Speaker
Description
Carrying out uncertainty quantification and robust optimisation workflows for naturally fractured reservoirs (NFR) is very challenging because exploring and capturing the full range of geological and mechanical uncertainties requires a large number of numerical simulations and hence computationally intensive. Specifically, the integration of poro-mechanical effects in full-field reservoir simulation studies is still limited, mainly because of high computational cost. As a result, poro-mechanical effects are often ignored in uncertainty quantification and optimisation workflows, which may result in inadequate reservoir performance forecasts. Computationally efficient poro-mechanical screening methods are therefore important to identify if poro-mechanics could impact reservoir dynamics and identify individual models from a model ensemble for more detailed full-physics reservoir simulations.
Here we introduce a new methodology that extends traditional uncertainty quantification workflows, through the use of poro-mechanical informed flow diagnostics and proxy models. This approach provides first-order approximations of the complex interactions between poro-mechanics and hydrodynamics using existing steady-state dual-porosity flow diagnostics and coupled dual-continuum poro-mechanics. The calculations are computationally efficient and allow us to quickly quantify their impact of poro-mechanics on reservoir dynamics and further enable us to select representative reservoir models that capture the uncertainty quantified in a reservoir model ensemble. These representative models can then be used in further, more detailed and computationally intensive full-physics coupled reservoir simulations. The proposed poro-mechanical screening hence provides an efficient complement to traditional reservoir simulation and uncertainty quantification workflows and enable us to assess a broader range of geological, petrophysical and mechanical uncertainties.
Using a series of case studies based on a fractured carbonate reservoir analogue, we demonstrate how (1) uncertainty quantification workflows can be improved by considereing different hydrodynamical-poro-mechanical scenarios, (2) how bias in the uncertainty estimation can be reduced by carrying out by thousands Monte Carlo realisations using ANN-based proxy models, and (3) how cluster analysis can be performed to identify a suitable set of representative models from a much larger model ensemle without reducing uncertainty in reservoir performance preiductions. The proposed framework has been implemented using the open-source MATLAB Reservoir Simulation Toolbox MRST and was linked to a commercial reservoir simulation package to carry out the experimental design, construct the proxy model, and perform the sensitivity and uncertainty analysis.
| Participation | Unsure |
|---|---|
| Country | United Kingdom |
| MDPI Energies Student Poster Award | Yes, I would like to submit this presentation into the student poster award. |
| Time Block Preference | Time Block B (14:00-17:00 CET) |
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