1 Convolutional Time-Lapse Seismic Modeling for CO 2 Sequestration at the Dickman Oilfield, Ness County, Kansas Jintan Li Advisor: Dr. Christopher Liner.

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Presentation transcript:

1 Convolutional Time-Lapse Seismic Modeling for CO 2 Sequestration at the Dickman Oilfield, Ness County, Kansas Jintan Li Advisor: Dr. Christopher Liner May 2 nd, 2012

2 Outline Background/Introduction Methods Completed results Summary and future Work

3 Background CO 2 capture and storage (CCS) – was first discussed in late 1970s (Baes, et al.) – has aroused great interest with increasing concern of global warming CCS candidates: – Deep saline aquifer ( storage capacity is estimated to range from 1, ,000 giga tons worldwide, IPCC,2005 ) – Depleted hydrocarbon reservoirs – Coal beds

Previous Studies Sleipner field: Norwegian North Sea – 1 st field test to inject CO 2 into a saline aquifer (1996) Salah project: in Algeria – an industrial-scale CO 2 site, with injection into an aquifer of a gas producing field (since 2004) Weyburn Field : Canada – has injected CO 2 into depleted gas reservoirs ( since 2000) Others (Germany,etc.) 4

5 Motivation Previous and on-going time lapse studies rarely involve the output of a commercial reservoir simulator as input to the seismic earth model. – difficult issues of scaling and smoothness in geological, flow simulation, and seismic earth models. seismic scale Flow simulation scale Log scale Calibration

6 Introduction Study area: Dickman Field, Kansas – Geology: carbonate build-ups, karst feature (Mississippian angular unconformity) – Two CO 2 capture and storage targets Deep Saline Aquifer – primary ( ft) Shallower depleted oil reservoir – secondary (~100ft)(in this study) Flow simulation Residual gas trapping Solubility trapping Free CO 2 geological trapping (hydrodynamic)

Dickman Field Site Live 3D area Type log 7

Type Log for Dickman Project Area Deep saline reservoir This study Depleted oil reservoir Lower Cherokee Sandstone Cherokee Group Fort Scott Limestone Gilmore City Limestone Viola Limestone Arbuckle Group Mississippian Limestone Osage Formation ( Liner et al.,2009) 8

3D Seismic area, time slice at the Mississippian ( Liner et al.,2009)Cross section courtesy of Tom Bjorklund. 9

10 Flow Simulation Simulate liquid and gas flow in real world conditions Simulator : Generalized equation of state compositional simulator (GEM)- by CMG (computation modeling group). – Used for : CO 2 capture and storage (CCS) CO2 enhanced oil recovery Well data +core data +seismic Flow simulation

Flow Simulation Geology model for five aquifer layers 11

12 PermK (md) Sim Layer No.VerticalPermPorosity(%)Formation Name md18.2Shallow Reservoir layers md20.0Two Seal Layers Horizontal Perm10.3Ford Scott Limestone Horizontal Perm19.1Cherokee Horizontal Perm16.5Lower Cherokee Horizontal Perm14.8Mississippian Unconformity Horizontal Perm20.0Mississippian Porous Carbonate Horizontal Perm22.45Mississippian Osage and Gillmor City 32 simulation layers Flow simulation grid dx=500ft; dy=500ft; dz: variable ( 0.5 ft to 300ft) Flow Simulation Model (cross sect.) x z Perm K (md)

13 Regridding x z Flow Model dz=30ft dz=8ft dz=0.5ft Porosity=15% Porosity=12% Porosity=18% Porosity=15% Porosity=12% Porosity=18% Set dz= smallest dz in the flow simultion model, and top fill the properties with the same value, to maintain the subtle features in the geology model Seismic Model

Free CO 2 Sat b) a) CO 2 Saturation at 250 Years Leakage Test CO2 saturation

Example: Geology Structure for Sim Layers x z 15 Mississippian unconformity and porous carbonate

Velocity Model at Year 0 16

Velocity Model at Year Average velocity difference m/s

Sco 2 Velocity Model at Year 250 and CO 2 Saturation 18 CO 2 saturation Average velocity difference m/s difference

Seismic Simulation Results ( Year 0) 19

amp Seismic Simulation Results (Year 250) 20

a)b) amp c) S co 2 Difference on Seismic ( Year 0 and Year 250) 21 CO 2 SatDifference

m/s d) a) b) c) m/s Map View of Reservoir Properties (Depth 1348 m) 22

a) b) amp c) 23 Depth Slice 1348 m Year 250Year 0Difference

Conclusions and Discussions Work flow: flow -> seismic Re-gridding from flow simulation to seismic is challenging, which includes both downscaling and upscaling The followings need to further investigated – Is any geology feature lost during this process? – If all the features is maintained, can it all be detect on seismic? ( may be below seismic resolution ) 1D convolution forward modeling-> 2D/3D acoustic/elastic 24

Acknowledgement Dr. Christopher Liner (PI) Po Geng (Flow simulation) June Zeng (Geology) UH CO 2 Sequestration Group 25

Thank You !!! 26

27

Data format (for each layer) : x y property value ( depth, pressure, porosity, saturation, etc.) Flow simulation Output x y 28

Flow Simulation Property Format X(ft)Y(ft)Z(ft) dx=500 ft dx=2000 ft Data points at the truncation or pinch-out will be skipped when exported from flow simulation model 29

X(ft)Y(ft)Z(ft) Fill the gaps : assume z is missing dx=2000 ft 30 After this step, the grid will be regular with NX by NY by NZ in each direction

Flow simulation Output 31 Assumptions: If z is zero, then fill the zeros value with the same depth of the next top layer and also corresponding property values x(ft) y(ft)z(ft)

Frequency Spectrum Freq Amp 32 (Liner, 2012) f dom : 35Hz

Flow Simulation Grid types – Unconstructed – Constructed (preferred, high efficiency of simulation performance) Orthogonal corner grid (easy, fewer faults structure) (used in this study) Un-orthogonal corner grid (more complicated, more faults structure) 33

Flow Simulation Single well with only CO 2 injection: 368 tons per day Both local grid refinement (LGR) around the injection well and uniform grid are used 34

TVD subsea (ft) Vertical CO 2 injection well, perforated in the bottom layers) Flow Simulation 35

Flow simulation Output x y dx=500ft dy=500ft dz varies: <1ft >200ft 32 layers total 36

37 Re-gridding to Seismic Grid 1) dz=0.8ft to dz =5ft 2) dx=500ft to dx=82.5ft (seismic bin size) 3) dy=500ft to dy=82.5ft (seismic bin size) a. interpolation /averaging is needed b. top/bottom fill Uncertainties: 1)Top fill may cause considerate variation in the velocity and produce sharp boundaries, which greatly affect velocity model 2) Smoothing may be necessary to avoid the shape edges