Three-dimensional simulation of droplet dynamics in a fractionally-wet constricted channel

Imani, Gloire; Zhang, Lei; Blunt, Martin J.; Foroughi, Sajjad; Ntibahanana, Munezero; Sun, Hai; Ye, Jun

doi:10.1016/j.advwatres.2022.104341

Cited by 11 publications

(2 citation statements)

References 29 publications

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“…It helps us identify areas underground that are valuable for producing hydrocarbon and geothermal energy, as well as determining how water is distributed below the surface (Ashraf et al., 2020; Kushwaha et al., 2020; Ullah et al., 2022). Porosity is important for estimating reserves and studying fluid flows and subsurface mechanical behavior (Imani et al., 2022; McBeck et al., 2019). Reservoir porosity models with high resolution allow the selection of the appropriate intervals for placing production wells (Figueiredo et al., 2018).…”

Section: Introductionmentioning

confidence: 99%

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

Ntibahanana,

Jianguo,

Luemba

et al. 2024

Earth and Space Science

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Seismic inversion holds significant importance across various domains of geoscience and engineering, including the characterization of energy resource reservoirs, the assessment of polluted sites, and CO2 storage. It is a process of estimating rock properties from seismic data that is inherently uncertain, nonlinear, non‐unique, and highly challenging. Using multiple seismic attributes increases the size of the data, requiring considerable processing resources and time. However, deep learning can accurately fit quantities of nonlinear variables, making it an excellent method for predicting spatially distributed subsurface properties. We trained some multi‐output regression neural networks to carry out porosity inversion from seismic data. We initially computed a series of seismic attributes and generated the corresponding porosity using interpreted horizons, well logs, and seismic data. Subsequently, we proposed a technique to identify the most relevant seismic attributes for porosity inversion. Because our networks work as stochastic modeling entities, we created a weight‐averaging ensemble approach to build a strong model with the highest level of accuracy. We combined realizations from baseline entities, considering their respective performance levels. Using the statistics between these realizations and the robust model, we determined the degree of uncertainty associated with the outcome. We found an R2 of 0.993 and an MAE of 0.00112 in the F3 block offshore the Netherlands, proving the method's effectiveness. The mean porosity was 0.175193, compared to 0.175626 from a reference model, and the mean uncertainty was ±0.0008998.

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Section: Introductionmentioning

confidence: 99%

Ensemble of Neural Networks Utilizing Seismic Attributes for Rock‐Property Inversion With Uncertainty Estimation

Ntibahanana,

Jianguo,

Luemba

et al. 2024

Earth and Space Science

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“…Porosity is a critical parameter in many applied geoscience and engineering fields for delineating and characterizing petroleum and geothermal energy reservoirs in order to further quantify reserves and drill production wells. Predicting and analyzing subsurface porosity enables understanding the properties of fluid flow [1], [2] and the mechanical behavior of rocks [3]. In addition, porosity is key in the assessment of subsurface contamination [4] and CO 2 sequestration [5].…”

Section: Introductionmentioning

confidence: 99%

Ensemble Deep Learning-Based Porosity Inversion From Seismic Attributes

et al. 2023

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Underground porosity is important in many earth sciences and engineering fields, including hydrocarbon reservoir characterization and geothermal energy production. Popular methods largely rely on the analysis of lithological core data, well logs, and seismic inversion methods. While these methods are reliable, they are also time-consuming, expensive, and difficult to implement. In addition, seismic inversion has nonlinearity, data dimensionality, and non-uniqueness issues. However, deep learning (DL) can provide a more flexible, efficient, and accurate capability by mapping directly from seismic attributes to underground porosity. Therefore, we trained several DL models with different optimization functions. In the training steps, we labelled every seismic attribute data point with its corresponding porosity derived from the welllogs. In contrast to popular ensemble techniques, we proposed a weighted prediction approach based on the strengths of each model. Testing results showed a coefficient of determination (R 2 ) of 0.94345 and a Pearson's correlation coefficient of 0.9725 between the actual model and the model of the proposed approach, versus 0.9681 and 0.9716 for the best single and popular ensemble models, respectively. Further, we tested the effectiveness of our method using real seismic data from the North Sea. With a Pearson's correlation value of 0.9743, the inverted model ranges from 27 to 35%, compared to the reference model, which has an overall range of 20 to 33%. These results provide insights into the potential of the proposed method and its applicability to any other seismic volume to determine spatially varying underground porosity from seismic attributes directly.INDEX TERMS Deep neural networks, ensemble methods, seismic inversion, reservoir properties.

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