Risk Assessment and Simulation of Injectivity Decline Under Uncertainty

Chiotoroiu, Maria-Magdalena (OMV E&P) | Clemens, Torsten (OMV E&P) | Zechner, Markus (OMV E&P/Stanford University) | Hwang, Jongsoo (University of Texas) | Sharma, Mukul M. (University of Texas) | Thiele, Marco (Streamsim/Stanford University)

OnePetro 

Abstract

Waterflooding can lead to substantial incremental oil production. Implementation of water injection projects requires the project to fit into the risk (defined here as negative outcomes relative to defined project objectives) and uncertainty (defined here as inability to estimate a value precisely) a company is willing to take.

One of the key risks for water injection into a shallow reservoir is injection induced fractures extending into the caprock. If this risk is seen as "Intolerable" in an As Low As Reasonable Practicable (ALARP) analysis a decision may be made to not proceed with the project., In this study we evaluated caprock integrity by conducting simulations of long-term water injection that include the effects of formation damage caused by internal/external plugging, geomechanical stress changes and fracture propagation in the sand and bounding shale.

The risk of fracture growth into the caprock was assessed by conducting Monte-Carlo simulations considering a set of modelling parameters each associated with an uncertainty range. This allowed us to identify the range of operating parameters where the risk of fracture height growth was acceptable. Our simulations also allowed us to identify important factors that impact caprock integrity. To cover the uncertainty in geomechanical reservoir evaluation, the operating envelope is identified such that the risk of the caprock integrity is reduced. This requires introducing a limit for the Bottom Hole Pressure (BHP) including a safety margin.

The limit of the BHP is then used as a constraint in the uncertainty analysis of water injectivity. The uncertainty analysis should cover the various development options, the parametrisation of the model, sampling from the distribution of parameters and distance-based Generalized Sensitivity Analysis (dGSA) as well as probabilistic representation of the results.

The dGSA can be used to determine which parameter has a strong impact on the BHP and hence the project and should be measured if warranted by a Value of Information analysis.

The final development option to be chosen depends on a traditional NPV analysis.