Unsupervised-learning based self-organizing neural network using multi-component seismic data: Application to Xujiahe tight-sand gas reservoir in China

Zhang, Kai; Lin, Niantian; Tian, Gaopeng; Yang, Jiuqiang; Wang, Deying; Jin, Zhiwei

doi:10.1016/j.petrol.2021.109964

Cited by 17 publications

(3 citation statements)

References 36 publications

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“…Unsupervised learning methods are data-driven approaches that reveal internal data characteristics and laws through the learning of unlabeled data sets. These approaches excavate the internal features of the data in greater depth, making it more conducive to extracting discriminative features [33,34].…”

Section: Introductionmentioning

confidence: 99%

A Topic Modeling Approach to Discover the Global and Local Subjects in Membrane Distillation Separation Process

Aytaç,

Khayet

2023

Separations

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Membrane distillation (MD) is proposed as an environmentally friendly technology of emerging interest able to aid in the resolution of the worldwide water issue and brine processing by producing distilled water and treating high-saline solutions up to their saturation with a view toward reaching zero liquid discharge (ZLD) at relatively low temperature requirements and a low operating hydrostatic pressure. Topic modeling (TM), which is a Machine Learning (ML) method combined with Natural Language Processing (NLP), is a customizable approach that is ideal for researching massive datasets with unknown themes. In this study, we used BERTopic, a new cutting-edge Python library for topic modeling, to explore the global and local themes in the MD separation literature. By using the BERTopic model, the words describing the collected dataset were detected together with over- and underexplored research topics to guide MD researchers in planning their future works. The results indicated that two global themes are widely discussed and are relevant to MD scientists abroad. In brief, these topics are permeate flux, heat-energy recovery, surface modification, and polyvinylidene fluoride hydrophobic membranes. BERTopic discovered 62 local concepts. The most researched local topics were solar applications, membrane scaling, and electrospun membranes, while the least investigated were boron removal, dairy effluent applications, and nickel wastewater treatment. In addition, the topics were illustrated in a 2D plane to better understand the obtained results.

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Section: Introductionmentioning

confidence: 99%

A Topic Modeling Approach to Discover the Global and Local Subjects in Membrane Distillation Separation Process

Aytaç,

Khayet

2023

Separations

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“…The emergence of the simplified formula laid the foundation for the development of the joint P‐ and S‐wave inversion theory. With the development of multi‐wave multicomponent seismic exploration technology, multi‐wave joint inversion has become an important means to reduce inversion multi‐solution (Chen & Innanen, 2018; Pan et al., 2017) and improve reservoir prediction accuracy (Dai et al., 2021; Li et al., 2019; Zhang et al., 2022). SH–SH‐wave seismic data are suitable for high‐precision imaging and reservoir prediction in unconsolidated sandstone gas cloud areas with their good imaging ability of gas cloud areas and better recovery of gas reservoir structural features (Dai et al., 2022; Deng et al., 2019).…”

Section: Introductionmentioning

confidence: 99%

Seismic fluid identification method based on the joint PP‐ and SH–SH‐wave stochastic inversion

Lin,

Zhang,

Wang

et al. 2023

Geophysical Prospecting

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Pre‐stack seismic inversion is an effective method for elastic parameter inversion using seismic data, which facilitates seismic fluid identification. However, pure PP‐wave inversion has issues of strong multi‐solution and limited prediction accuracy. Therefore, we propose a seismic fluid identification approach based on the joint PP wave and SH‐SH wave stochastic inversion. Firstly, the linearized SH‐SH wave amplitude variation with offset (AVO) approximation parameterized by shear modulus and density is derived. Numerical simulations demonstrate that the SH‐SH wave AVO approximation has a good accuracy. Reflection coefficient contribution analysis indicates that the new formulation has better parameter sensitivity to shear modulus and density than the PP wave AVO approximation derived by Russell, which helps to improve the inversion of shear modulus and density. On this basis, we construct a joint inversion equation of PP and SH‐SH waves for Russell fluid indicator, shear modulus, and density and present a novel joint stochastic inversion method based on the ensemble smoother with multiple data assimilation (ES‐MDA). Stanford VI‐E model tests reveal that the Russell fluid indicator factor, shear modulus, and density obtained from the joint PP wave and SH‐SH wave inversion have higher identification accuracy and smaller relative errors than those from pure PP wave inversion. Furthermore, field data tests indicate that this method has practical applicability in seismic fluid identification.This article is protected by copyright. All rights reserved

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“…The combined application of multiple logs has improved the accuracy and precision compared to direct observation methods, but for areas with limited wells, only mathematical algorithms can be used to interpolate and predict the planimetric distribution of basement buried-hill reservoirs (Ye, 2019). The use of seismic detection technology for fracture prediction began later; however, because seismic data cover a much wider space than drilling data, have a greater range of applications and are more practical, they are more generally used (Gong et al, 2013;Zhang et al, 2022). At present, the main techniques for the fracture prediction of reservoirs using seismic data are shear wave exploration, multiwave and multicomponent detection, and threedimensional p-wave fracture detection (Pu and Qing, 2008).…”

Section: Introductionmentioning

confidence: 99%

Predicting Granitic Buried-Hill Reservoirs Using Seismic Reflection data—A Case Study From the Bongor Basin, Southwestern Chad

Wang

Zhou

et al. 2022

Front. Earth Sci.

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Predicting the distribution of fractured and dissolved reservoirs in granitic buried hills is a prerequisite for finding high-quality hydrocarbon reservoirs and improving the success rate of exploration activities. Granitic buried-hill reservoirs in the Bongor Basin are characterized by strong heterogeneity and poor lateral continuity. Through the analysis of seismic, core, logging and dynamic production data, this study predicts the distribution of fractured reservoirs and fractured-cave reservoirs in the granitic buried hills in the BC Block of the Bongor Basin. In this study, we predict fractured reservoirs using coherence cubes and linearly enhanced attributes and identify fractured-cave reservoirs with single-frequency attribute bodies. Integrated predictions of fractured reservoirs and fractured-cave reservoirs are conducted using attribute fusion techniques. With these methods, good results have been achieved in reservoir prediction in BC buried hills. Furthermore, this study summarizes a set of granitic buried-hill reservoir prediction techniques for densely inverted rift basins in the Central African Rift System.

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Unsupervised-learning based self-organizing neural network using multi-component seismic data: Application to Xujiahe tight-sand gas reservoir in China

Cited by 17 publications

References 36 publications

A Topic Modeling Approach to Discover the Global and Local Subjects in Membrane Distillation Separation Process

A Topic Modeling Approach to Discover the Global and Local Subjects in Membrane Distillation Separation Process

Seismic fluid identification method based on the joint PP‐ and SH–SH‐wave stochastic inversion

Predicting Granitic Buried-Hill Reservoirs Using Seismic Reflection data—A Case Study From the Bongor Basin, Southwestern Chad

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