Reservoir simulation
The DOE-backed EGS-Twin project aims to simulate geothermal production systems, helping operators better predict performance and maximize output.
Modeling solubility trapping at reservoir scale remains a key challenge for carbon storage projects. This study introduces a novel sub-grid convective mixing model, implemented in the Open Porous Media Flow simulator and applied to the SPE11 benchmark, that enables accurate prediction of carbon dioxide dissolution trapping in geological storage using computationally e…
This work describes a study in which distributed data parallel training, paired with a node-local caching pipeline, enabled efficient multigraphics-processing-unit scaling for a CO₂-storage graph-neural-network surrogate while maintaining generalization.
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Because the uncertainty analysis is complex and time consuming, in this paper, a stochastic representation of the computer model, called an emulator, was constructed to quantify the reduction in the parameter input space. -
With the easy conventional oil in Argentina having been produced, one remaining way to find new oil in existing fields is to convert fields from primary or secondary production to secondary or tertiary production, respectively.
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Reservoir-simulation-model inputs are numerous, and uncertainty is pervasive—before, during, and after development. With the pressure to deliver results quickly, how do we find the right balance?
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In upstream oil and gas, cloud computing is very immature because the industry has always been challenged by storage and computational capability. However, high-performance cloud computing may create an opportunity for smaller companies lacking infrastructure for scientific applications.
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With the recent drop in oil prices, operators are shifting to optimization of existing assets with minimal costs. For mature floods (water, chemical, and CO2), one low-cost optimization strategy is the intelligent adjustment of well-rate targets.
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Permanent downhole gauges (PDGs) can provide a continuous record of flow rate and pressure, which provides extensive information about the reservoir. In this work, a machine-learning framework based on PDG data was extended to two applications: multiwell testing and flow-rate reconstruction.
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In this paper, the authors introduce a novel semianalytic approach to compute the sensitivity of the bottomhole pressure (BHP) data with respect to gridblock properties.
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This paper critically investigates the impact of using realistic, inaccurate simulation models. In particular, it demonstrates the risk of underestimating uncertainty when conditioning real-life models to large numbers of field data.
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History matching is only one part of something more comprehensive—reservoir modeling.
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In this paper, the authors derive and implement an interwell numerical simulation model (INSIM) that can be used as a calculation tool to approximate the performance of a reservoir under waterflooding.
