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Risk Management in CO2 Storage Projects

  1. Integrate risk management into project design and implementation

  2. Identify site-specific project risks

  3. Characterize and rank the impact and probability of project risks

  4. Develop Risk Management Plans (RMP's)

  5. Implement the RMP

  6. Complete periodic updates to the risk analysis

1. Response Surface Models (RSM) for Quantitative Risk Assessment

RSM can substantially reduce the number of models to replace the fully coupled geological model. It has been successfully used in oil & gas projects as well as CO2 research for risk management; especially for the top two subsurface uncertainties in CO2 storage that may lead to seriuous risks:

  1. CO2 injected plume migrates out of Area of Review (AoR)

  2. Pore Pressure increasing during CO2 injection causing seal failure


Here we will illustrate use of RSM to quantitatively predict CO2 plume movement.


RSM is a 5-step process (see figure)


1- A fully coupled geo-cellular model is constructed first.


2- A parameter sensitivity analysis is done using the model to rank  top uncertainties/predictors for the plume movement (see Tornado chart below).  Here we assume only top three predictors are significant (permeability, porosity and Kv/kh).


3- An RSM or Design of Experiments  (DoE) software is used to generate response data from the model for the calibration of Model Response Equation (MRE) in next step by generating number of experiments necessary (15 in this case) using top 3 predictors.


4- Regression analysis is then run in DoE to generate a MRE that is calibrated with data (previous step). A high R2 indicates a a high degree of confidence in predicting model response; in this case the CO2 plume migration distance.


5- Finally Monte Carlo simulation is run on calibrated MRE by providing probability distribution (input) to each of three predictors to generate Probability Density Function (output) or PDF for predicting expected plume distance (Low-Mid-High).


This quantitative probability assessment of plume distance is fed to experts in next step for Risk Assessment Matrix (RAM), providing a high degree of confidence.

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  1. Qualitative Risk Assessment develops non-numeric estimates of the probability of occurrence and magnitude of impact of different risks to provide a subjective evaluation and basis for risk management.

  2. Quantitative Risk Assessment develops numeric estimates of the probability of occurrence and magnitude of impact of different risks to provide an objective evaluation and basis for risk management.

  3. Semi-quantitative Risk Assessment combines the two approaches, often using expert opinion and evidence- based numeric data, to develop a reasonably objective evaluation and basis for risk management.

The Risk Assessment Matrix (RAM)

RAM is often used tool in oil & gas industry and well adapted in CO2 storage business for risk analysis (See fig. below). Team of SME’s work following steps:

  • Identify the consequence of ‘Hazard’ if released (done in next section). 

  • Estimate the “Severity “(1 -5) of consequence for each hazard 

  • Estimate ‘Likelihood’ ( 1- 5) of each consequence 

  • Rank each hazard according to impact 

  • Impact Score (Hazard) = Severity x Likelihood

    • Score = 20 - 25 (RED) : Non-Operable

    • Score = 10 - 16 (ORANGE) : Intolerable

    • Score = 5 - 9 (YELLOW) : Undesirable

    • Score = 1 - 4 (GREEN) : Acceptable

Risk Mitigation & Consequence Management

The Bow-Tie method can be used to analyze all risk sources, communicate risk scenarios and risk mitigation strategies. Broadly it has four element:

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