Reservoir rock properties characterization using well test data is crucial for the success of field exploration and development phases. Unfortunately, the conventional well test interpretation approach is based on analytical models (Horner plot, Gringarten type curves, and Bourdet derivative) which does not consider the reservoir complexity and the actual geological information used in reservoir simulation models. This may lead to non-geological interpretation and inaccurate reservoir description. Various testing methods are generally used in the oil and gas industry to evaluate the reservoir performance and acquire useful information to reduce uncertainties in reservoir characterization. The two common methods are interval pressure transient testing (IPTT) and drill stem test (DST). Integration of the DST/IPTT interpretation results into the reservoir simulation model is required to better characterize the reservoir properties and help minimize the uncertainties in rock property distribution around the wellbore. Unfortunately, this integration is not done automatically and systematically.

A grid-based inversion techniques of the pressure transient test is introduced to allow seamless integration of well test data into a reservoir model. This technique uses the same grid for the dynamic simulation model to discretize the reservoir properties. It is based on data assimilation, represented by Ensemble Kalman Filter (EnKF) and maximum likelihood techniques such as gradient-based methods. The main challenge of this technique is how to deal with large unknowns in reservoir parameters. This requires the use of Bayesian framework technique (similar to seismic inversion) to overcome the challenge of large unknown parameters.

A workflow that allows a seamless integration of well test interpretation results into the reservoir simulation model is used considering the prior geological knowledge of the reservoir that provides an efficient way to integrate IPTT/DST results and geological information. The grid-based inversion technique has been successfully tested and used to better estimate the reservoir parameters using well test data. The integration of the IPTT results into the reservoir model helped to reduce uncertainties in near wellbore permeability distribution, which ultimately allowed to achieve better history match of the pressure data.

The new integration workflow reconciles static and dynamic reservoir data to update the geological reservoir model with meaningful parameters. It also allows seamless integration (which was not possible before) of well test data into reservoir simulation model which is key for successful reservoir management.

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