Maracuya: ECHELON Showcases Its Multi-Reservoir Simulation Capacity

Seamless coupling of multiple reservoir models with shared production targets and a gathering network.

Bg case study maracuya

Jan 13 2022

Eni uses the ECHELON field scheduler and facility network solver to balance groups producing from multiple reservoirs.

By the numbers
0
Reservoirs
0
Wells
0
Production Network
Stone Ridge Technology – ECHELON Advantages

Challenge

Maracuya is a greenfield consisting of three dry gas reservoirs characterized by good petrophysical properties, with homogeneous and well connected sands. The three models have approximately 1M cells each and use a simple two-component (water-gas) black-oil model. The wells use nonlinear inflow models and are subject to maximum drawdown limits. Field production is managed by assigning a different plateau target rate to three groups combining wells perforated in different reservoirs, i.e., a multi-reservoir simulation capacity is needed. Furthermore, all wells are produced through the same gathering network, and the impact of network backpressure on the production needs to be accounted for.

Solution

ECHELON supports multi-reservoir coupling by delegating well allocation tasks to an independent field scheduler able to balance groups producing from multiple reservoirs. ECHELON comes with its own integrated facility network solver (FNS) for seamless inclusion in reservoir engineering workflows. FNS can model complex production and injection network topologies, including bifurcations, where pressure losses in pipes are interpolated from precomputed hydraulic tables.

Solution – Stone Ridge Technology
Figure 1. Standalone ECHELON simulation models can easily be coupled through the field scheduler to balance groups producing from multiple reservoirs. No change to the static and well properties parts of the .DATA files is required. The field scheduler seamlessly connects to the integrated facility network solver, to self-consistently model the impact of network backpressure on the wells.

Results

These figures show ECHELON is able to honor the gas production target and respective plateaus of each group, while gas production for each reservoir adapts to their differential depletion. Coupling to the facility network solver (FNS) results in a shorter plateau, due to an underestimation of network backpressure in the model without network. This is more easily seen in the figure (bottom) where the tubing-head pressure and the gas production rate of a representative well are shown; the well stops producing earlier when the network is accounted for.

Results – Stone Ridge Technology
Figure 2. (Top) Gas production rate per group. (Middle) Gas production rate per reservoir. Dashed-lines have been obtained with multi-reservoir-coupling of the field balancing logic, while solid lines include, in addition, coupling to FNS. (Bottom) Gas production rate and tubing head pressure for a representative well. Dashed-lines have been obtained with multi-reservoir-coupling of the field balancing logic, while solid lines include, in addition, coupling to FNS.

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

Download Case Study

Additional case studies

Deep dives into our software product and its efficacy.

ECHELON: A Cloud Computing Use Case – Stone Ridge Technology

ECHELON: A Cloud Computing Use Case

Evaluating the productivity enhancements of Fishbones in fractured carbonate reservoirs using high-performance cloud-based reservoir simulation.

View case study →
Unconventional Field Development – Stone Ridge Technology

Unconventional Field Development

Workflow to manage the operational challenges in an unconventional field development requires full field simulation.

View case study →
Kabosu: ECHELON Software Tackles Complex Field with Flying Colors – Stone Ridge Technology

Kabosu: ECHELON Software Tackles Complex Field with Flying Colors

ECHELON software tackles an extremely difficult compositional model. A demonstration of the robustness of ECHELON software.

View case study →