1,140 research outputs found

    Neural network applications to reservoirs: Physics-based models and data models

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    Using Convolutional Neural Networks to Develop Starting Models for 2D Full Waveform Inversion

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    Non-invasive subsurface imaging using full waveform inversion (FWI) has the potential to fundamentally change engineering site characterization by enabling the recovery of high resolution 2D/3D maps of subsurface stiffness. Yet, the accuracy of FWI remains quite sensitive to the choice of the initial starting model due to the complexity and non-uniqueness of the inverse problem. In response, we present the novel application of convolutional neural networks (CNNs) to transform an experimental seismic wavefield acquired using a linear array of surface sensors directly into a robust starting model for 2D FWI. We begin by describing three key steps used for developing the CNN, which include: selection of a network architecture, development of a suitable training set, and performance of network training. The ability of the trained CNN to predict a suitable starting model for 2D FWI was compared against other commonly used starting models for a classic near-surface imaging problem; the identification of an undulating, two-layer, soil-bedrock interface. The CNN developed during this study was able to predict complex 2D subsurface images of the testing set directly from their seismic wavefields with an average mean absolute percent error of 6%. When compared to other common approaches, the CNN approach was able to produce starting models with smaller seismic image and waveform misfits, both before and after FWI. The ability of the CNN to generalize to subsurface models which were dissimilar to the ones upon which it was trained was assessed using a more complex, three-layered model. While the predictive ability of the CNN was slightly reduced, it was still able to achieve seismic image and waveform misfits comparable to the other commonly used starting models. This study demonstrates that CNNs have great potential as a tool for developing good starting models for FWI ...Comment: 29 pages, 16 figures, submitted to Geophysic

    j-Wave: An open-source differentiable wave simulator

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    We present an open-source differentiable acoustic simulator, j-Wave, which can solve time-varying and time-harmonic acoustic problems. It supports automatic differentiation, which is a program transformation technique that has many applications, especially in machine learning and scientific computing. j-Wave is composed of modular components that can be easily customized and reused. At the same time, it is compatible with some of the most popular machine learning libraries, such as JAX and TensorFlow. The accuracy of the simulation results for known configurations is evaluated against the widely used k-Wave toolbox and a cohort of acoustic simulation software. j-Wave is available from https://github.com/ucl-bug/jwave

    Transdimensional surface wave tomography of the near-surface: Application to DAS data

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    Distributed Acoustic Sensing (DAS) is a novel technology that allows sampling of the seismic wavefield densely over a broad frequency band. This makes it an ideal tool for surface wave studies. In this study, we evaluate the potential of DAS to image the near-surface using synthetic data and active-source field DAS data recorded with straight fibers in Groningen, the Netherlands. First, we recover the laterally varying surface wave phase velocities (i.e., local dispersion curves) from the fundamental-mode surface waves. We utilize the Multi Offset Phase Analysis (MOPA) for the recovery of the laterally varying phase velocities. In this way, we take into account the lateral variability of the subsurface structures. Then, instead of inverting each local dispersion curve independently, we propose to use a novel 2D transdimensional surface wave tomography algorithm to image the subsurface. In this approach, we parameterize the model space using 2D Voronoi cells and invert all the local dispersion curves simultaneously to consider the lateral spatial correlation of the inversion result. Additionally, this approach reduces the solution nonuniqueness of the inversion problem. The proposed methodology successfully recovered the shear-wave velocity of the synthetic data. Application to the field data also confirms the reliability of the proposed algorithm. The recovered 2D shear-wave velocity profile is compared to shear-wave velocity logs obtained at the location of two boreholes, which shows a good agreement

    Caracterização petrofísica das coquinas da formação Morro do Chaves (Bacia de Sergipe-Alagoas) utilizando a tomografia computadorizada de raios X

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    sem InformaçãoCarbonate rocks constitute a large number of petroleum reservoirs worldwide. Notwithstanding, the characterization of these rocks is still a challenge due to their high complexity and pore space variability, indicating the importance of further studies to183313sem Informaçãosem Informaçãosem Informaçã

    Caracterização petrofísica das coquinas da Formação Morro do Chaves (Bacia de Sergipe-Alagoas) utilizando a tomografia computadorizada de raios X

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    Carbonate rocks constitute a large number of petroleum reservoirs worldwide. Notwithstanding, the characterization of these rocks is still a challenge due to their high complexity and pore space variability, indicating the importance of further studies to reduce uncertainty in reservoir interpretation and characterization. This work was performed for coquina samples from Morro do Chaves Formation (Sergipe-Alagoas Basin), analogous to important Brazilian reservoirs. Computed tomography (CT) was used for three-dimensional characterization of rock structure. The neural network named Self-Organizing Maps (SOM) was used for CT images segmentation. According to our tests, CT demonstrated to be a consistent tool for quantitative and qualitative analysis of heterogeneous pore space, by the evaluation of porosity, connectivity and the representative elementary volume.As rochas carbonáticas constituem um grande número de reservatórios de petróleo no mundo, contudo a caracterização dessas rochas ainda é um desafio em virtude de sua alta complexidade e da variabilidade do espaço poroso, indicando a im-portância de novos estudos para reduzir a incerteza associada à interpretação e caracterização dos reservatórios carbonáti-cos. Este trabalho foi realizado para amostras de coquinas da Formação Morro do Chaves — Bacia de Sergipe-Alagoas —, rochas análogas a importantes reservatórios brasileiros. A tomografia computadorizada (TC) de raios X foi empregada para a caracterização tridimensional da estrutura da rocha. A rede neural Self-Organizing Maps (SOM) foi utilizada para a seg-mentação das imagens tomográficas. De acordo com nossos testes, a TC demonstrou ser uma ferramenta consistente para a análise qualitativa e quantitativa de espaços porosos heterogêneos, avaliando a porosidade, a conectividade e o volume elementar representativo
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