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Advances On Sparse Representation of Reservoir Properties, Fluid Characterization Methods for Steam-Solvent Co-Injection, and Full-Resolution Simulations of Multi-Scale Processes

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Course Credit: 0.15 CEU, 1.5 PDH

Presented by Behnam Jafarpour, Ryosuke Okuno, Rami M. Younis.
Topic One
High-resolution reservoir imaging is critical for predicting reservoir response to alternative development strategies and thereby optimizing reservoir production performance. Inference of high-resolution heterogeneous rock properties from low-resolution production measurements leads to a challenging nonlinear inverse problem with many non-unique solutions. The goal of this research is to combine recent advances in signal processing, applied mathematics and computational inverse theory with insights gained from geosciences and multiphase flow in porous media to formulate effective, robust, and geologically consistent frameworks for solving subsurface characterization and imaging inverse problems.

In this presentation, I first motivate low-rank (sparse) representations for describing reservoir properties and review some of the conventional reduced-order parameterization techniques in reservoir engineering. I then introduce a flexible and robust sparse representation approach for reservoir property description that is inspired by recent developments in sparse signal processing. I present some of our recent research work in sparse history matching and discuss the “sparse geologic dictionaries” as a novel conceptual framework for reservoir model selection and updating under geologic uncertainty.

Results from several numerical history matching examples illustrate that geologically-inspired sparse representations of rock properties offer important advantages over conventional reduced-order parameterization techniques. We are currently working on extending our formulation to a stochastic formulation for uncertainty quantification. Our future research in this direction will be focused on developing computationally efficient sparse learning algorithms for estimating facies distribution in large-scale realistic history matching problems.

Topic Two

Design of steam-solvent coinjection requires accurate representation of multiphase behavior that results from interaction of fluid and energy flow in porous media. Although PVT measurements provide important data at selected pressure-temperature-composition conditions, it is difficult to measure actual phase behavior encountered during in-situ processes. Thus, reliable fluid characterization is as important as reliable experimental data for coinjection simulation.

The research project proposed for this SPE award is to develop a fluid characterization method for steam-solvent coinjection. It aims to accurately model multiphase behavior of heavy-oil/solvent/water mixtures using the Peng-Robinson equation of state (PR EOS). At most four equilibrium phases are considered; the oleic (L1), gaseous (V), solvent-rich liquid (L2), and aqueous (W) phases.

A new fluid characterization method is developed based on the concept of perturbation from n-alkanes (PnA). The focus in the first year is on L1-V two phases. The PnA method initially gives pseudo components critical parameters that are optimized for n-alkanes in terms of liquid densities and vapor pressures predictions using the PR EOS. Critical pressures, critical temperatures, and acentric factors for pseudo components are then perturbed in well-defined directions to match PVT data available. The perturbation considers the size of composition space (i.e., the carbon-number range) and the distribution of components in that space.

Consequently, a lighter oil tends to have a greater sensitivity of the energy density to molecular weight in the PnA method. The energy density is defined as ai/bi, where ai and bi are the attraction and covolume parameters for pseudo component i, respectively. This energy density modeling is the key to development of a universal characterization method that is applicable to a wide range of reservoir fluids.

The extensive testing of the PnA method uses 77 different reservoir fluids, including near-critical fluids, gas condensate, volatile oil, and heavy oil. Challenging cases include predictions of liquid dropout curves for near-critical gas condensates, where accurate prediction of both volumetric and compositional properties is crucial. Simulation case studies are presented for gas injection processes. Results show that the PnA method requires much fewer pseudo components to correctly model phase behavior during oil displacements, compared to the conventional characterization methods. The PnA method uses only three adjustment parameters in its regression process, regardless of the number of components used. With the PnA method, the PR EOS does not need to correct volumetric predictions using volume shift parameters for the fluids tested.

In the next year, the PnA method will be extended to L1-L2-V three hydrocarbon phases and L1-L2-V-W four phases. To this end, we have examined prior methods for modeling water-hydrocarbon mixtures, and collected experimental data for such mixtures. Effects of fluid characterization will be also investigated on flash and stability calculations in solvent injection simulation.

Topic Three

By revisiting classic analytical methods that were known to Newton, this talk will answer a quintessential question of modern simulation research. For any subsurface process, what, precisely, is the instantaneous, spatiotemporal support of change?

Coupled nonlinear flow and transport is inherently a multiscale process; flow evolves on a global scale thereby dictating the extent of the system, whereas transport waves travel with a distinctly local spatial support, dictating resolution. This disparity in scales continues to challenge modern numerical simulation strategies which invariably rely on the orthogonal notions of resolution and extent. Through this work, we can develop full-resolution simulations of multiscale processes while only performing computation when and where it is absolutely necessary.

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Course Chapters

  • 1Advances On Sparse Representation of Reservoir Properties, Fluid Characterization Methods for Steam-Solvent Co-Injection, and Full-Resolution Simulations of Multi-Scale Processes - Chapter 1
    Media Type: Video

Credits

Earn credits by completing this course0.15 CEU credit1.5 PDH credits

Speakers

Behnam Jafarpour
Rami M. Younis
Ryosuke Okuno