Energies

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17 pages, 7906 KiB

Open AccessArticle

An Intelligent Recognition Method for Low-Grade Fault Based on Attention Mechanism and Encoder–Decoder Network Structure

by Yujie Zhang, Dongdong Wang, Renwei Ding, Jing Yang, Lihong Zhao, Shuo Zhao, Minghao Cai and Tianjiao Han

Energies 2022, 15(21), 8098; https://doi.org/10.3390/en15218098 - 31 Oct 2022

Cited by 2 | Viewed by 1189

Abstract

Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample [...] Read more.

Low-grade faults play an important role in controlling oil and gas accumulations, but their fault throw is small and difficult to identify. Traditional low-grade fault recognition methods are time-consuming and inaccurate. Therefore, this study proposes a combination of a simulated low-grade fault sample set and a self-constructed convolutional neural network to recognize low-grade faults. We used Wu’s method to generate 500 pairs of low-grade fault samples to provide the data for deep learning. By combining the attention mechanism with UNet, an SE-UNet with efficient allocation of limited attention resources was constructed, which can select the features that are more critical to the current task objective from ample feature information, thus improving the expression ability of the network. The network model is applied to real data, and the results show that the SE-UNet model has better generalization ability and can better recognize low-grade and more continuous faults. Compared with the original UNet model, the SE-UNet model is more accurate and has more advantages in recognizing low-grade faults. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques in Oil and Gas Exploration and Development)

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17 pages, 7437 KiB

Open AccessArticle

Eliminate Time Dispersion of Seismic Wavefield Simulation with Semi-Supervised Deep Learning

by Yang Han, Bo Wu, Gang Yao, Xiao Ma and Di Wu

Energies 2022, 15(20), 7701; https://doi.org/10.3390/en15207701 - 18 Oct 2022

Cited by 3 | Viewed by 1337

Abstract

Finite-difference methods are the most widely used methods for seismic wavefield simulation. However, numerical dispersion is the main issue hindering accurate simulation. In the case where the finite-difference scheme is known, the time dispersion can be predicted mathematically and, thus, can be eliminated. [...] Read more.

Finite-difference methods are the most widely used methods for seismic wavefield simulation. However, numerical dispersion is the main issue hindering accurate simulation. In the case where the finite-difference scheme is known, the time dispersion can be predicted mathematically and, thus, can be eliminated. However, when only pre-compiled software is available for wavefield simulation, which is common in practical applications, the software-used algorithm becomes a black box (unknown). Therefore, it is challenging to obtain the mathematical expression of the time dispersion, resulting in difficulty in eliminating the time dispersion. To solve this problem, we propose to use deep learning methods to eliminate time dispersion. We design a semi-supervised framework based on convolutional and recurrent neural networks for eliminating time dispersion caused by seismic wave modeling. The framework of our proposed neural network includes two main modules: Inverse Model and Forward Model, both of which have learnable parameters. The Inverse Model is used for eliminating time dispersion while the Forward Model is used for regularizing the training. Particularly, this framework includes two steps: Firstly, using the compiled modeling software to generate two data sets with large and small time steps. Secondly, we train these two modules for transformation between large time-step data (with time dispersion) and small time-step data (without time dispersion) by labeled and unlabeled data sets. In this work, the labeled data set is a paired data set with large time-step data and their corresponding small time-step data; the unlabeled data set is the large time-step data that need time-dispersion elimination. We use the unlabeled data set to guide the network. In this learning framework, re-training is required whenever the modeling algorithms, time interval, or frequency band is changed. Hence, we propose a transfer learning training method to extend from the trained model to another model, which reduces the computational cost caused by re-training. This minor drawback is offset overwhelmingly by the modeling efficiency gain with large time steps in large-scale production. Tests on two models confirm the effectiveness of the proposed method. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques in Oil and Gas Exploration and Development)

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20 pages, 11763 KiB

Open AccessFeature PaperArticle

A Novel Method of Deep Learning for Shear Velocity Prediction in a Tight Sandstone Reservoir

by Ren Jiang, Zhifeng Ji, Wuling Mo, Suhua Wang, Mingjun Zhang, Wei Yin, Zhen Wang, Yaping Lin, Xueke Wang and Umar Ashraf

Energies 2022, 15(19), 7016; https://doi.org/10.3390/en15197016 - 24 Sep 2022

Cited by 10 | Viewed by 1523

Abstract

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. [...] Read more.

Shear velocity is an important parameter in pre-stack seismic reservoir description. However, in the real study, the high cost of array acoustic logging leads to lacking a shear velocity curve. Thus, it is crucial to use conventional well-logging data to predict shear velocity. The shear velocity prediction methods mainly include empirical formulas and theoretical rock physics models. When using the empirical formula method, calibration should be performed to fit the local data, and its accuracy is low. When using rock physics modeling, many parameters about the pure mineral must be optimized simultaneously. We present a deep learning method to predict shear velocity from several conventional logging curves in tight sandstone of the Sichuan Basin. The XGBoost algorithm has been used to automatically select the feature curves as the model’s input after quality control and cleaning of the input data. Then, we construct a deep-feed neuro network model (DFNN) and decompose the whole model training process into detailed steps. During the training process, parallel training and testing methods were used to control the reliability of the trained model. It was found that the prediction accuracy is higher than the empirical formula and the rock physics modeling method by well validation. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques in Oil and Gas Exploration and Development)

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14 pages, 3681 KiB

Open AccessArticle

Imaging Domain Seismic Denoising Based on Conditional Generative Adversarial Networks (CGANs)

by Hao Zhang and Wenlei Wang

Energies 2022, 15(18), 6569; https://doi.org/10.3390/en15186569 - 8 Sep 2022

Cited by 3 | Viewed by 1390

Abstract

A high-resolution seismic image is the key factor for helping geophysicists and geologists to recognize the geological structures below the subsurface. More and more complex geology has challenged traditional techniques and resulted in a need for more powerful denoising methodologies. The deep learning [...] Read more.

A high-resolution seismic image is the key factor for helping geophysicists and geologists to recognize the geological structures below the subsurface. More and more complex geology has challenged traditional techniques and resulted in a need for more powerful denoising methodologies. The deep learning technique has shown its effectiveness in many different types of tasks. In this work, we used a conditional generative adversarial network (CGAN), which is a special type of deep neural network, to conduct the seismic image denoising process. We considered the denoising task as an image-to-image translation problem, which transfers a raw seismic image with multiple types of noise into a reflectivity-like image without noise. We used several seismic models with complex geology to train the CGAN. In this experiment, the CGAN’s performance was promising. The trained CGAN could maintain the structure of the image undistorted while suppressing multiple types of noise. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques in Oil and Gas Exploration and Development)

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13 pages, 2452 KiB

Open AccessArticle

An Optimized Gradient Boosting Model by Genetic Algorithm for Forecasting Crude Oil Production

by Eman H. Alkhammash

Energies 2022, 15(17), 6416; https://doi.org/10.3390/en15176416 - 2 Sep 2022

Cited by 2 | Viewed by 1589

Abstract

The forecasting of crude oil production is essential to economic plans and decision-making in the oil and gas industry. Several techniques have been applied to forecast crude oil production. Artificial Intelligence (AI)-based techniques are promising that have been applied successfully to several sectors [...] Read more.

The forecasting of crude oil production is essential to economic plans and decision-making in the oil and gas industry. Several techniques have been applied to forecast crude oil production. Artificial Intelligence (AI)-based techniques are promising that have been applied successfully to several sectors and are capable of being applied to different stages of oil exploration and production. However, there is still more work to be done in the oil sector. This paper proposes an optimized gradient boosting (GB) model by genetic algorithm (GA) called GA-GB for forecasting crude oil production. The proposed optimized model was applied to forecast crude oil in several countries, including the top producers and others with less production. The GA-GB model of crude oil forecasting was successfully developed, trained, and tested to provide excellent forecasting of crude oil production. The proposed GA-GB model has been applied to forecast crude oil production and has also been applied to oil price and oil demand, and the experiment of the proposed optimized model shows good results. In the experiment, three different actual datasets are used: crude oil production (OProd), crude oil price (OPrice), and oil demand (OD) acquired from various sources. The GA-GB model outperforms five regression models, including the Bagging regressor, KNN regressor, MLP regressor, RF regressor, and Lasso regressor. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques in Oil and Gas Exploration and Development)

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16 pages, 6800 KiB

Open AccessArticle

Stylization of a Seismic Image Profile Based on a Convolutional Neural Network

by Huiting Hu, Wenxin Lian, Rui Su, Chongyu Ren and Juan Zhang

Energies 2022, 15(16), 6039; https://doi.org/10.3390/en15166039 - 20 Aug 2022

Viewed by 1277

Abstract

Seismic data are widely used in oil, gas, and other kinds of mineral exploration and development. However, due to low artificial interpretation accuracy and small sample sizes, seismic data may not meet the needs of convolutional neural network training. There are major differences [...] Read more.

Seismic data are widely used in oil, gas, and other kinds of mineral exploration and development. However, due to low artificial interpretation accuracy and small sample sizes, seismic data may not meet the needs of convolutional neural network training. There are major differences between optical image and seismic data, making it difficult for a model to learn seismic data characteristics. Therefore, a style transfer network is necessary to make the styles of optical image and seismic data more similar. Since the stylization effect of a seismic section is similar to that of most art styles, based on an in-depth study of image style transfer, this paper compared the effects of various style transfer models, and selected a Laplacian pyramid network to carry out a study of seismic section stylization. It transmits low-resolution global style patterns through a drafting network, revises high-resolution local details through correction networks, and aggregates all pyramid layers to output final stylized images of seismic profiles. Experiments show that this method can effectively convey the whole style pattern without losing the original image content. This style transfer method, based on the Laplacian pyramid network, provides theoretical guidance for the fast and objective application of the model to seismic data features. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques in Oil and Gas Exploration and Development)

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21 pages, 23655 KiB

Open AccessArticle

by Peinan Bao, Ying Shi, Weihong Wang, Jialiang Xu and Xuebao Guo

Energies 2022, 15(11), 3883; https://doi.org/10.3390/en15113883 - 25 May 2022

Cited by 2 | Viewed by 2011

Abstract

Multiple elimination has always been a key, challenge, and hotspot in the field of hydrocarbon exploration. However, each multiple elimination method comes with one or more limitations at present. The efficiency and success of each approach strongly depend on their corresponding prior assumptions, [...] Read more.

Multiple elimination has always been a key, challenge, and hotspot in the field of hydrocarbon exploration. However, each multiple elimination method comes with one or more limitations at present. The efficiency and success of each approach strongly depend on their corresponding prior assumptions, in particular for seismic data acquired from complex geological regions. The multiple elimination approach using deep learning encodes the input seismic data to multiple levels of abstraction and decodes those levels to reconstruct the primaries without multiples. In this study, we employ a classic convolution neural network (CNN) with a U-shaped architecture which uses extremely few seismic data for end-to-end training, strongly increasing the neural network speed. Then, we apply the trained network to predict all seismic data, which solves the problem of difficult elimination of global multiples, avoids the regularization of seismic data, and reduces massive amounts of calculation in traditional methods. Several synthetic and field experiments are conducted to validate the advantages of the trained network model. The results indicate that the model has the powerful generalization ability and high calculation efficiency for removing surface-related multiples and internal multiples effectively. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques in Oil and Gas Exploration and Development)

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15 pages, 11427 KiB

Open AccessArticle

Research on Noise Suppression Technology of Marine Optical Fiber Towed Streamer Seismic Data Based on ResUNet

by Hongfei Qian, Xiangchun Wang, Xuelei Chen and Zhu Yang

Energies 2022, 15(9), 3362; https://doi.org/10.3390/en15093362 - 5 May 2022

Cited by 2 | Viewed by 1450

Abstract

Optical fiber seismic exploration technology has been widely used in marine oil and gas hydrate exploration due to its wide frequency band and high sensitivity. However, there are more types of noise in the collected data by optical fiber hydrophone than by a [...] Read more.

Optical fiber seismic exploration technology has been widely used in marine oil and gas hydrate exploration due to its wide frequency band and high sensitivity. However, there are more types of noise in the collected data by optical fiber hydrophone than by a conventional piezoelectric seismic exploration system. Considering that the conventional denoising method is time-consuming, this paper proposes a convolutional neural network (CNN) and a ResUNet network based on deep learning to suppress the noises. ResUNet is improved on the basis of CNN; it is composed of a feature extraction part, a feature reconstruction part and a residual block. Both CNN and ResUNet networks achieved obvious denoising effects on optical fiber towed streamer seismic data and improved the signal-to-noise ratio of data effectively. The ResUNet network has better denoising effects than CNN, even better than conventional denoising methods. The ResUNet network can solve the problem of gradient disappearance caused by network deepening; it recovered edge data well, and it has high efficiency compared with conventional denoising methods. Two evaluation indexes, relative error (RE) and similarity structure degree (SSIM), were introduced to compare the denoising effect of the ResUNet network with that of CNN. The experimental results showed that the performance of the ResUNet network in these two aspects is better than that of CNN. Full article

(This article belongs to the Special Issue Artificial Intelligence Techniques in Oil and Gas Exploration and Development)

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