The study objective is to investigate the use of Artificial Intelligence (AI) methods to accelerate the history matching process.

A new criterion for measuring the deviation of the simulation model from measured and/or observed parameters has been introduced. Instead of comparing parameter deviations in wells to input changes on regional basis, it is proposed to calculate a regional RMS (Root Mean Square)-error, so that the impact of input changes can be directly evaluated. Instead of grouping grid blocks based on geology, it is proposed here, to generate regions of similar trends based on all available information. Artificial intelligence (AI) is used via Self Organizing Maps (SOM) to cluster grid blocks of similar behavior. SOMs can process any kind of information; in this case these types of parameters have been particularly used:

  • geological description: lithofacies type

  • hydraulic flow units (HFU): permeabilities, porosities

  • initialization: water saturations (initial and critical), initial pressure

  • discretization: spatial discretization (e.g. DZ), grid block pore volumes

  • secondary phase movement: relative permeability endpoints

A three fold approach for improving and/or assisting the history matching (AHM) work-flow using Artificial Intelligence has been tested:

  1. Use production plots, Neural Networks and "Material Balance with Interference (MBI) method for quality control and consistency check of time dependent and static data.

  2. Use the multi-dimensional cross-plot and SOM to evaluate reservoir and well performance.

  3. Use SOMs and the region RMS error to evaluate the performance of history matching runs.

This new approach is simple and leads to a clear improvement of the match quality and significantly reduces the number of runs needed to achieve the match. Different field models have been used to develop this new AHM workflow. Finally in this paper, two of them are selected to demonstrate the improvement of model pressure and watercut matches using this new method.

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