuttings transport modeling in underbalanced oil drilling operation using radial basis neural network

AbstractUnderbalanced drilling is one of the drilling methods for better drilling according to its advantages. Cuttings transport effects on cost, time, and quality of oil/gas wells in drilling operation. Inefficient cleaning of wellbore may cause many drilling problems. Prediction and measuring of the cleaning efficiency in the wellbore annulus is a complex problem according to many effective factors. The field and experimental measurements of this parameter are time consuming and costly. This paper presents the radial basis function network (RBFN) method for prediction of cuttings concentration in underbalanced drilling condition to avoid the high cost experimental and field measurements. The average absolute percent relative error (AAPE) for train and test datasets in this study is 2.9e-13%, and 5.7% for the RBFN model. The comparison results of this study with literature review show the benefit of RBFN in prediction compared to back propagation neural network (BPNN) according to higher accuracy, faster training and simple network architecture. So, this network can be used in many mathematical problems for prediction and estimation instead of BPNN. Results of this study show that implementation of this developed model can be incorporated in drilling simulators for accurate estimation of cuttings concentration in wellbore instead of field and experimental measurements for hydraulic design in drilling operation

Oil Reservoir Permeability Estimation from Well Logging Data Using Statistical Methods (A Case Study: South Pars Oil Reservoir)

Permeability is a key parameter that affects fluids flow in reservoir and its accurate determination is a significant task. Permeability usually is measured using practical approaches such as either core analysis or well test which both are time and cost consuming. For these reasons applying well logging data in order to obtaining petrophysical properties of oil reservoir such as permeability and porosity is common. Most of petrophysical parameters generally have relationship with one of well logged data. But reservoir permeability does not show clear and meaningful correlation with any of logged data. Sonic log, density log, neutron log, resistivity log, photo electric factor log and gamma log, are the logs which effect on permeability. It is clear that all of above logs do not effect on permeability with same degree. Hence determination of which log or logs have more effect on permeability is essential task. In order to obtaining mathematical relationship between permeability and affected log data, fitting statistical nonlinear models on measured geophysical data logs as input data and measured vertical and horizontal permeability data as output, was studied. Results indicate that sonic log, density log, neutron log and resistivity log have most effect on permeability, so nonlinear relationships between these logs and permeability was done

Prevalence of the rs1801282 single nucleotide polymorphism of the PPARG gene in patients with metabolic syndrome

Objective: this study aimed to get the genotypic and allelic frequencies of rs1801282 in 179 volunteer donors and 154 patients with Metabolic syndrome (MetS) in Brasilia, Brazil and also examine the association with anthropometric, biochemical and hemodynamic variables in the latter group. MetS comprises a group of diseases resulting from insulin resistance, in-creased risk of type 2 diabetes and atherosclerotic cardiovascular disease. MetS is defined by the presence of increased visceral fat, atherogenic dyslipidemia (elevated triglycerides (TGL)), with decreased high density lipoprotein (HDL) and increased low density lipoprotein (LDL) levels, hypertension (BPH) and disturbances in glucose homeostasis representing a significant burden across the world due to the alarming increase in the incidence over the last decades besides their significant morbidity and mortality. Peroxisome proliferator activated receptor-gamma (PPARg) has been mentioned as a candidate gene for determining the risk of MetS. It is a member of the nuclear receptors superfamily and a ligand-activated transcription factor, which regulates the expression of genes involved in the network lipogenesis and adipogenesis, insulin sensitivity, energy balance, inflammation, angiogenesis and atherosclerosis. Among the PPARG genetic variants, single nucleotide polymorphism rs1801282 has been the most extensively studied one since it was first described by Yen and cols. in 1997. This polymorphism is characterized by the replacement of a proline (CCC) to an alanine (GCA) at codon 12 of exon B, due to the exchange of a cytosine with a guanine. The Ala allele frequency varies in different ethnic groups. Materials and methods: DNA was extracted using Chelex-100 method and determinations of genotypes were performed by allele-specific chain reaction. Results: the distribution of genotype frequency of the MetS group was not statistically different from the frequency in the donor population at large. In the first group, genotype frequency was CC to 0.869 and 0.103 for CG, while allelic frequencies were 0.948 for C and 0.052 for G allele. In the group of donors, the genotype and allele frequencies were 0.882 for CC, 0.117 to CG; and 0.941 to 0.059 for G and C, respectively. GG genotype was not found in any of the two groups. The genotype distribution and allele frequencies were in Hardy-Weinberg equilibrium. No marker could be detected from the analysis of anthropometric, biochemical and hemodynamic variables in the MetS group. Conclusion: our data suggest that this polymorphism is not correlated with predisposition to MetS. The results obtained on a small sample of the population of Brasilia, corroborate the data reported in the literature on the prevalence of this polymorphism in PPAR in populations of different ethnic origins

WEAR RATE PREDICTION OF GRINDING MEDIA USING BPNN AND MLR MODELS IN GRINDING OF SULPHIDE ORES

Nowadays steel balls wear is a major problem in mineral processing industries and forms a significant part of the grinding cost. Different factors are effective on balls wear. It is needed to find models which are capable to estimate wear rate from these factors. In this paper a back propagation neural network (BPNN) and multiple linear regression (MLR) method have been used to predict wear rate of steel balls using some significant parameters including, pH, solid content, throughout of grinding circuit, speed of mill, charge weight of balls and grinding time. The comparison between the predicted wear rates and the measured data resulted in the correlation coefficients (R), 0.977 and 0.955 for training and test data using BPNN model. However, the R values were 0.936 and 0.969 for training and test data by MLR method. In addition, the average absolute percent relative error (AAPE) obtained 2.79 and 4.18 for train and test data in BPNN model, respectively. Finally, Analysis of the predictions shows that the BPNN and MLR methods could be used with good engineering accuracy to directly predict the wear rate of steel balls