Analyzing pressure responses to Earth tides for monitoring CO 2 migration Kozo Sato Geosystem Engineering The University of Tokyo

Objective Monitoring techs for geological sequestration  seismic (4D, VSP, cross-well tomography)  non-seismic (electromagnetic, gravity, tilting, logging) Alternative technique?  cost-effective  labor-saving Utilize pressure responses to Earth tides  perturbation by the M and the S (no artificial energy required)  pressure measurements only (no extra operation required)

Outline Objective Tidal deformations  Earth tide  Cubic dilatation  Calculation of Cubic dilatation Poroelasticity Tidal signals in pressure responses Results and discussion Concluding remarks

Tidal deformations Earth tide  Tidal deformation (cyclic compaction and expansion) of the solid Earth  phenomenon similar to ocean tides  the gravitational attraction of the solar system bodies: M and S

Tidal deformations Cubic dilatation  cubic dilatation (trace of strain matrix)  normal stresses and strains  near the Earth surface free surface boundary condition

Tidal deformations Calculation of cubic dilatation   as a linear combination of Y and its derivatives w.r.t.   Y: spherical harmonics defining tidal potential  sample calculation of  (an onshore site, Nagaoka, Japan) (latitude: 37.40, longitude: )

Outline Objective Tidal deformations Poroelasticity  Deformations and pressure fluctuation   and CO 2 migration Tidal signals in pressure responses Results and discussion Concluding remarks

Poroelasticity Deformations and pressure fluctuation  tidal deformation induces pressure fluctuation  p  Biot-Gassmann equation  poroelastic parameter 

Poroelasticity  and CO 2 migration  K f for the H 2 O-CO 2 system   as a function of S CO2

Poroelasticity  and CO 2 migration  K f for the H 2 O-CO 2 system   as a function of S CO2  K CO2 =0.003~0.07GPa, K w   increases as S CO2 increases:  =AS CO2 +B   =  /  p : a good indicator for monitoring the CO 2 migration

Outline Objective Tidal deformations Poroelasticity Tidal signals in pressure responses  Pressure responses  Retrieving  p(t) from p(t) Results and discussion Concluding remarks

Tidal signals in pressure responses Pressure responses  long-term pressure trend p t (t) associated with a certain event, s.a. CO 2 sequestration

Tidal signals in pressure responses Pressure responses  long-term pressure trend p t (t) associated with a certain event, s.a. CO 2 sequestration  total pressure response p(t) : superposition of p t (t) and  p(t)   p(t): tidal signal induced by the Earth tide

Tidal signals in pressure responses Retrieving  p(t) from p(t)  model the long-term pressure trend with the cubic spline  retrieve the tidal signals p(t)p(t)pt(t)pt(t)

Tidal signals in pressure responses Retrieving  p(t) from p(t)  model the long-term pressure trend with the cubic spline  retrieve the tidal signals p(t)p(t)pt(t)pt(t) p(t)p(t)

Outline Objective Tidal deformations Poroelasticity Tidal signals in pressure responses Results and discussion  Monitoring at a sequestration test field  Estimation of   Detection of CO 2 arrival Concluding remarks

Results and discussion Monitoring at a sequestration test field  onshore aquifer, Nagaoka, Japan  sandston bed, thickness: 60m, depth: 1100m  injection well: CO2-1, Zone-2a (6m) and Zone-2b (6m)  monitoring wells: CO2-2, CO2-3, CO2-4 CO2-4 CO2-2 CO2-3 CO2-1 60m 120m 40m logging pressure measurements logging

Results and discussion Monitoring at a sequestration test field  pressure measurement  time-lapse sonic logging (compressional wave velocity)

Results and discussion Monitoring at a sequestration test field  is it possible to detect CO 2 arrival only with pressure data?   =AS CO2 +B

Results and discussion Estimation of  ( days)  calculation of 

Results and discussion Estimation of  ( days)   p retrieved from the pressure data

Results and discussion Estimation of  ( days)   =  /  p   scaled to match the  p profile

Results and discussion Estimation of  ( days)   =  /  p   scaled to match the  p profile

Results and discussion Estimation of  ( days)  calculation of 

Results and discussion Estimation of  ( days)   p retrieved from the pressure data

Results and discussion Estimation of  ( days)   =  /  p   scaled to match the  p profile

Results and discussion Estimation of  ( days)   =  /  p   scaled to match the  p profile

Results and discussion Detection of CO 2 arrival

Results and discussion Detection of CO 2 arrival  time-lapse  estimation (13 intervals)

Results and discussion Detection of CO 2 arrival  time-lapse  estimation (13 intervals)   =AS CO2 +B

Results and discussion Detection of CO 2 arrival  time-lapse  estimation (13 intervals)   =AS CO2 +B

Results and discussion Detection of CO 2 arrival  time-lapse  estimation (13 intervals)   =AS CO2 +B

Outline Objective Tidal deformations Poroelasticity Tidal signals in pressure responses Results and discussion Concluding remarks

The poroelastic parameter , a function of S CO2, can be estimated from  p and . The CO 2 migration can be monitored with time- lapse estimations of . The technique is applicable to well-developed sites (depleted o/g reservoirs).