Reservoir Characterization Using Multi-component Seismic Data in a Novel Hybrid Model Based on Clustering and Deep Neural Network

Yang, Jiuqiang; Lin, Niantian; Zhang, Kai; Zhang, Chong; Fu, Chao; Tian, Gaopeng; Song, Changyue

doi:10.1007/s11053-021-09863-z

Cited by 30 publications

(4 citation statements)

References 38 publications

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“…Due to the advantage of automatically characterizing complex multivariate non-linear relationships, DNN is also used for acquiring reservoir gas-bearing properties. For example, a deep neural network (DNN) model with several hidden layers (Yang et al, 2021;Zhang et al, 2022) is built for gas-bearing prediction by leveraging the capability of DNN to handle an end-to-end task. The convolution neural network (CNN), one of the most promising DNN approaches for geophysics issues, has been trained for extracting oil and gas properties (Song et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Gas-bearing prediction of deep reservoir based on DNN embeddings

Cao

Liu

et al. 2023

Front. Earth Sci.

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The extraction of gas-bearing information from the deeply underground reservoir is extremely difficult due to the weak seismic response and complicated gas distribution characteristics. To predict gas-bearing reservoirs efficiently, we developed a deep neural network (DNN) embedding-based gas-bearing prediction scheme. First, the cepstrum coefficient that is sensitive to hydrocarbons is computed using the raw seismic data. A DNN model inspired by the x-vector in speech recognition is designed, comprising the long short-term memory (LSTM) networks and two fully connected (FC) networks, stacked from the bottom to the top layer. Then, the cepstrum features are fed into the DNN for training and testing, and DNN embedding is extracted from the top layers after optimized network parameters are determined. Finally, the gas-bearing probability of the reservoir is predicted by calculating the cosine distance between pairs of DNN embeddings. When applied to synthetic seismic data, the proposed method offers greater than 90% accuracy at SNR > 3 dB. Besides, the predicted result applied in deep carbonate reservoirs in China’s Sichuan Basin is in basic agreement with the actual situation, demonstrating the certain feasibility of the proposed scheme.

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Section: Introductionmentioning

confidence: 99%

Gas-bearing prediction of deep reservoir based on DNN embeddings

Cao

Liu

et al. 2023

Front. Earth Sci.

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“…In 1989, Robert proved that the continuous function existing in the closed interval can be approximately represented by BP neural network including hidden layer [10,11], so BP neural network including three layers allows the construction from arbitrary input (m-dimension) to output (n-dimension) as shown in Figure 1.…”

Section: Principle Of Bp Neural Networkmentioning

confidence: 99%

Research on System Economic Operation and Management Based on Deep Learning

Wangtao

Zheng²,

Wang

et al. 2022

Scientific Programming

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It is of great significance to accurately predict the operation of the system economy, analyze the gains and losses of macrocontrol policies, evaluate the operation quality of the economic system, and correctly formulate the future development plan and strategy. This paper introduces the deep belief network, which has attracted much attention in the field of deep learning in recent years, into the research of system economic operation and management. This method solves the problems of slow training and learning speed, easy to fall into local minima and insufficient generalization of BP artificial neural network in the research of system economic operation and management. Taking the consumer price index and total import and export volume of F Province as the research object, the experiment proves that DBN has better application in system economic operation and management than BP neural network and vector autoregressive analysis. This paper analyzes and compares the modeling performance of DBN, BP neural network, and VaR method from many aspects, such as prediction accuracy, training convergence speed, and pretraining with or without samples. Relevant empirical results show that DBN has better economic prediction performance than BP neural network and ver. On the other hand, DBN can effectively use nonstandard samples to pretrain network weight parameters. Therefore, DBN is a better operation and management modeling means of economic system, with excellent practicability and application, and is expected to be popularized and applied in the field of economic forecasting.

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“…A deep neural network (DNN) can be used to solve problems usually associated with longitudinal waves in reservoir char-acterization. Yang et al [223] used cluster analysis and DNN to optimize seismic features prone to oil and gas response. The seismic gas reservoir distribution forecasted using this method had higher accuracy and was consistent with actual drilling information.…”

Section: ) Reservoir Characterizationmentioning

confidence: 99%

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

et al. 2021

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The market volatility in the oil and gas (O&G) sector, the dwindling demand for oil due to the impact of COVID-19, and the push for alternative greener energy are driving the need for innovation and digitization in the O&G industry. This has attracted research interest from academia and the industry in the application of industry 4.0 (I4.0) technologies in the O&G sector. The application of some of these I4.0 technologies has been presented in the literature, but the domain still lacks a comprehensive survey of the application of I4.0 in the O&G upstream sector. This paper investigates the state-of-the-art efforts directed toward I4.0 technologies in the O&G upstream sector. To achieve this, first, an overview of the I4.0 is discussed followed by a systematic literature review from an integrative perspective for publications between 2012-2021 with 223 analyzed documents. The benefits and challenges of the adoption of I4.0 have been identified. Moreover, the paper adds value by proposing a framework for the implementation of I4.0 in the O&G upstream sector. Finally, future directions and research opportunities such as framework, edge computing, quantum computing, communication technologies, standardization, and innovative areas related to the implementation of I4.0 in the upstream sector are presented. The findings from this review show that I4.0 technologies are currently being explored and deployed for various aspects of the upstream sector. However, some of the I4.0 technologies like additive manufacturing and virtual reality are least explored.INDEX TERMS Artificial intelligence (AI), cyber-physical systems, digital-twin, framework, oil and gas (O&G), industry revolution 4.0 (IR 4.0), industry 4.0 (I4.0), internet of things (IoT), simulation, upstream sector

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Reservoir Characterization Using Multi-component Seismic Data in a Novel Hybrid Model Based on Clustering and Deep Neural Network

Cited by 30 publications

References 38 publications

Gas-bearing prediction of deep reservoir based on DNN embeddings

Gas-bearing prediction of deep reservoir based on DNN embeddings

Research on System Economic Operation and Management Based on Deep Learning

A Survey on Industry 4.0 for the Oil and Gas Industry: Upstream Sector

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