Core scale EME for IOR
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1 Core scale EME for IOR Experiment Modelling - Experiment Bergit Brattekås Post Doc The National IOR Centre of Norway
2 Aksel Hiorth Arne Stavland Chief scientist Martin A. Fernø THE PROJECT TEAM Arild Lohne Senior Research Engineer Kenny Walrond Daisy S. Aisyah Pål Østebø Andersen Post Doc Oddbjørn Nødland Bergit Brattekås Post Doc/Project manager / Tore L. Føyen Geir Ersland Associate Trond Vabø Håkon Haugland Petter D. Solberg Andreas G. Polden Anders S. Saunes Simon Reite
3 Aksel Hiorth Arne Stavland Chief scientist Martin A. Fernø WHY COLLABORATE? Arild Lohne Senior Research Engineer Kenny Walrond Daisy S. Aisyah Pål Østebø Andersen Post Doc Oddbjørn Nødland RESULTS HOW? Bergit Brattekås Post Doc/Project manager / Geir Ersland Associate Tore L. Føyen Trond Vabø Håkon Haugland Petter D. Solberg Andreas G. Polden Anders S. Saunes Simon Reite
4 WHY COLLABORATE? Dmitry Shogin (/ the National IOR Centre)
5 WHY COLLABORATE? EXPERIMENTS Test ideas and develop EOR methods NUMERICAL MODELLING Validate proposed hypotheses and explain observations CORE SCALE SIMULATION PREDICT IOR ON LARGER SCALES
6 RESULT EXAMPLE Sand pack in glass tube Direct visualization of front development FILTER: Glass Metal mesh Paper Glass wool #NOFILTER Porous medium Imbibing phase Spontaneous imbibition: the process where a less-wetting fluid is displaced out of a porous medium by a morewetting fluid due to capillary actions. Numerical modelling to quantify filter impact on flow.
7 RESULT EXAMPLE the simulations revealed that experimentally measured spontaneous imbibition was extremely sensitive to the experimental conditions, due to the high permeability and low capillarity of the sand packs. In particular, the presence of semi-permeable filters at the boundaries affected the imbibition rate and profiles, end recovery and counter-current production. Post Doc The National IOR Centre Pål Ø. Andersen 1,2, Bergit Brattekås 1,2, Kenny Walrond 2, Daisy S. Aisyah 2, Oddbjørn Nødland 1,3,4, Arild Lohne 1,4, Håkon Haugland 5, Tore L. Føyen 5, and Martin A. Fernø 5 MSc student /SINTEF Researcher
8 WHY COLLABORATE? Can modelling benefit from experiments?
9 Slide 9 RESULT EXAMPLE Polymer gel over 99% water Solvent Polymer solution 0.5% HPAM 0.04% Crosslinking agent (Cr(III)-Acetate) Gelant Properties similar to polymer solution flows through rock
10 RESULT EXAMPLE Gel placement in a fracture to reduce fracture conductivity Water leakoff Seright (2003)
11 RESULT EXAMPLE Gel placement in a fracture to reduce fracture conductivity CORE PLUGS HAVE THIS FAR BEEN FULLY SATURATED BY WATER. WHAT IF WE HAVE OIL IN THE MATRIX?
12 RESULT EXAMPLE Core plugs saturated with oil Polymer gel Sandstone * collaborative experimental research by the Reservoir Physics Research Group at and Dr. Randall S. Seright (PRRC, New Mexico Tech., USA). Published in SPE Jpurnal: SPE
13 RESULT EXAMPLE Oil drops Chalk core
14 RESULT EXAMPLE Experiment
15 Modelling Gel as a compressible medium Immobile solid fraction (polymer particles) and pore volume fraction (mobile solvent) Solvent concentration = solvent volume fraction = gel porosity Karman-Cozeny approach to estimate solvent gel permeability from φφ gggggg Gel structure carries a stress from surroundings, but is supported by pore pressure. Deforms according to net effective stress and a compressibility: Reducing pw Increased net stress Gel compresses Reduced gel porosity and permeability
16 Baseline matching Simulations
17 SPE MS RESULT EXAMPLE Solvent Leakoff during Gel Placement in Fractures: Extension to Oil- Saturated Porous Media New experiments Brattekås, B. 1, Ersland, G. 2 and Seright, R.S. 3 1 The National IOR Centre of Norway, Dept. of Energy Resources, University of Stavanger, Norway, 2 Dept. of Physics and Technology, University of Bergen, Norway 3 Petroleum Recovery Research Center (PRRC), New Mexico Insitute of Mining and Technology, NM, USA With oil in the pores: - Core shape matters - Dimensions matter - Core material matters How can we predict leakoff during gel injection in fractured reservoirs with an oil saturation? - Cannot rely on conventional model - Cannot implemet experimental findings directly
18 HOW? The dialog between experimentalist and modelers [in catchment hydrology] has been minimal to date. The experimentalist often has a highly detailed yet highly qualitative understanding of [the] processes While modelers often appreciate the need for hard data
19 Aksel Hiorth Arne Stavland Chief scientist Martin A. Fernø Arild Lohne Senior Research Engineer Kenny Walrond Daisy S. Aisyah Pål Østebø Andersen Post Doc Oddbjørn Nødland HOW? Bergit Brattekås Post Doc/Project manager / Geir Ersland Associate Tore L. Føyen Trond Vabø Håkon Haugland Petter D. Solberg Andreas G. Polden Anders S. Saunes Simon Reite
20 NUMERICAL MODELLING Aksel Hiorth Arne Stavland Chief scientist Martin A. Fernø EXPERIMENTS Arild Lohne Senior Research Engineer Kenny Walrond Daisy S. Aisyah Pål Østebø Andersen Post Doc Oddbjørn Nødland Bergit Brattekås Post Doc/Project manager / Geir Ersland Associate Tore L. Føyen Trond Vabø Håkon Haugland Petter D. Solberg Andreas G. Polden Anders S. Saunes Simon Reite
21 NUMERICAL MODELLING Aksel Hiorth Arne Stavland Chief scientist Martin A. Fernø EXPERIMENTS Arild Lohne Senior Research Engineer Kenny Walrond Daisy S. Aisyah Pål Østebø Andersen Post Doc Oddbjørn Nødland Bergit Brattekås Post Doc/Project manager / Geir Ersland Associate Tore L. Føyen Trond Vabø Håkon Haugland Petter D. Solberg Andreas G. Polden Anders S. Saunes Simon Reite
22 NUMERICAL MODELLING Aksel Hiorth Arne Stavland Chief scientist Martin A. Fernø EXPERIMENTS Arild Lohne Senior Research Engineer Kenny Walrond Daisy S. Aisyah Pål Østebø Andersen Post Doc Oddbjørn Nødland Bergit Brattekås Post Doc/Project manager / Geir Ersland Associate Tore L. Føyen Trond Vabø Håkon Haugland Petter D. Solberg Andreas G. Polden Anders S. Saunes Simon Reite
23 Aksel Hiorth Arne Stavland Chief scientist Martin A. Fernø EXPERIMENT Arild Lohne Senior Research Engineer Kenny Walrond Daisy S. Aisyah Pål Østebø Andersen Post Doc Oddbjørn Nødland MODELLING Bergit Brattekås Post Doc/Project manager / Geir Ersland Associate Tore L. Føyen Trond Vabø Håkon Haugland Petter D. Solberg Andreas G. Polden Anders S. Saunes Simon Reite
24 Department of Physics and Technology Lab to Field: CO 2 Foam EOR Field Pilots 2014 OBJECTIVE Cost-effective Roadmap for Success for CO 2 EOR implementation on Norwegian Continental Shelf through onshore field trials in Texas WHY TEXAS? - CO 2 is commercially available; w/infrastructure - Up-scaling; major challenge in oil recovery - Fraction of costs of off-shore field tests - Fast results: short inter-well distances - 30 years experience in Texas on CO 2 EOR East Seminole Well Location Map Ft. Stockton Well Location Map Simulation Results EFFICIENT Oil Production: CO 2 Foam EOR increase sweep efficiency COLLABORATORS U. of Bergen Total Stanford U. U. of Bordeaux Rice U. National IOR centre U. of Houston TU Delft Schlumberger Statoil UT Austin Shell FUNDING 15MNOK Norwegian Research Council, CLIMIT program Oil Industry (Shell, Total, Schlumberger, Statoil) + local independent operators Laboratory Results MORE Oil produced: CO 2 Foam EOR produces 10-30% additional oil after waterflooding FASTER Production Operational times reduced up to 90%. Laboratory Team Sunniva Fredriksen (PhD) Arthur Uno Rognmo (PhD) Michael Jian (PhD) Connie Wergeland (MSc) Anders Frøland (MSc) Andreas G. Polden (MSc) MORE CO 2 stored: Displace water to increase CO 2 storage volume Simulation Team Zachary Alcorn (PhD) Mohan Sharma (PhD) Lars Petter Grønvigh (MSc) Anna Bang (MSc) Max Castro (MSc) Stine Kristiansen (MSc)
25 The 2018 user partners and observers:
26 Acknowledgement: The authors acknowledge the Research Council of Norway and the industry partners, ConocoPhillips Skandinavia AS, Aker BP ASA, Eni Norge AS, Maersk Oil, a company by Total, Statoil Petroleum AS, Neptune Energy Norge AS, Lundin Norway AS, Halliburton AS, Schlumberger Norge AS, Wintershall Norge AS, and DEA Norge AS, of The National IOR Centre of Norway for support.
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