Efficient application of stochastic Discrete Well Affinity (DiWA) proxy model with adjoint gradients for production forecast

Tian, Xiaoming; Voskov, Denis

doi:10.1016/j.petrol.2021.109911

Cited by 6 publications

(7 citation statements)

References 30 publications

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“…In this study, we will use the stochastic DiWA framework for data quality diagnostic and production forecast. The reliability and the accuracy of the stochastic DiWA model has been shown on the synthetic Brugge model (Tian and Voskov, 2022). The application of stochastic DiWA model to a real oil field is demonstrated in this study.…”

Section: Introductionmentioning

confidence: 77%

“…An application of stochastic DiWA model to a synthetic reservoir field was described in Tian and Voskov (2022). In this section, we present an application of stochastic DiWA methodology to a sector of a real hydrocarbon reservoir.…”

Section: Resultsmentioning

confidence: 99%

“…Eventually, Ju u u is assembled and combined with the numerical gradients of rock-fluid variables for gradient descent. More details and discussions about the adjoint method can be found in Jansen (2011) and Tian and Voskov (2022).…”

Section: Adjoint Gradientsmentioning

confidence: 99%

“…The initial conditions of DiWA model, including initial reservoir pressure, well locations, average water saturation, and average depth, are consistent with the high-fidelity model. This is done for simplicity and can be easily replaced by approximate data as in Tian and Voskov (2022).…”

Section: Unstructured Proxy Modelmentioning

confidence: 99%

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Stochastic Discrete Well Affinity (DiWA) Model for Data Quality Diagnostic and Production Forecast

et al. 2022

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Section: Introductionmentioning

confidence: 77%

Section: Resultsmentioning

confidence: 99%

Section: Adjoint Gradientsmentioning

confidence: 99%

Section: Unstructured Proxy Modelmentioning

confidence: 99%

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Stochastic Discrete Well Affinity (DiWA) Model for Data Quality Diagnostic and Production Forecast

et al. 2022

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“…In the second stage, the classical incomplete Lower-Upper factorization (ILU(0)) preconditioner is applied to the FIM system. DARTS also has advanced inversion capabilities including efficient adjoint gradients implementation [56,57].…”

Section: Darts: Tu-delftmentioning

confidence: 99%

A benchmark study on reactive two-phase flow in porous media: Part II - results and discussion

Ahusborde,

Amaziane,

de Hoop

et al. 2024

Comput Geosci

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This paper presents and discusses the results obtained by the participants to the benchmark described in deHoop et al, Comput. Geosci. (2023). The benchmark uses a model for CO 2 geological storage and focuses on the coupling between two-phase flow and geochemistry. Several test cases of various levels of difficulty are proposed, both in one and two spatial dimensions. Six teams participated in the benchmark, each with their own simulation code, though not all teams attempted all the cases. The codes used by the participants are described, and the results obtained on the various test cases are compared, as well as the performance of the codes. It is shown that the results obtained are widely consistent, giving a good level of confidence in the outcome of the benchmark. The general complexity of two-phase flow coupled with chemical reactions altering porous media means that some differences between the codes remain. Besides, from the convergence study, it is clear that the two-dimensional problem has a relatively high sensitivity to a spatial resolution which adds to the complexity.

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A History Matching Study for the FluidFlower Benchmark Project

Tian,

Wapperom,

Gunning

et al. 2024

Transp Porous Med

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In this study, we conduct a comprehensive history matching study for the FluidFlower benchmark model. This benchmark was prepared and organized by the University of Bergen, the University of Stuttgart, and Massachusetts Institute of Technology, for promoting understanding of the complex physics of geological carbon storage (GCS) through in-house experiments and numerical simulations. This paper synthesizes the experiences of history matching the benchmark data encountered by the Delft-DARTS and CSIRO participants. History matching is first performed based on a low-dimensional-zonated structured model using a simple Poisson-like solver. The permeability of six facies and two faults is inferred in this stage to match the digitized concentration data. The history matching is then further enhanced to richer spatial and physical models to capture the spatial variation of permeability and buoyancy effects, using an unstructured grid. Efficient adjoint methods are used to evaluate the gradient used in the optimization of data misfits or equivalent Bayesian log-likelihoods. With efficient optimization methods available for both maximum a posteriori model inference and Randomized Maximum Likelihood methods for model uncertainty, we perform history matching using both binary and continuous concentration observations. The results show that the tracer plumes in the enriched model match the experimental plumes more accurately compared with the results from the parsimonious-zonated model. The history matching results based on the concentration observations provide more similar plume shapes compared with the case based on the binary observations. The permeability difference between the model before and after history matching reveals that the tracer plume zone and the high permeable zone are the regions of high sensitivity in terms of data misfit between the model response and observations. Surprisingly, CO$$_2$$ 2 concentration plume forecasts based on these history-matched models were not especially sensitive to the improvements observed in the enhanced model.

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Efficient application of stochastic Discrete Well Affinity (DiWA) proxy model with adjoint gradients for production forecast

Cited by 6 publications

References 30 publications

Stochastic Discrete Well Affinity (DiWA) Model for Data Quality Diagnostic and Production Forecast

Stochastic Discrete Well Affinity (DiWA) Model for Data Quality Diagnostic and Production Forecast

A benchmark study on reactive two-phase flow in porous media: Part II - results and discussion

A History Matching Study for the FluidFlower Benchmark Project

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