Cracking is a process whereby light and heavy hydrocarbons are broken down into lighter and useful hydrocarbons such as paraffin and olefins. Different types of cracking applied in petroleum and petrochemical industries like steam cracking, catalytic cracking and hydro cracking.

In steam cracking, a gaseous or liquid hydrocarbon feedstock like Ethane, naphtha, LPG or ethane is thermally cracked through the use of steam without the presence of oxygen. It is the principal industrial method for producing the lighter olefins such as ethylene and propylene.

The furnaces are the most important and expensive pieces of equipment in the unit and they are considered as the heart of the ethylene plant. Each furnace is composed of convection and radiation sections and reactions take place in the pyrolysis coils. The pyrolysis yield pattern is strongly affected by the hydrocarbon partial pressure, the temperature profile, and the residence time. Moreover, moderation of radiant heat flux ensures the coke formation rate is kept to a minimum. By carefully optimizing these parameters, the best yield patterns are obtained while retaining a long continuous on-stream time between successive decoke operations.

Overview

Objectives:

1-     Introducing steam cracker unit

2-     Pyrolysis Cracking theory and description

3-     Which items are more effective in cracking process?

4-     How important factors could affect on performance and yield?

5-     Inspection and control the effective factors in order to improve process parameters

6-     Advantages and disadvantages of periodic maintenance 

Teacher CV

Ourmazd Dehghani Khold (o_dehghani@jpcomplex.com) is PhD of chemical engineering. He holds a BS (2003) from the Persian Gulf University, Bushehr, Iran, MS (2012) from the Shiraz University, Shiraz, Iran, and a PhD (2019) from Shiraz University school of chemical and petroleum engineering, Shiraz, Iran, all in chemical engineering. He has more than 15 years’ experience at Jam Petrochemical Complex in South Pars Special Economy and Energy zone, Assaluyeh, Iran, as Head of 10^th Olefin plant (JPC) Operation department (one of the largest worldwide).

Aghil Parvin (a_parvin@jpcomplex.com) is chemical engineer. He holds a BS (2001) from the Shiraz University, Shiraz, Iran, MS (2011) from the Amirkabir University of Technology, Tehran, Iran all in chemical engineering. He has more than 15 years’ experience at Jam Petrochemical Complex in South Pars Special Economy and Energy zone, Assaluyeh, Iran, as responsible of Hot section and Furnaces at 10^th Olefin plant (JPC) Operation department (one of the largest worldwide).

As a popular programming language for statistical computing and machine learning, R provides an ideal environment for geoscientists and petroleum engineers to analyze oil and gas data using powerful data wrangling, visualization, and engineering packages, mathematical and machine learning algorithms, and reporting tools.

In the first part of the workshop, participants will learn about the essentials of R programming including data structures, importing data, functional programming, and data visualization. “Rmbal” package, a material balance tool for reservoir engineering studies, will be reviewed in the second part of the course. Fundamentals and the theoretical background will be discussed first, followed by some field examples to highlight the practical applications of the package.

By the end of the workshop, participants will be able to write codes to import, analyze, and visualize oil and gas data within the R environment.

Overview

1- Learn how to work with various types of data structures

2- Learn how to write technical functions

3- Learn the fundamentals of the generalized material balance model and its applications in reservoir engineering studies

4- Learn how to use “Rmbal” package for hydrocarbon-in-place estimation and reservoir performance forecasting

Teacher CV

Dr. Tabasinejad is currently a research associate in the “Tight Oil Consortium” at the University of Calgary where he conducts numerical simulations, production data analyses, and phase behavior modelling studies for oil and gas companies. He also consults and develops petroleum engineering software through his consulting firm, Susa Energy Inc.

He has more than 15 years of industry and research experience in the Canadian/US tight oil, shale gas and oilsands resources. His industry experience extends across research and development studies, EOR pilot design and operations, and field performance optimization. He has worked on various thermal projects in the Canadian oilsands including in-situ combustion, steam-assisted gravity drainage (SAGD), and solvent-aided SAGD modelling and optimization. He is currently involved in reservoir characterization, hydraulic fracturing and EOR studies of unconventional tight oil and gas reservoirs across North America.

His interests include phase behavior of hydrocarbons at nano-scale, enhanced oil recovery, production data analysis, machine learning applications in the oil and gas industry, and development of petroleum engineering software.

Dr. Tabasinejad holds a B.Sc. and an M.Sc. both in petroleum reservoir engineering from the Petroleum University of Technology (Iran), and a Ph.D. in petroleum engineering from the University of Calgary (Canada).

NMR signal measurements are being widely used to estimate reservoir petrophysical properties, which directly impact calculations of recoverable oil. NMR T2 distribution curves have plenty of information about rock properties; however most of the work done on this matter has been concentrated in studying only few parameters of that curve (logarithmic and geometric averages). This workshop is aimed at explaining how NMR can characterize the different reservoirs.

NMR has a good tool for low resisitivity reservoirs because of high contrast relaxation time between water and oil. The initial amplitude of the NMR spin echo or the area under the T2 distribution curve is proportional to the number of hydrogen protons (porosity) that are contained in the pore fluids with in the sensitive volume. MRIL tool is very adequate in the rugose bore hole. There are several correlations for estimating permeability such as Free Fluid and mean T2 model. Several models have been investigated as these models are not accurate in the carbonate due to diagenesis of the rock. T2 cut off is different for any reservoir which there is correlation for calculating it. Irreducible water saturation is one of the most important properties for secondary and tertiary recovery which can be determined by NMR. BY use of NMR can estimate viscosity in light and heavy oil reservoirs. Bypassed gas area can be detected in the stationary mode which is using DSM and TDA method.

Overview

1-     Principles and Fundamentals.

2-     Estimation of porosity including movalable and bound fluid.

3-     Permeability models.

4-     T2cut off and Irreducible Water Saturation.

5-     Viscosity (light and heavy oil).

6-     Pore size distribution and Gas detection.

Teacher CV

Shahin Parchekhari is now working in Reseach and Technology Department. He is Principal Petrophysicist. He has done numerous petrophysical evaluations in different fields with variety of different lithologies. He is experienced in logging operation, operation management and Head of Petrophysics. He is expert in NMR technology. He has written the first NMR paper in Iran in 2003.

He has registered two innovations. He has written about 16 scientific papers and two books. He is reviewer in Journal of Petroleum Science and Engineering. Shahin was a member of editorial board of Journal of exploration and production for three years. He attended in about 10 petrophisical courses in oversees countries. Shahin teaches petrophysical courses in industrial centers.

He was born in Abadan in 1972. He was graduated in Chemical Engineering at Petroleum University of Technology. Shahin has got his Master in Petroleum Engineering from University of Calgary. He is PHD candidate in Reservoir Engineering in University of Tehran now.

Oily sludge is one of the most significant solid wastes generated in upstream and downstream operations in petroleum industry. It is a complex emulsion of various petroleum hydrocarbons (PHCs), water, heavy metals, and solid particles. A considerable amount of oily sludge can be generated from the petroleum industry during its crude oil exploration, production, transportation, storage, and refining processes. Many methods have been investigated for dealing with PHCs in oily sludge either through oil recovery or sludge disposal, and some of them have been applied to field-scale treatment The selection of oily sludge treatment technologies should depend on sludge characteristics, treatment capacity, costs, disposal regulatory requirements, and time constraints. Some methods such as centrifugation, surfactant enhanced oil recovery, freeze/thaw, froth flotation, and bio-slurry treatment may be more suitable for treating oily sludge with high moisture content.

Overview

1. to introduce the source, characteristics, and environmental impact of oily sludge in the petroleum industry,
2. to summarize current treatment methods available for dealing with oily sludge,
3. to discuss the advantages and limitations of these methods, and
4. to discuss future development needs to meet resource recycling and waste disposal standards

Traditionally, separation columns in chemical industries are modeled based on the equilibrium approach. This approach assumes 100% separation efficiency on each tray or section of the packed column. Then, stage efficiency/HETP or overall column efficiency is applied to correct for this assumption. However, the uncertainty associated with this approach becomes significant for the separation of non-ideal multicomponent mixtures as the efficiency for such systems may vary from –∞ to +∞. The efficiency varies from component to component and stage to stage, and therefore the estimation of the efficiency using empirical correlations would be very limited.  An alternative approach is a non-equilibrium or rate-based model that eliminates the need for efficiency. This model calculates the true heat and mass transfer on each tray or section of a packed column using heat and mass transfer coefficients. In this workshop, the non-equilibrium model will be presented theoretically, and the implementation of this model in Aspen Plus will be presented.

Overview

1-      To present the theoretical basis of the non-equilibrium model

2-      To simulate a separation column using a non-equilibrium model in Aspen Plus