Reservoir Static Property Estimation Using Nearest-Neighbor Neural Network

• URL: http://arxiv.org/abs/2409.15295v2
• Date: Fri, 27 Sep 2024 07:05:47 GMT
• Title: Reservoir Static Property Estimation Using Nearest-Neighbor Neural Network
• Authors: Yuhe Wang,
• Abstract summary: This note presents an approach for estimating the spatial distribution of static properties in reservoir modeling using a nearest-neighbor neural network. It incorporates a nearest-neighbor to capture local spatial relationships between data points and introduces randomization to quantify the uncertainty inherent in the process.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This note presents an approach for estimating the spatial distribution of static properties in reservoir modeling using a nearest-neighbor neural network. The method leverages the strengths of neural networks in approximating complex, non-linear functions, particularly for tasks involving spatial interpolation. It incorporates a nearest-neighbor algorithm to capture local spatial relationships between data points and introduces randomization to quantify the uncertainty inherent in the interpolation process. This approach addresses the limitations of traditional geostatistical methods, such as Inverse Distance Weighting (IDW) and Kriging, which often fail to model the complex non-linear dependencies in reservoir data. By integrating spatial proximity and uncertainty quantification, the proposed method can improve the accuracy of static property predictions like porosity and permeability.

Related papers

• Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging [3.8623569699070353]
  Trajectory inference deals with the challenge of reconstructing continuous processes from such observations. We propose methods for B-spline approximation of point clouds through consecutive averaging that is instrinsic to the Wasserstein space. We rigorously evaluate our method by providing convergence guarantees and testing it on simulated cell data.
  arXiv  Detail & Related papers  (2024-05-30T04:19:20Z)
• A Neural Network-Based Enrichment of Reproducing Kernel Approximation for Modeling Brittle Fracture [0.0]
  An improved version of the neural network-enhanced Reproducing Kernel Particle Method (NN-RKPM) is proposed for modeling brittle fracture. The effectiveness of the proposed method is demonstrated by a series of numerical examples involving damage propagation and branching.
  arXiv  Detail & Related papers  (2023-07-04T21:52:09Z)
• Efficient Large-scale Nonstationary Spatial Covariance Function Estimation Using Convolutional Neural Networks [3.5455896230714194]
  We use ConvNets to derive subregions from the nonstationary data. We employ a selection mechanism to identify subregions that exhibit similar behavior to stationary fields. We assess the performance of the proposed method with synthetic and real datasets at a large scale.
  arXiv  Detail & Related papers  (2023-06-20T12:17:46Z)
• Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees [57.67528738886731]
  We study the numerical stability of scalable sparse approximations based on inducing points. For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.
  arXiv  Detail & Related papers  (2022-10-14T15:20:17Z)
• PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows [58.96306192736593]
  We present PU-Flow, which incorporates normalizing flows and feature techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we formulate the upsampling process as point in a latent space, where the weights are adaptively learned from local geometric context. We show that our method outperforms state-of-the-art deep learning-based approaches in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.
  arXiv  Detail & Related papers  (2021-07-13T07:45:48Z)
• Featurized Density Ratio Estimation [82.40706152910292]
  In our work, we propose to leverage an invertible generative model to map the two distributions into a common feature space prior to estimation. This featurization brings the densities closer together in latent space, sidestepping pathological scenarios where the learned density ratios in input space can be arbitrarily inaccurate. At the same time, the invertibility of our feature map guarantees that the ratios computed in feature space are equivalent to those in input space.
  arXiv  Detail & Related papers  (2021-07-05T18:30:26Z)
• Clustered Federated Learning via Generalized Total Variation Minimization [83.26141667853057]
  We study optimization methods to train local (or personalized) models for local datasets with a decentralized network structure. Our main conceptual contribution is to formulate federated learning as total variation minimization (GTV) Our main algorithmic contribution is a fully decentralized federated learning algorithm.
  arXiv  Detail & Related papers  (2021-05-26T18:07:19Z)
• Tensor Laplacian Regularized Low-Rank Representation for Non-uniformly Distributed Data Subspace Clustering [2.578242050187029]
  Low-Rank Representation (LRR) suffers from discarding the locality information of data points in subspace clustering. We propose a hypergraph model to facilitate having a variable number of adjacent nodes and incorporating the locality information of the data. Experiments on artificial and real datasets demonstrate the higher accuracy and precision of the proposed method.
  arXiv  Detail & Related papers  (2021-03-06T08:22:24Z)
• Robust and integrative Bayesian neural networks for likelihood-free parameter inference [0.0]
  State-of-the-art neural network-based methods for learning summary statistics have delivered promising results for simulation-based likelihood-free parameter inference. This work proposes a robust integrated approach that learns summary statistics using Bayesian neural networks, and directly estimates the posterior density using categorical distributions.
  arXiv  Detail & Related papers  (2021-02-12T13:45:23Z)
• Optimal oracle inequalities for solving projected fixed-point equations [53.31620399640334]
  We study methods that use a collection of random observations to compute approximate solutions by searching over a known low-dimensional subspace of the Hilbert space. We show how our results precisely characterize the error of a class of temporal difference learning methods for the policy evaluation problem with linear function approximation.
  arXiv  Detail & Related papers  (2020-12-09T20:19:32Z)
• Spatially Adaptive Inference with Stochastic Feature Sampling and Interpolation [72.40827239394565]
  We propose to compute features only at sparsely sampled locations. We then densely reconstruct the feature map with an efficient procedure. The presented network is experimentally shown to save substantial computation while maintaining accuracy over a variety of computer vision tasks.
  arXiv  Detail & Related papers  (2020-03-19T15:36:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.