pressure transient analysis in co storage projects: from reservoir
TRANSCRIPT
Pressure Transient Analysis in CO2 Storage Projects: from Reservoir Characterization
to Injection Performance Forecast
Anton Shchipanov*, Lars Kollbotn, Roman Berenblyum
International Research Institute of Stavanger (IRIS)*Corresponding author: [email protected]
Outline
› Introduction
› CO2 injection at Tubåen formation of Snøhvit field
› Analysis of CO2 injection history
› Interpretation of time-lapse injection and fall-off pressure transients
› Simulation of history with analytical models
› Uncertainty resulted from interpretation of different transients
› Lessons learned from analysis and simulation of CO2 injection at Tubåen formation
› Testing wells to forecast injection performance and pressure build-up
› Interpretation of injection and fall-off responses
› Duration of a test and optimal test design
› Acknowledgements
CO2 injection at Tubåen formation of Snøhvit field
Difference amplitude map between baseline and monitor at base of the reservoir [from Hansen et al., 2011]
Depth map of top Fuglen formation (left) and geological cross-section N-S through the reservoir sections at Snøhvit (right)[from Hansen et al., 2013]
Analysis of CO2 injection history
History of CO2 injection at Tubåen formation
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Time [hr]
Salt precipitation followed by treatment
History period to be analyzed and simulated
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Gauge
Model (Fall-offs)
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Interpretation of time-lapse fall-off pressure transients
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Elapsed Time [hr]
Pressure DerivativeFall-off 1 Fall-off 1Fall-off 2 Fall-off 2Fall-off 3 Fall-off 3Fall-off 4 Fall-off 4
Referencefall-off
› Different fall-off responses / derivatives
› Follow the same trend
› May be matched with the same model
Analysis of injection pressure transients:Smoothing derivatives
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Elapsed Time [hr]
Pressure DerivativeInjection 3 ReferenceInjection 2 No SmoothInjection 2 Smooth 0.2Injection 2 Smooth 1
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Elapsed Time [hr]
Pressure DerivativeInjection 2 ReferenceInjection 3 No SmoothInjection 3 Smooth 0.2Injection 3 Smooth 1
› Injection responses
› Often more noisy measurements than during shut-ins
› But if interpreted, well shut-ins are not required
› Smoothing derivative of injection response, criteria
› Convergence to a stable trend
› Repeatable trends from different responses
› Time-lapse flowing responses
› Flowing vs shut-in
› Smoothing derivative of injection response, criteria
› Convergence to a stable trend
› Repeatable trends from different responses
› Time-lapse flowing responses
› Flowing vs shut-in
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Elapsed Time [hr]
Pressure DerivativeInjection 3 ReferenceInjection 2 No SmoothInjection 2 Smooth 0.2Injection 2 Smooth 1
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Elapsed Time [hr]
Pressure DerivativeInjection 2 ReferenceInjection 3 No SmoothInjection 3 Smooth 0.2Injection 3 Smooth 1
› Injection responses
› Often more noisy measurements than during shut-ins
› But if interpreted, well shut-ins are not required
Analysis of injection pressure transients:Smoothing derivatives
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Model (Injection 1)
Model (Injection 3)
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Elapsed Time [hr]
Pressure DerivativeFall-off 1 Fall-off 1Injection 1 Injection 1Injection 2 Injection 2Injection 3 Injection 3
Interpretation of time-lapse injection pressure transients
Referenceinjection
› Different injection responses / derivatives
› Follow similar trends
› May be matched with the same model with some adjustment of parameters
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GaugeModel (Fall-offs)Model (Injection 1)Model (Injection 3)
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Accuracy of history simulation with models assembled via interpretation of different transients
History simulated based on interpretation of different periods: fall-offs and injections
FO 1 FO 2 FO 3 FO 4
INJ 1 INJ 2 INJ 3
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GaugeModel (Fall-offs)Model (Injection 1)Model (Injection 3)
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or Forecast uncertaintygiven by interpretation of different transients
This spread may be considered as forecast uncertainty when taking different periods as the reference
FO 1 FO 2 FO 3 FO 4
INJ 1 INJ 2 INJ 3
› Analytical PTA models are an efficient tool in CO2 injection projects even after an initial CO2 plume has been formed near the well
› Impact of the plume (different fluid properties) may be approximately accounted for via well skin factor
› Well flowing periods may be interpreted in a line with shut-ins, even though smoothing may be required
› Actual flowing response may be qualified through comparison of time-lapse responses and control of the smoothing to get repeatable trends
› PTA models may be used to forecast injection performance and long-term pressure build-up in CO2 injection projects (both storage and EOR)
Lessons learned from analysis and simulation of CO2 injection at Tubåen formation
Testing wells to forecast injection performance and pressure build-up
A synthetic model of injection site (a fault block)
An aquifer block (U-shape) confined by three faults (no-flow boundaries): West, North and South from the injector
Pressure[bar]
N
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Well test designs: from best to more feasible
› ‘100’ (day): Long injection
› 4.3x10-3 PV (of the model) injected, may be followed by fall-off (100 day)
› Complex evaluation of sealing faults: pressure build-up (linear scale), injection and fall-off derivatives (log-log)
› ’10’ (day): Reduced injection
› 4.3x10-4 PV injected with long fall-off (100 day)
› Sealing faults may be captured from both injection and fall-off derivatives
› ‘1’ (day): Sort-term injection
› 4.3x10-5 PV injected with long fall-off (100 day)
› May be more feasible, since small volume injected short-term
› Long fall-off still provides diagnostics of sealing boundaries
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Pressure
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1, 2, 4, 8…
Design ‘1’: Radius of investigationand flow regimes
1st day:end of
injection
2nd day
4th day
8th day: and similar
picture afterwards
Long pressure fall-off (100 day)
Short-terminjection (1 day)
Fall-off response: Pressure and derivative
Pressure evolution in U-shape reservoir during well test. Pressure exceeding 302 bar is colored in red to highlight pressure
evolution deeper into reservoir
Interpretation of injection and fall-off responses
Pressure
Derivative
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Elapsed Time [hr]
1 (injection) 1 (fall-off)10 (injection) 10 (fall-off)100 (injection) 100 (fall-off)Boundary 1, 2, 4, 8 day
Injection
›Boundary dominated flow regime started after ~40 hr
›Reliable boundary indication after additional ~40 hr
Fall-off
›Beginning of boundary dominated regime depends on injection duration prior to fall-off
›100 day: after ~80 hr
›10: ~120 hr
›1: ~170 hr
›Reliable boundary indication after additional ~40 hr
› Injection is best candidate for diagnostics of flow barriers (minimum test duration)
› If injected volume is limited, fall-off may be considered as alternative
Derivative
Pressure
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Reference
Total Compressibility (x2)
Closer Barrier (West 1/2)
Sensitivity helps to test hypotheses and qualify uncertainty
› PTA deals with ill-posed problems meaning different models / parameters may provide match of the same pressure response
› Sensitivity improves understanding of major parameters and uncertainty with interpretation
As an example, sensitivity to
›Distance to the West flow barrier
›Total compressibility
› A well test taken before the main phase of injection may consist of a short-term water production or injection with following well shut-in
› CO2 may be used as the injection fluid in such a test, but this would lead to larger uncertainty in the test interpretation
› Pressure dynamics during the main phase of injection may also be interpreted to assemble PTA models for long-term pressure forecasting
› Installation of permanent downhole gauge as near as possible to the sand face of a reservoir in combination with frequent rate measurements improves forecast capabilities of PTA models
Testing wells to forecast injection performance and pressure build-up
Acknowledgements
The authors acknowledge Statoil Petroleum AS and the Snøhvit licensepartners including Petoro AS, Total E&P Norge AS, GDF SUEZ E&P Norge ASand RWE Dea Norge AS for the permission to publish this paper
Contribution from our partners in Statoil, especially Bamshad Nazarian andOlav R. Hansen is gratefully acknowledged
The interpretations and conclusions presented in this paper are those of theauthors and not necessarily those of Statoil, nor the Snøhvit license partnersincluding Petoro, Total, GDF SUEZ and RWE Dea
This work is co-financed by the REPP-CO2 project, which is supported by agrant from Norway
Thank you for your attention!