Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

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Unit Operations Of Chemical Engineering

소형입자 유화중합(Miniemulsion Polymerization)의 최근 연구동향

Application of machine learning and artificial intelligence in oil and gas industry

Nature-inspired algorithms are a class of effective tools for solving optimization problems and these algorithms have good properties such as simplicity, flexibility and high efficiency. Despite their popularity in practice, a mathematical framework is yet to be developed to analyze these algorithms theoretically. This work intends to analyze nature-inspired algorithms both qualitatively and quantitatively. We briefly outline the links between self-organization and algorithms, and then analyze algorithms using Markov chain theory, dynamic system and other methods. This can serve as a basis for building a multidisciplinary framework for algorithm analysis.

Nature-Inspired Algorithms and Applied Optimization

Improved predictions of wellhead choke liquid critical-flow rates: Modelling based on hybrid neural network training learning based optimization

The hybrid-polymer nanocomposite poly(styrene-methyl methacrylate- acrylic acid) /nanoclay was synthesized by miniemulsion polymerization for novel use as a drilling fluid additive. Three low-solid-drilling fluids (bentonite-based; natural polymer-based; nanoclay-based) were formulated using the hybrid nanocomposite as an additive and their rheological performance compared. The polymer/clay hybrid nanoparticles significantly improve rheological and filtration properties of the drilling fluids and they remain stable at high pressure, high temperature and harsh salinity conditions. The fluids’ filtration properties improve as the concentration of the polymer/nanoclay-hybrid-nanoparticles increases. The clay-based nanocomposite additive had the highest positive impact on the rheological behavior of low-solids polymer-based drilling fluid. Analysis of rheological properties and filtration loss of the drilling fluids suggest that the optimum nanoclay concentration in the hybrid-polymer nanocomposite is about 5 wt.%.

A hybrid nanocomposite of poly(styrene-methyl methacrylate- acrylic acid) /clay as a novel rheology-improvement additive for drilling fluids

Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm

The suspension properties of drilling fluids containing pure and polymer-treated (partially-hydrolyzed polyacrylamide (PHPA) or Xanthan gum) clay nanoparticles are compared withthose of a conventional water-and-bentonite-based drilling fluid, used as the referencesample. Additionally, the mud weight, plastic viscosity, apparent viscosity, yield point, primary and secondary gelatinization properties, pH, and filtration properties of the various drilling fluids studied are also measured and compared. The performance of each drilling fluid type is evaluated with respect in terms of its ability to reduce mud cake thickness and fluid loss thereby inhibiting differential-pipe-sticking. For that scenario, the mud-cake thickness is varied, and the filtration properties of the drilling fluids are measured as an indicator of potential well-diameter reduction, caused by mud cake, adjacent to permeable formations. The novel results show that nanoparticles do significantly enhance the rheological and filtration characteristics of drilling fluids. A pure-clay-nanoparticle suspension, without any additives, reduced fluid loss to about 42% and reduced mud cake thickness to 30% compared to the reference sample. The xanthan-gum-treated-clay-nanoparticle drilling fluid showed good fluid loss control and reduced fluid loss by 61% compared to the reference sample. The presence of nanofluids also leads to reduced mud-cake thicknesses, directly mitigating the risks of differential pipe sticking. Cited as : Mohamadian, N., Ghorbani, H., Wood, D., Hormozi, H.K. Rheological and filtration characteristics of drilling fluids enhanced by nanoparticles with selected additives: an experimental study. Advances in Geo-Energy Research, 2018, 2(3): 228-236, doi: 10.26804/ager.2018.03.01

/pdf/rheological-and-filtration-characteristics-of-drilling-10uaduseke.pdf

Rheological and ﬁltration characteristics of drilling ﬂuids enhanced by nanoparticles with selected additives: an experimental study

None of the various published models used to predict oil production rates through wellhead chokes from fluid composition and pressures can be considered as a universal model for all regions. Here, a model is provided to predict liquid production-flow rates for the Reshadat oil field offshore southwest Iran, applying a customized genetic optimization algorithm (GA) and standard Excel Solver non-linear and evolutionary optimization algorithms. The dataset of 182 records of wellhead choke measurements spans liquid flow rates from < 100 to 30,000 stock tank barrels/day. Each data record includes measurements of five variables: liquid production rate (QL), wellhead pressure, choke size, basic sediment and water, and gas–liquid ratio. 70% of the dataset (127 data records) was used for training purposes to establish the prediction relationships, and 30% of the dataset (55 data records) was utilized for independently testing the accuracy of the derived relationships as predictive tools. The methodology applying either the customized GA or standard Solver optimization algorithms, demonstrates significant improvements in QL-prediction accuracy with the lowest APD (− 7.72 to − 2.89), AAPD (7.33–8.51), SD (288.77–563.85), MSE (91,871–316,429), and RMSE (303.1–562.52); and the highest R2 (greater than 0.997) compared to six previously published liquid flow-rate prediction models. As a general result, the novel methodology is easily applied to other field/reservoir datasets, to achieve rapid practical flow prediction applications, and is consequently of worldwide significance.

/pdf/predicting-liquid-flow-rate-performance-through-wellhead-2888iunuhi.pdf

Hamzeh Ghorbani

Papers

Improved predictions of wellhead choke liquid critical-flow rates: Modelling based on hybrid neural network training learning based optimization

A hybrid nanocomposite of poly(styrene-methyl methacrylate- acrylic acid) /clay as a novel rheology-improvement additive for drilling fluids

Prediction of gas flow rates from gas condensate reservoirs through wellhead chokes using a firefly optimization algorithm

Rheological and ﬁltration characteristics of drilling ﬂuids enhanced by nanoparticles with selected additives: an experimental study

Predicting liquid flow-rate performance through wellhead chokes with genetic and solver optimizers: an oil field case study