Consistent Update of Well Path, Grid Structure and Grid Model Parameters Using an Iterative Ensemble Smoother

For horizontally drilled wells offshore, uncertainty in well path position can be substantial (10 meters or more in true vertical depth). In traditional modelling workflow, this uncertainty is often ignored, despite the potential impact this has on the reservoir management decisions. Fortunately, state of the art tools for seismic depth conversion allows us to incorporate and generate multiple realizations where uncertainty in well path trajectory and structural horizons can be accounted for. However, inconsistencies are easily introduced when these tools are used as part of an integrated modelling and data conditioning workflow, which includes both static (seismic, logs, etc.) and dynamic (production, 4D seismic etc.) data conditioning. Typical inconsistencies can include:

• When a well trajectory is changed during data conditioning, how do we preserve consistency with the updated well log distributions and resulting grid properties?

• When well trajectories and structural horizons are updated simultaneously, how do we prevent that artificial well tops are introduced, so that the conditioned model parameters remain physical?

• When changing the facies property in a single grid cell, how do we preserve the consistency of the petrophysical properties at large?

Although numerous papers have been published on the topic of conditioning grid structure and facies distributions to static and dynamic data, an algorithm accounting for all the three cruxes outlined above has not been published.

In this paper, we describe a complete workflow, from well path uncertainty to flow simulation, that prevents introducing these model inconsistencies during data conditioning using an iterative ensemble smoother, looking in particular at the three cruxes outlined above. The workflow can be divided into two steps:

1) Prior modelling, where we define the limits of the sample space for each model parameter and use Monte Carlo sampling to generate an ensemble of realizations. In the initial step, static data is used to guide the local and global variability for the generated realizations, without hard data conditioning. The goal of this initial step is to establish a graphical network model defining physical connections between the model parameters in an integrated workflow, and constrain the sample space of the resulting model parameters.

2) Training: Using an iterative ensemble Kalman smoother, we condition the model parameters to observed data simultaneously using all available data (both static and dynamic).

An anonymous field on the Norwegian continental shelf will be used to demonstrate the practical use of this workflow.