Stochastic Pix2pix: A New Machine Learning Method for Geophysical and Well Conditioning of Rule-Based Channel Reservoir Models

Pan, Wen; Torres‐Verdín, Carlos; Pyrcz, Michael J.

doi:10.1007/s11053-020-09778-1

Cited by 38 publications

(12 citation statements)

References 56 publications

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“…As a follow-up of Pix2Pix, Pix2PixHD was proposed [90] improving the quality of the generated images. Many later works have used Pix2Pix [121,107,114,36] converting it to one of the most popular architectures of the last decade.…”

Section: Image-to-image Translation With Conditional Adversarial Nets...mentioning

confidence: 99%

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Iglesias¹,

Talavera²,

Díaz-Álvarez³

2022

Preprint

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In the last few years, there have been several revolutions in the field of deep learning, mainly headlined by the large impact of Generative Adversarial Networks (GANs). GANs not only provide an unique architecture when defining their models, but also generate incredible results which have had a direct impact on society. Due to the significant improvements and new areas of research that GANs have brought, the community is constantly coming up with new researches that make it almost impossible to keep up with the times. Our survey aims to provide a general overview of GANs, showing the latest architectures, optimizations of the loss functions, validation metrics and application areas of the most widely recognized variants. The efficiency of the different variants of the model architecture will be evaluated, as well as showing the best application area; as a vital part of the process, the different metrics for evaluating the performance of GANs and the frequently used loss functions will be analyzed.The final objective of this survey is to provide a summary of the evolution and performance of the GANs which are having better results to guide future researchers in the field.

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Section: Image-to-image Translation With Conditional Adversarial Nets...mentioning

confidence: 99%

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Iglesias¹,

Talavera²,

Díaz-Álvarez³

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Thus, G:( 𝑥, 𝑧) → y (Figure 6A). Furthermore, Isola et al (2017), in their pix2pix methodology, added the L1norm of the difference between the generated images with the real data (equation 2) over all pixels to the loss function (Pan et al 2020). This inclusion enhances the resolution of the We modify the pix2pix Generator and Discriminator networks from the original 256 x 256 pixels into 512 x 512 pixels to suit the objective of this study (Figure 6B).…”

Section: G Dmentioning

confidence: 99%

Acoustic impedance to outcrop: Presenting near-surface seismic data as a virtual outcrop in carbonate analog studies

Ramdani

Perbawa

Puspita³

et al. 2022

The Leading Edge

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Outcrop analogs play a central role in understanding subseismic interwell depositional facies heterogeneity of carbonate reservoirs. Outcrop geologists rarely utilize near-surface seismic data due to the limited vertical resolution and difficulty visualizing seismic signals as “band-limited rocks.” This study proposes a methodology using a combination of forward modeling and conditional generative adversarial network (cGAN) to translate seismic-derived acoustic impedance (AI) into a pseudo-high-resolution virtual outcrop. We tested the methodology on the Hanifa reservoir analog outcropping in Wadi Birk, Saudi Arabia. We interpret a 4 km long outcrop photomosaic from a digital outcrop model (DOM) for its depositional facies, populate the DOM with AI properties, and forward calculate the band-limited AI of the DOM facies using colored inversion. We pair the synthetic band-limited AI with DOM facies and train them using a cGAN. Similarly, we pair the DOM facies with outcrop photos and train them using a cGAN. We chain the two trained networks and apply them to the approximately 600 m long seismic-derived AI data acquired just behind the outcrop. The result translates AI images into a virtual outcrop “behind-the-outcrop” model. This virtual outcrop model is a visual medium that operates at a resolution and format more familiar to outcrop geologists. This model resolves subseismic stratigraphic features such as the intricate downlap-onlap stratal termination at scales of tens of centimeters and the outline of buildup facies, which are otherwise unresolvable in the band-limited AI.

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“…Petrophysical interpretation is important for accurate reservoir characterization, reservoir modeling calibration and decision making during the reservoir development (Xu et al, 1992;Pan et al,2021;Santos et al, 2021;Jo et al, 2021). However, the petrophysical interpretation of complex carbonate reservoirs, such as the prediction of formation properties, determination of geological settings and well-to-well correlation, is a major challenge in the oil and gas industry (Greder et al, 1996;Mohaghegh et al, 1997).…”

Section: Introductionmentioning

confidence: 99%

Reducing the Uncertainty of Multi-Well Petrophysical Interpretation from Well Logs via Machine-Learning and Statistical Models

Pan¹,

Torres‐Verdín²,

Duncan³

et al. 2022

Preprint

Self Cite

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Well-log interpretation provides in situ estimates of formation properties such as porosity, hydrocarbon pore volume, and permeability. Reservoir models based on well-log-derived formation properties deliver reserve-volume estimates, production forecasts, and help with decision making in reservoir development. However, due to measurement errors, variability of well logs due to multiple measurement vendors, different borehole tools, and non-uniform drilling/borehole conditions, conventional well-log interpretation methods may not yield accurate estimates of formation properties, especially in the context of multi-well interpretation. To improve the robustness of multi-well petrophysical interpretation, well-log normalization techniques such as two-point scaling and mean-variance normalization are commonly used to impose stationarity constraints for well logs requiring correction. However, these techniques are mostly based on the marginal distribution of well logs and require expert knowledge to be effectively implemented. To reduce the uncertainties and time associated with multi-well petrophysical interpretation, we develop the discriminative adversarial (DA) model and the linear constraint model for well-log normalization and interpretation. We also develop a new divergence-based type well identification method for improved test-well and training-well adaptation.The DA neural network model developed for well-log normalization and interpretation can perform both linear and nonlinear well-log normalization by considering the joint distribution of all types of well logs and formation properties. To train the DA model, classical machine-learning models or classical petrophysical models are first trained to minimize the prediction error of formation properties in the training data set; then the adversarial model is trained to normalize well logs in the test set, such that the joint distribution of normalized well logs and formation property estimates of the test data set reproduce those of the training data set. The linear constraint model uses an ensemble of predictions from linear models to constrain both well-log normalization and interpretation. To identify wells with stationary formation properties as well as well logs, the divergence-based type well identification method is developed to choose type wells for wells requiring correction based on well-log statistical similarity instead of closeness of wells.We apply the developed methods to improve the accuracy of well-log normalization and the estimation of permeability in a carbonate reservoir. Six types of well logs and over 9000 feet of core measurements from 30 wells drilled between 1980s and 2010s in the Seminole San Andres Unit are available to validate the new multi-well interpretation workflow. Our interpretation models is flexible to integrate any types of classical machine-learning methods and petrophysical assumptions for robust petrophysical estimations. In comparison to classical machine-learning models with no normalization, with two-point scaling normalization and with linear constraints, the DA method yields better performance, e.g., the mean-squared error of permeability estimation decreases by approximately 20-50%. Our interpretation workflow can be applied to other stationary signal and image processing problems to mitigate errors introduced by biased measurements, and to better adapt models calibrated with data from one field to other neighboring fields.

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Stochastic Pix2pix: A New Machine Learning Method for Geophysical and Well Conditioning of Rule-Based Channel Reservoir Models

Cited by 38 publications

References 56 publications

A survey on GANs for computer vision: Recent research, analysis and taxonomy

A survey on GANs for computer vision: Recent research, analysis and taxonomy

Acoustic impedance to outcrop: Presenting near-surface seismic data as a virtual outcrop in carbonate analog studies

Reducing the Uncertainty of Multi-Well Petrophysical Interpretation from Well Logs via Machine-Learning and Statistical Models

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