Agave: Highlighting ECHELON Software's Robust Compositional Solver

For ECHELON software, probabilistic forecasts on multi-million cell compositional models is routine.

Bg case study agave highlighting echelon softwares robust compositional solver

Nov 30 2021

Within a weekend, ECHELON simulated 150 realizations of Eni's multi-million cell compositional model Agave.

By the numbers
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Stone Ridge Technology – ECHELON Advantages

Challenge

Agave is a light-oil, deep-water field with early production history. Evaluating the risks associated with the proposed development strategy called for the generation of a probabilistic forecast (or risk analysis) starting from an ensemble of 150 history-matched models. The models feature 2.4M active cells and two equations of state regions with 11 hydrocarbon components each. They are run without relaxing the convergence criteria.

The development plan is based on produced water and gas reinjection in the form of WAG (water-alternating-gas) injection cycles, taking into account production constraints such as maximum liquid production and water treatment capacity.

The challenge is to simulate Agave in a sufficiently short time-frame for the ensemble of 150 forecast runs to complete over one week-end with a maximum of 50 nodes of Eni's HPC5 supercomputer.

Results – Stone Ridge Technology
Initial water saturation in a sector of the Agave model, for illustration. The results presented in this case have been obtained with the full-field model.
Solution – Stone Ridge Technology
Figure 1. Simulated field GOR is identical for all the GPU configurations considered, highlighting the robustness of ECHELON's compositional solver

Solution

ECHELON 2.0 has fully implicit (FIM) and adaptive implicit (AIM) formulations and supports most reservoir and field management options available in legacy reservoir simulators. A scalability test shows that a good compromise between raw simulation speed and optimization of resources is to run each model in parallel on four GPUs, i.e., one node. Comparing the simulated field GOR between all the considered GPU configurations shows that results are unaffected, highlighting the robustness of ECHELON’s compositional solver (See Figure 1).

Results

ECHELON was able to run a full Monte Carlo risk analysis on a complex compositional model over the week-end. Figure 2 shows the P10, P50 and P90 regions evaluated for the full ensemble.

Results – Stone Ridge Technology
Figure 2

We thank Eni S.p.A for the permission to publish the data contained in this case study.

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