A hybrid framework for reservoir characterization using fuzzy ranking and an artificial neural network

Wang, Baijie; Wang, Xin; Chen, Zhangxin

doi:10.1016/j.cageo.2013.03.016

Cited by 39 publications

(13 citation statements)

References 16 publications

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“…All the candidate values were first normalized by using their maximums and minimums; and, fuzzy membership functions μ ik (x i ) were then created in Gaussian forms for every data point in each x i À y space as follows (Wang et al, 2013):…”

Section: Fuzzy Rankingmentioning

confidence: 99%

A data mining approach to finding relationships between reservoir properties and oil production for CHOPS

Cai

Wang

et al. 2014

Computers & Geosciences

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Section: Fuzzy Rankingmentioning

confidence: 99%

A data mining approach to finding relationships between reservoir properties and oil production for CHOPS

Cai

Wang

et al. 2014

Computers & Geosciences

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“…Artificial intelligence (AI) technologies have been gaining increasingly more attention for their fast response speeds and vigorous generalization capabilities. The AI technology exhibits promising potentials to assist and improve the conventional reservoir engineering approaches in a large spectrum of reservoir engineering problems [1][2][3][4]. Advanced machine-learning algorithms such as fuzzy logic (FL), artificial neural networks (ANN), support vector machines (SVM), response surface model (RSM) are employed by numerous studies as regression and classification tools [5][6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence Applications in Reservoir Engineering: A Status Check

Ertekin

Sun

2019

Energies

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This article provides a comprehensive review of the state-of-art in the area of artificial intelligence applications to solve reservoir engineering problems. Research works including proxy model development, artificial-intelligence-assisted history-matching, project design, and optimization, etc. are presented to demonstrate the robustness of the intelligence systems. The successes of the developments prove the advantages of the AI approaches in terms of high computational efficacy and strong learning capabilities. Thus, the implementation of intelligence models enables reservoir engineers to accomplish many challenging and time-intensive works more effectively. However, it is not yet astute to completely replace the conventional reservoir engineering models with intelligent systems, since the defects of the technology cannot be ignored. The trend of research and industrial practices of reservoir engineering area would be establishing a hand-shaking protocol between the conventional modeling and the intelligent systems. Taking advantages of both methods, more robust solutions could be obtained with significantly less computational overheads.

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“…In the latter, GIS are used as platforms, from which spatial analysis functions are used, and with which mathematical models are integrated to complete reservoir evaluation. In terms of mathematical models, fuzzy mathematics (Schrader, Balch, & Ruan, ; Taheri, ; Zoveidavianpoor, Samsuri, & Shadizadeh, ), gray clustering (Denney, ), artificial neural networks (Ahmadi, Saemi, & Asghari, ; Elshafei & Hamada, ; Wang, Wang, & Chen, ), gray multivariate correlation analysis (Naseri, Khishvand, & Sheikhloo, ; Shi, ), rough sets (Wu, Li, Dai, & Feng, ), support vector machines (Ahmadi, ), and geostatistical methods (Tamaki, Suzuki, Fujii, & Sato, ) are studied and/or integrated with GIS.…”

Section: Introductionmentioning

confidence: 99%

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

Chen

Wang

Jun-wen³

2018

Transactions in GIS

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Delineating the distribution of oil and natural gas resources is a prerequisite of exploitation. The delineation methods usually include conventional techniques of reservoir evaluation and mathematical models. The conventional reservoir evaluation results generally depend on the experts' knowledge and experience in the field. The mathematical methods mostly require accurate models to be proposed. Considering spatial relationships and characteristics of geological reservoir problems, including nonlinearity, complexity and uncertainty, a novel model called geospatial case-based reasoning for oil-gas reservoir evaluation was proposed in this article. The key components of the new model, including: (1) the joint representation of spatial relationship and attribute features; (2) the model of spatial relationship and attribute similarity joint reasoning; and (3) the methods of establishing weights for the spatial relationship and attribute features, are completely constructed. A case study of the proposed model for gas reservoir evaluation was carried out. Compared with the backpropagation artificial neural network (BP-ANN) and the geological empirical evaluation (GEE) methods, the model presented in this article performs6.38% and 46.81% better than BP-ANN and GEE, respectively. Furthermore, its execution is simpler, more convenient, and importantly, its utilization hardly requires any professional knowledge of the field.

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A hybrid framework for reservoir characterization using fuzzy ranking and an artificial neural network

Cited by 39 publications

References 16 publications

A data mining approach to finding relationships between reservoir properties and oil production for CHOPS

A data mining approach to finding relationships between reservoir properties and oil production for CHOPS

Artificial Intelligence Applications in Reservoir Engineering: A Status Check

A geospatial case‐based reasoning model for oil–gas reservoir evaluation

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