Advanced models in industrial praxis - from process design to process optimization

Transcription

1 Advanced models in industrial praxis - from process design to process optimization Georgios K. Folas

2 Introduction to Statoil We are headquartered in Norway with 30,300 employees worldwide and operations in 42 countries Equity production of 1,888 thousand barrels of oil equivalent per day in 2010 Market capitalization of about USD 75 billion About 22 billion boe in proven resources Operator for 40 producing oil and gas fields One of the world's largest net sellers of crude oil and the second largest exporter of gas to Europe The world's largest operator in waters deeper than 100 meters Source: 2010 annual report ( 2

3 Main Challenges for Process Design Design or operations is done by use of commercial simulators (for example Hysys or PROII) and use of standard methods such as SRK or PR EoS As system complexity increases, different models are required to overcome the shortcomings of standard methods Increased number of accelerated and non-standard (also in terms of system compexity) projects coming into stream Higher demand for process optimization and reduction in design margins Need to implement new tools in a way that can be easily utilized by our process engineers Cape-Open packages Expand internal knowledge and enhance internal tools 3-

4 Our Goals within Thermodynamics Obtain experimental data and models that will help us understand the fundamental phase behaviour Collaborate with academia in order to develop advanced models/test existing models for achieving confidence in design and reduction in OPEX by reducing margins while keeping operations safe Develop a reliable internal toolbox that can be used with confidence within Statoil Collection and systematization of operational experience Information sharing and implementation 4-

5 Example of Academic Partners DTU CPA / Cape-Open NTU Athens UMR PR model Ample of research projects (Experimental and theoretical) 5-

6 Examples of use of glycol in Statoil TEG is commonly used as solvent for water dew point control by absorption Glycol is commonly used as heating and cooling medium MEG hydrate inhibition Sleipner condensate to Kårstø (MEG) Troll/Kvitebjørn Kollsnes multiphase pipelines (MEG) Midgard Åsgard B flow line (MEG) Huldra to Heimdal (MEG) MEG and TEG regeneration processes Kollsnes/Snøhvit MEG regeneration processes

7 Glycol solutions for water dew point control In Statoil TEG is used to obtain dew point specifications on all offshore installations that delivers gas to rich/dry gas pipelines Case Study A: Gas Inhibited with methanol from some wells for pipeline transportation dried and exported in facility A. Question 1: Where does methanol end up? Question 2: What is the methanol content of gas? 7-

8 Case A: PFD Typical Dew Point Control Cricondenbar / Cricondentherm 8-

9 Case A: PFD Typical CS =1.3*CPA Cold Separator operating at -18 to -30 oc CPA = 3*CS CPA = CS 9-

10 2 models 2 results, which one to trust??? Key systems are: methanol water TEG water METHANOL solubility in HCs at low Temperatures

11 MeOH Solubility at low Temperatures MeOH H2O nc3 Løkken et al., IGRC Paris 2008 MeOH nc5 Folas, Ph.D. Thesis DTU, Lyngby, 2006

12 MeOH solubility at low T - Performance of Commercial Simulator vs CPA MeOH C6 MeOH C7

13 Typical Examples of Use of CPA in Statoil water content of natural Gas Water dew point temperature The highest temperature, at a specified pressure, where water spontaneously can condense from the natural gas Aqueous dew point temperature The highest temperature, at a specified pressure, where a solution of water and trace chemicals (e.g. monoethylene- and triethylene glycol) can spontaneously condense from the natural gas Frost point temperature The highest temperature, at a specified pressure, where ice can spontaneously precipitate from natural gas Hydrate point temperature The highest temperature, at a specified pressure, where natural gas hydrate can spontaneously form in a gas mixture Maximum water precipitation temperature - The highest temperature, at a specified pressure, where water can spontaneously precipitate in any form (liquid water, aqueous solution, ice or hydrate) in a gas mixture 13 -

14 Evaluation of condensation/freeze out in pipelines Hydrocarbon dew point Hydrocarbon buble point aqueous dew point frost point hydrate point TEG freezing point Pressure [bar] Temperature [ºC] Phase behaviour of natural gas with traces of water (40 ppm(mole)) and TEG (0.5 ppm(mole)), NG composition (mole): 85 % C1, 10 % C2, 4 % C3, 0.5 % nc4, 0.5 % ic4 Løkken et al., IGRC Paris

15 Water Content for Natural Gas W ater content [ppm ( m o l)] Measurement (Karl Fischer) CPA-EoS GERG-water-EoS Empirical correlation of Bukacek [17] Chart of McKetta and Wehe [14]) Temperature [ºC] Løkken et al., IGRC Paris

16 Thermodynamic models in Oil industry System complexity EoS Why back to 1980? Association theories «Predictive» EoS/GE: Zero Reference Pressure Eliminate double EoS/GE combinatorial effect good results for size asymmetric systems

17 UMR-PRU Dew Point Determination in NG The Universal Mixing Rules (UMR) a E E RTb G Staverman Guggenheim G residual RT RT i a b i i b b ij i b j 1/ 2 i x i x j b b ij 1/ 2 j 2 2 PRU EoS: Peng-Robinson Activity coefficient model: UNIFAC type model 17 -

18 Phase envelope of a typical natural gas Bubble point line Dense phase EOS = SRK Penelou 120 Cricondenbar Dew point line Liquid Crit P In offshore processing the cricondenbar specification must be fulfilled to avoid condensation in the pipelines Gas Two phases Cricondentherm In onshore processing the cricondentherm specification must be fulfilled to achieve desired quality for the sales gas Temperature/ C 18 -

19 Dew points determination of natural gases Direct and indirect techniques Two common techniques for dew point determination Direct: Manual or automatic devices (field instruments, chilled mirror principle, gas condensation by cooling) Subjective to the human eye to detect condensate (manual) Analyzer s condensate detection threshold and sensitivity setting of the automatic instrument (automatic) Disturbances due to presence of traces of water, glycols, methanol Can only measure dew point at one pressure at the time Indirect: Dew point determination based on gas composition analysis (GC) and a thermodynamic model (EoS) How extended GC-analysis? Which model? 19 -

20 Results for Real Gases RG01 RG P [bar] 60 Exp. Pts UMR PRU P [bar] 60 Exp. Pts UMR PRU 40 SRK PC SAFT 40 SRK PC SAFT T [K] T [K] Data taken for the literature: Avila et al., Ind. Eng. Chem. Res. 2006, 45, (samples of natural gas were taken at the inlet of the Maghreb Europe pipeline in Spain, with a detailed GC analysis for 35 components up to C12) E.Voutsas ESAT

21 Modelling issues: Extended CC-analysis Rich gas from Field with C6+ fraction of 0.38 mol% How detailed should the gas analysis be? Analysis up to C6, C7,, C12? Deviations up to 20 C at the cricondentherm Deviations up to 10 bar at the cricondenbar Pressure [bar] GC-analysis up to C5 (C6+ fraction) GC-analysis up to C6 (C7+ fraction) 40 GC-analysis up to C Temperature [ C] 21 -

22 Real Gases Characterization on the basis of PNA distribution The C7+ fraction is split into three sub-fractions: C7, C8 and C9. All components with boiling points within a specific range in each sub-fraction were put into a selected: Paraffin: nc7 for C7, nc8 for C8 and nc9 for C9, Naphthene: cc6 for C7, cc7 for C8 and cc8 for C9, and Aromate: benzene for C7, toluene for C8 and m-xylene for C9. Synthetic gases Average ΔT Average ΔP SRK 2.1 K 4.8 bar Synthetic gases: 27 data sets published in the literature UMR-PRU 1.3 K 2.8 bar Real gases Average ΔT Average ΔP SRK/PNA 3.8 K 2.6 bar Real gases: 46 data sets for real gases from various fields/plants measured by Statoil at Rotvoll UMR-PRU 2.7 K 1.5 bar 22 -

23 EoS/GE online monitoring - Field Test o Improved on details of GC analysis o Detailed a fluid characterization method o Pilot test on going

24 Potential freeze out risk in the Snøhvit LNG process CO 2 removal Water removal HHC removal MEG injection LNG Product with trace amounts of CO 2 and heavy hydrocarbons

25 Prediction of C6 solubility along the SLV loci, C1 C6 Longman, Ph.D. Thesis NTNU, Trondheim,

26 Solubility of HHC in C1 at LNG related conditions Longman, Ph.D. Thesis NTNU, Trondheim,

27 Solubility of HHC in C1 at LNG related conditions Longman, Ph.D. Thesis NTNU, Trondheim,

28 Conclusions - Messages Advanced models are establishing themselves within Statoil. We see a big benefit of applying CPA in specific calculations, ranging from process design to troubleshooting. Statoil is highly focused in making advanced models fully available/accecible to engineers in a CAPE-OPEN form. We encourage implementation of those models from vendors. In the absence of experimental data advanced models are shown to provide good «first indications». Do not underestimate the classical EoS. The weakest link is the vdw1f mixing rules. Correcting the mixing/combining rules offers new possibilities for the applicability of cubic EoS coupled with well understood and simple activity coefficient models 28 -

29 Thank you Presentation title Georgios K. Folas Senior Process Engineer tel: