François Fleuret

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Unsupervised Electrofacies Clustering Based on Parameterization of Petrophysical Properties: A Dynamic Programming Approach

François Fleuret, Karthigan Sinnathamby

Electrofacies using well logs play a vital role in reservoir characterization. Often, they are sorted into clusters according to the self-similarity of input logs and do not capture the known underlying physical process. In this paper, we propose an unsupervised clustering algorithm based on the concept of dynamic programming, in which the underlying physical processes and geological constraints, such as the number of clusters, number of transitions between clusters, and minimal size of formation layers, can be directly integrated. We benchmark the proposed algorithm with synthetic data sets and demonstrate its applications to two field examples, where formations are clustered into zones through automated clustering using a consistent resistivity response. The inputs for our examples are porosity, clay volume fraction from elemental analysis, invaded zone resistivity, and invaded zone water saturation from dielectric interpretation or nuclear magnetic resonance logs. The proposed algorithm provides the optimized cluster pattern/electrofacies that satisfies desired constraints and enables the extraction of relevant petrophysical parameters, such as brine resistivity, cementation, and saturation exponents, as well as parameters that relate to the cation exchange capacity (CEC) of the clay for shaly-sand formations. Beyond the immediate examples demonstrated in this paper, we present the proposed algorithm in a generic form such that it can be easily tailored to the task at hand, taking into account any prior knowledge of the physics of the underlying process.

SOC PETROPHYSICISTS & WELL LOG ANALYSTS-SPWLA2023

We present GeoNeRF, a generalizable photorealistic novel view synthesis method based on neural radiance fields. Our approach consists of two main stages: a geometry reasoner and a renderer. To render a novel view, the geometry reasoner first constructs cascaded cost volumes for each nearby source view. Then, using a Transformer-based attention mechanism and the cascaded cost volumes, the renderer infers geometry and appearance, and renders detailed images via classical volume rendering techniques. This architecture, in particular, allows sophisticated occlusion reasoning, gathering information from consistent source views. Moreover, our method can easily be fine-tuned on a single scene, and renders competitive results with per-scene optimized neural rendering methods with a fraction of computational cost. Experiments show that GeoNeRF outperforms state-of-the-art generalizable neural rendering models on various synthetic and real datasets. Lastly, with a slight modification to the geometry reasoner, we also propose an alternative model that adapts to RGBD images. This model directly exploits the depth information often available thanks to depth sensors.

IEEE COMPUTER SOC2022

Angular analysis of B0->D-Ds+ with Ds+-> Ds+gamma decays

Hossein Afsharnia, Liupan An, Vladislav Balagura, Aurelio Bay, Violaine Bellée, Federico Betti, Frédéric Blanc, Alexey Boldyrev, Maxim Borisyak, Sara Celani, Chunhui Chen, Serhii Cholak, Peter Clarke, Victor Coco, Tommaso Colombo, Adam Davis, Michel De Cian, Hans Dijkstra, Paolo Durante, Dipanwita Dutta, Surapat Ek-In, François Fleuret, Christoph Frei, Elena Graverini, Marco Guarise, Guido Haefeli, Xiaoxue Han, Maxim Karpov, Veronica Sølund Kirsebom, Almagul Kondybayeva, Yiming Li, Ho Ling Li, Hao Liu, Shuai Liu, Vladimir Macko, Maurizio Martinelli, Simone Meloni, Tatsuya Nakada, Tara Nanut, Matthew Needham, Niko Neufeld, Preema Rennee Pais, Anton Petrov, Guillaume Max Pietrzyk, Renato Quagliani, Gerhard Raven, Federico Leo Redi, Olivier Schneider, Maxime Schubiger, Sebastian Schulte, Lesya Shchutska, Evgenii Shmanin, Pavol Stefko, Maria Elena Stramaglia, Liang Sun, Frédéric Teubert, Mark Tobin, Minh Tâm Tran, Carina Trippl, Yi Wang, Rui Wang, Mingkui Wang, Zheng Wang, Jian Wang, Zhirui Xu, Yong Yang, Hang Yin, Ettore Zaffaroni, Lei Zhang, Yi Zhang, Yu Zheng, Yao Zhou, Xiaoqing Zhou

The first full angular analysis of the B0 -> D-Ds+ decay is performed using 6 fb(-1) of pp collision data collected with the LHCb experiment at a centre-of-mass energy of 13 TeV. The Ds+-> Ds+gamma and D*- -> D0- vector meson decays are used with the subsequent Ds+ -> K+K-pi (+) and D0 -> K+pi (-) decays. All helicity amplitudes and phases are measured, and the longitudinal polarisation fraction is determined to be f(L) = 0.578 +/- 0.010 +/- 0.011 with world-best precision, where the first uncertainty is statistical and the second is systematic. The pattern of helicity amplitude magnitudes is found to align with expectations from quark-helicity conservation in B decays. The ratio of branching fractions [B(B0 -> D-Ds+) x B(Ds+-> Ds+gamma)]/B(B-0 -> D(-)Ds+) is measured to be 2.045 +/- 0.022 +/- 0.071 with world-best precision. In addition, the first observation of the Cabibbo-suppressed B-s -> D(-)Ds+ decay is made with a significance of seven standard deviations. The branching fraction ratio B(B-s -> D(-)Ds+)/B(B-0 -> D(-)Ds+) is measured to be 0.049 +/- 0.006 +/- 0.003 +/- 0.002, where the third uncertainty is due to limited knowledge of the ratio of fragmentation fractions.

SPRINGER2021