Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network

Ishak, M Anwar; Latiff, Abdul Halim Abdul; Ho, Eric Tatt Wei; Fuad, M.I. Ahmad; Tan, Nian Wei; Sajid, Muhammad; Elsebakhi, Emad

doi:10.3390/app13106311

Cited by 3 publications

(1 citation statement)

References 39 publications

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“…In recent years, acoustic FWI has successfully inverted industrial-scale real 3D marine data [8] and refracted waves in ocean bottom node data [9]. However, modeling of elastic wave propagation through elastodynamic theory is required for reflection analysis [10,11]. In real cases, when seismic waves propagate in underground media, if they encounter a reflecting interface, energy conversion will occur and converted waves will be generated.…”

Section: Introductionmentioning

confidence: 99%

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

Qi,

Huang,

Zhang

et al. 2023

Applied Sciences

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The elastic full-waveform inversion (EFWI) method efficiently utilizes the amplitude, phase, and travel time information present in multi-component seismic recordings to create detailed parameter models of subsurface structures. Within full-waveform inversion (FWI), accurate source wavelet estimation significantly impacts both the convergence and final result quality. The source wavelet, serving as the initial condition for the wave equation’s forward modeling algorithm, directly influences the matching degree between observed and synthetic data. This study introduces a novel method for estimating the source wavelet utilizing cross-correlation norm elastic waveform inversion (CNEWI) and outlines the EFWI algorithm flow based on this CNEWI source wavelet inversion. The CNEWI method estimates the source wavelet by employing normalized cross-correlation processing on near-offset direct waves, thereby reducing the susceptibility to strong amplitude interference such as bad traces and surface wave residuals. The proposed CNEWI method exhibits a superior computational efficiency compared to conventional L2-norm waveform inversion for source wavelet estimation. Numerical experiments, including in ideal scenarios, with seismic data with bad traces, and with multi-component data, validate the advantages of the proposed method in both source wavelet estimation and EFWI compared to the traditional inversion method.

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Section: Introductionmentioning

confidence: 99%

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

Qi,

Huang,

Zhang

et al. 2023

Applied Sciences

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Reservoir Property Prediction in the North Sea Using Machine Learning

Al-Fakih,

Kaka,

Koeshidayatullah

2023

IEEE Access

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The North Sea sedimentary basin is characterized by geological complexity, encompassing a wide range of rock types and structures, including multiple reservoirs (carbonates and siliciclastic) with variations in reservoir quality and heterogeneity. These phenomena pose significant challenges for accurately predicting reservoir properties using traditional well log analysis. Moreover, these challenges are further compounded by complex geological conditions and scarcity of available data. Hence, the aim of this study was to address these challenges by applying advanced machine learning techniques within this basin. This study delves into both supervised and unsupervised machine learning approaches to forecast the essential petrophysical properties that are crucial for assessing reservoir quality. These properties encompass total porosity, effective porosity, and shale volume, all derived from well log data originating from the North Sea sedimentary basin. The models were trained using data from four wells consisting of 32,215 data points (80% for training, 10% for testing, and 10% for validation). Furthermore, our study introduced pioneering datadriven preprocessing workflow, encompassing exploratory data analysis, missing data imputation, and outlier detection to improve the performance of the machine learning models. ANN and RF models achieved the best results among the algorithms evaluated, with an average MAE of 0.01, RMSE of 0.01, and R-squared of 0.95 for total porosity, effective porosity, and volume of shale, respectively. These metrics demonstrate that the model can accurately predict reservoir properties in a challenging sedimentary basin, even with limited data availability, enabling reservoir characteristics and field development optimization, particularly in areas where core data are scarce.

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Deep Learning for Geothermal Reservoir Characterization: Estimating Rock Properties from Seismic Data Using Convolutional Neural Networks

Shreif,

de Chizelle,

Turner

et al. 2024

SPE Europe Energy Conference and Exhibition

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Estimating rock properties is a crucial aspect of geothermal reservoir characterization, which plays a pivotal role in the efficient harnessing of geothermal energy. Rock properties include hydraulic properties, such as porosity and permeability, and elastic properties such as Poisson’s ratio, P-wave, S-wave velocity, bulk modulus, and acoustic impedance. Accurate determination of these properties allows geoscientists and reservoir engineers to assess and optimize the reservoir performance and assess the long-term stability of geothermal projects. Seismic inversion is the process of deriving these rock properties from seismic data. Conventional seismic inversion can be time-consuming and costly. Machine learning can effectively estimate rock properties which reducesthe need to rely on conventional seismic inversion, expensive lab experiments, and well logging data. This study aims to estimate keyrock properties (acoustic impedance, bulk modulus, density, permeability, Poisson’s ratio, and porosity) from the SCAN dataset using a convolutional neural network. The proposed U-net architecturewas used to develop models that rely on a full-stack seismic dataset as inputs to the model. Mean SquaredError (MSE) with a regularization factor was considered as a loss function when training the model and Mean Absolute Error (MAE) to assess the performance of the model. Results reveal an effective performance of the developed models in the estimation of rock properties with low MAE values ranging between 0.5-3 %. The higher MAE observed for the porosity and permeability estimation is attributed to poor data coverage in the ground truth data.This study demonstrates the potential of convolutional neural networks to predict rock properties from seismic data for efficient reservoir characterization.

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Advanced Elastic and Reservoir Properties Prediction through Generative Adversarial Network

Cited by 3 publications

References 39 publications

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

Robust Elastic Full-Waveform Inversion Based on Normalized Cross-Correlation Source Wavelet Inversion

Reservoir Property Prediction in the North Sea Using Machine Learning

Deep Learning for Geothermal Reservoir Characterization: Estimating Rock Properties from Seismic Data Using Convolutional Neural Networks

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