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Collaborating Authors
Artificial Intelligence
Influence of Strong Directional Sources in Ambient Seismic Imaging using Traffic-induced Noise
Zhang, Chengli (Guizhou University) | Pan, Jianwei (Guizhou University, Guizhou University) | Liu, Jiaxu (Guizhou University) | Zhan, Lin (Guizhou University) | Gao, Jian (Guizhou University) | Luo, Haixin (Guizhou University) | Yang, Chen (Guizhou University)
Continuously moving seismic sources, such as vehicles and train cars, play a crucial role as passive sources for non-destructive exploration in urban areas. Seismic interferometry is commonly employed in field data acquisition and processing, using linear arrays. However, the simultaneous recording of high energy noise, excited by buildings and factories, cannot be overlooked. We simulate moving-source seismic records with strong noises using orthogonal arrays. When strong noise originates from specific angles, the energy-phase velocity curves of arrays in different directions significantly diverge. We demonstrate that an L-shaped array, formed by orthogonal arrays, can effectively mitigate this effect, yielding more consistent results. Nonetheless, spatial constraints often preclude the deployment of L-shaped arrays. To mitigate this issue, we propose a new parallel observation system in this paper. Field tests conducted on a main road validate that the new array is comparable to the L-shaped array in terms of dispersion extraction. Similar to synthetic data, the phase velocity extracted from the linear array field data is found to be unreliable. Drilling data aligns well with the inversion results of the parallel observation system. Given the challenges of urban traffic-induced signal acquisition, deploying multi-azimuth arrays to minimize noise impact is essential. Considering spatial limitations, the convenient parallel observation system emerges as a good choice.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Passive Seismic Surveying (1.00)
- Transportation > Ground (1.00)
- Energy > Oil & Gas > Upstream (1.00)
Multichannel deconvolution with a high-frequency structural regularization
Wang, Pengfei (China University of Petroleum) | Zhao, Dongfeng (China University of Petroleum) | Niu, Yue (National Engineering Research Center for Oil and Gas Exploration Computer Software) | Li, Guofa (China University of Petroleum) | Gu, Weiwei (China University of Petroleum)
The resolution of seismic data determines the ability to characterize stratigraphic features from observed seismic record. Sparse spike inversion (SSI) as an important processing method can effectively improve the band-limited property of the seismic data. However, the approch ignores the spatial information along seismic traces, which causes the unreliability of the reconstructed high-resolution data. In this article, we have developed a high-frequency structure constrained multichannel deconvolution (HFSC-MD) to alleviate this issue. This method allows the cost function to incorporate high-frequency spatial information in the form of prediction-error filter (PEF), to regularize the components of the result beyond the original frequency. The PEF also called high-frequency structural characterization operator (HFRSC operator), is estimated from the mapping relationship of low and high-frequency components. We adopt the alternating direction method of multipliers (ADMM) to solve the cost function in HFSC-MD. Synthetic and field data demonstrate that the proposed method recovers more reliable high-resolution data, and enriches the reflective structures.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (0.34)
Accurate production forecasting plays a pivotal role in understanding and effectively developing reservoirs. Numerous research shows that machine learning could be used to achieve fast and precise production predictions. In this three-part article, we use long short-term memory (LSTM), a machine learning technique, to predict oil, gas, and water production using real field data. This first part discusses the mathematics behind the LSTM and part two and three focus on its practical implementation. Recurrent neural network (RNN) and its variants, namely LSTM and gated recurrent unit (GRU), are specifically tailored for time-dependent data.
The Abu Dhabi National Oil Company (ADNOC) announced this week that its implementation of artificial intelligence (AI) technologies generated an additional half-billion dollars in value last year. The company credited more than 30 AI programs for contributing to the gains, highlighting their impact across its entire value chain. ADNOC also reported that these AI initiatives helped to prevent the emission of up to a million metric tons of CO2 from 2022 to 2023. "Artificial intelligence is one of the most important economic and social game changers of our era, and it can play a crucial role in accelerating a just, orderly, and equitable energy transition," Sultan Ahmed Al Jaber, CEO of ADNOC, stated. "At ADNOC, we have integrated artificial intelligence across our operations, from the control room to the boardroom, and it is enabling us to make smarter decisions and better protect our people and the environment."
- Government > Regional Government > Asia Government > Middle East Government > UAE Government (1.00)
- Energy > Oil & Gas (1.00)
The Abu Dhabi National Oil Company (ADNOC) has announced that it generated 500 million in value by deploying artificial intelligence (AI) in 2023. The value was generated from the integration of more than 30 AI tools across ADNOC's value chain, the company said. Additionally, the company said, these applications abated up to 1 million tonnes of carbon dioxide emissions between 2022 and 2023, the equivalent of removing around 200,000 gasoline-powered cars from the road. The milestone marks the start of the company's multiyear program to accelerate the deployment of AI to enhance safety, while driving down emissions and driving up value. "Artificial intelligence is one of the most important economic and social game changers of our era, and it can play a crucial role in accelerating a just, orderly and equitable energy transition," said Sultan Ahmed Al Jaber, the UAE's minister of industry and advanced technology and the managing director and group CEO of ADNOC.
- Government > Regional Government > Asia Government > Middle East Government > UAE Government (1.00)
- Energy > Oil & Gas (1.00)
Simultaneous source acquisition has become common over the past few decades for marine seismic surveys because of the increased efficiency of seismic acquisition by limiting the time, reducing the cost, and having less environmental impact than conventional single-source (or unblended) acquisition surveys. For simultaneous source acquisition, seismic sources at different locations are fired with time delays, and the recorded data are referred to as the blended data. The air-water interface (or free surface) creates strong multiples and ghost reflections for blended seismic data. The multiples and/or ghost reflections caused by a source in the blended data overlap with the primary reflections of another source, thus creating a strong interference between the primary and multiple events of different sources. We develop a convolutional neural network (CNN) method to attenuate free-surface multiples and remove ghost reflections simultaneously from the blended seismic data. The CNN-based solution that we develop operates on single traces and is not sensitive to the missing near-offset traces, missing traces, and irregular/sparse acquisition parameters (e.g.,for ocean-bottom node acquisition and time-lapse monitoring studies). We illustrate the efficacy of our free-surface multiple attenuation and seismic deghosting method by presenting synthetic and field data applications. The numerical experiments demonstrate that our CNN-based approach for simultaneously attenuating free-surface multiples and removing ghost reflections can be applied to the blended data without the deblending step. Although the interference of primaries and multiples from different shots in the blended data makes free-surface multiple attenuation harder than the unblended data, we determine that our CNN-based method effectively attenuates free-surface multiples in the blended synthetic and field data even when the delay time for the blending is different in the training data than in the data to which the CNN is applied.
- North America > United States > Illinois > Madison County (0.34)
- Europe > United Kingdom > North Sea (0.29)
- Europe > Norway > North Sea (0.29)
- North America > United States > Colorado (0.28)
Colorado School of Mines and the US Geological Survey (USGS) announced they will partner to establish a joint industry program to explore the potential of geologic hydrogen as a low-carbon energy source. Eight companies including BP, Chevron, and Petrobras, have signed on as industry partners to help fund the program. The consortium's research will focus on the development of four key areas: A geologic "hydrogen system" model that identifies sources, migration pathways and mechanisms, reservoirs, traps, and seals leading to accumulations of hydrogen in the subsurface. Surface exploration approaches, including remote sensing and surface geochemistry, to refine our understanding of where hydrogen accumulations exist in the subsurface. Subsurface exploration tools, including multiple geophysical tools, advanced signal processing and artificial intelligence tools, to image geologic hydrogen systems and potential economic accumulations suitable for energy production.
- Energy > Renewable > Hydrogen (1.00)
- Government > Regional Government > North America Government > United States Government (0.98)
Elastic Full Waveform Inversion (EFWI) is a process used to estimate subsurface properties by fitting seismic data while satisfying wave propagation physics. The problem is formulated as a least squares data fitting minimization problem with two sets of constraints: PDE constraints governing elastic wave propagation and physical model constraints implementing prior information. The Alternating Direction Method of Multipliers (ADMM) is used to solve the problem, resulting in iterative algorithm with well-conditioned subproblems. Although wavefield reconstruction is the most challenging part of the iteration, sparse linear algebra techniques can be used for moderate-sized problems and frequency domain formulations. The Hessian matrix is blocky with diagonal blocks, making model updates fast. Gradient ascent is used to update Lagrange multipliers by summing PDE violations. Various numerical examples are used to investigate algorithmic components, including model parameterizations, physical model constraints, the role of the Hessian matrix in suppressing interparameter cross-talk, computational efficiency with the source sketching method, and the effect of noise and near surface effects.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)
Least-squares reverse-time migration of simultaneous source with deep-learning-based denoising
Wu, Bo (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Yao, Gang (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Ma, Xiao (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Chen, Hanming (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Wu, Di (China University of Petroleum (Beijing), China University of Petroleum (Beijing), China University of Petroleum (Beijing)) | Cao, Jingjie (Hebei Geo University)
Least-squares reverse-time migration (LSRTM) is currently one of the most advanced migration imaging techniques in the field of geophysics. It utilizes least-squares inversion to fit the observed data, resulting in high-resolution imaging results with more accurate amplitudes and better illumination compensation than conventional reverse-time migration (RTM). However, noise in the observed data and the Born approximation forward operator can result in high-wavenumber artifacts in the final imaging results. Moreover, iteratively solving LSRTM leads to one or two orders of computational cost higher than conventional RTM, making it challenging to apply extensively in industrial applications. Simultaneous source acquisition technology can reduce the computational cost of LSRTM by reducing the number of wavefield simulations. However, this technique can also cause high-wavenumber crosstalk artifacts in the migration results. To effectively remove the high-wavenumber artifacts caused by these mentioned issues, in this paper, we combine simultaneous source and deep-learning to speed up LSRTM, as well as, to suppress high-wavenumber artifacts. A deep-residual neural network (DR-Unet) is trained with synthetic samples, which are generated by adding field noise to synthesized noise-free migration images. Then, the trained DR-Unet is applied on the gradient of LSRTM to remove high-wavenumber artifacts in each iteration. Compared to directly applying DR-Unet denoising to LSRTM results, embedding DR-Unet denoising into the inversion process can better preserve weak reflectors and improve denoising effects. Finally, we tested the proposed LSRTM method on two synthetic datasets and a land dataset. The tests demonstrate that the proposed method can effectively remove high-wavenumber artifacts, improve imaging results, and accelerate convergence speed.
In simultaneous-shot full-waveform inversion (FWI), the re-started L-BFGS algorithm can be used to suppress crosstalk, but crosstalk cannot be completely eliminated from the inversion results. To further solve the crosstalk problem caused by the interference among individual shots in simultaneous-shot FWI, an adaptive structure-based smoothing is applied to the FWI with the re-started L-BFGS algorithm. The structure-based smoothing can mitigate the crosstalk by highlighting structure boundaries. To perform structure-based smoothing, an implicit anisotropic diffusion equation is solved. We carry out a multiscale inversion strategy. As the FWI results in the low-frequency band contain less structural information and more crosstalk, structure-based smoothing is applied to the frequency band at frequencies higher than the peak frequency to prevent negative effects from the model structure in the low-frequency band. The estimated structural information is iteratively updated during the inversion process. Furthermore, the structure-based smoothing is only added to the iterations with invariant encoding to reduce over-smoothing. The invariant encoding means that the shot encoding remains unchanged between iterations. Numerical experiments with an overthrust model indicate that the proposed adaptive structure-based FWI method can effectively suppress artifacts and provide a high-resolution inversion result, even when the encoded data is contaminated with strong noise. Another advantage of the adaptive structure-based FWI method is that no seismic migration needs to be performed, which makes it more efficient for real data.
- Geophysics > Seismic Surveying > Seismic Processing (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling > Seismic Inversion (1.00)