Impact of Model Parameterisation and Objective Choices on Assisted History Matching and Reservoir Forecasting

Assisted history matching (AHM) can be approached either by single objective (SO) or multi-objective (MO) optimisation. AHM generates multiple matched-models, which are then used to quantify uncertainty of the forecast. The goals in this framework are not only to produce matched-models in an efficient way but also to provide a reliable forecast under uncertainty. Choice of the model paramaterisation is one of the sources of uncertainty in reservoir modelling. This is usually done based on geological information and engineering knowledge.

This paper explores the impact of different geological model parameterisations on AHM and its impact on the reservoir forecasting. It compares the performance of SO and MO AHM approaches under different model parameterisation. In the MO approach, different choices of objective grouping were studied. Three performance measures in AHM and forecasting will be discussed, namely: diversity, convergence, and forecast reliability. Diversity is defined as set of models that are different but still match history. Convergence is defined as how fast history matching achieves the desirable match. Reliability, for synthetic case study in the present paper, is defined as encapsulation of a simulated truth case into the probabilistic range of forecasting (P10 – P50 – P90).

Study on a standard industry benchmark case in this paper shows that MO optimisation approach provides a more diverse set of matched-models, which leads to a better forecast. It also confirms that a MO approach is more robust and reliable in forecasting than SO under different model parameterisation. Different objective grouping choices in MO approach still give a reliable reservoir forecasting, although variability in convergence rate is seen.