Enhanced History Matching and Prediction Using Integrated Analytical and Numerical Modeling Approach

Ghadami, Nader (PETRONAS) | Benayad, Nourreddine (PETRONAS) | Motaei, Eghbal (PETRONAS) | Raub, M Razib (PETRONAS)

OnePetro 

Abstract

Integrated reservoir modeling with representative data is crucial for an effective reservoir management and depletion plan. Both analytical and numerical approaches benefit from this integrated process. The objective of this study is to incorporate the outcomes of analytical techniques such as rate transient analysis (RTA) and pressure transient analysis (PTA) into numerical reservoir model to have a better understanding of drive mechanisms, reservoir connectivity with minimal time-consuming for history matching efforts but a more reliable production forecast.

In order to demonstrate the methodology, a clastic reservoir from Malay basin was considered. Sedimentology and sequence stratigraphy studies were performed to have a better picture of heterogeneity and zonation of the reservoir. All production and injection data were investigated along with pressure data to filter data inconsistency. Shut-in time should be long enough to take representative reservoir pressure and accordingly material balance study conducted for accessible volume for a given area. However, flowing material balance is able to be applied with no restriction on the production data for evaluation of historical data and prediction cases.

The boundary of the channel sand was constructed based on the well log data and seismic attributes. Amplitude impedance was used as a guide for lithofacies and porosity distribution in the geological model. In addition, stratigraphy definition with further details were incorporated. Lithofacies, petrophysical and SCAL data were incorporated in rock-type classification and accordingly saturation-height-function were modelled. Analytical approaches including PTA, material balance, and RTA were utilized to have a better understanding of fluid flow and drive mechanisms. The well and reservoir properties and also connected volume from analytical approaches were utilized as a tuning tool of static model. This approach considerably reduced the iteration between static and dynamic models for history matching exercise. Afterwards, the production forecast were conducted with two development opportunities identified.

In this study, an integrated methodology was applied to mitigate the complexity of history matching task. Moreover, it is demonstrated that using such analytical methods help to improve the development plan of a given field significantly.