A Grey Fuzzy Comprehensive Model for Evaluation of Geological Structure Complexity

Yun-xia, Zhou; Yong-zhang, Zhou; Xie, Shuyun; Cao, Daiyong; Xiang-rong, Qiu

doi:10.1016/s1002-0705(08)60078-3

Cited by 4 publications

(3 citation statements)

References 6 publications

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“…This method is based on fuzzy theory to study the random phenomena of real world, through fuzzy membership functions and fuzzy statistical methods to quantify the qualitative evaluation indicators, and further applied on analyze and decision making for most of the uncertainty situation to achieve effective combination of qualitative and quantitative methods. One of the difficulties of this method is how to scientifically and objectively consolidate a multi-index problem into a single index style, to achieve a comprehensive assessment in one-dimensional space [6] . Its essence is to determine the weight of evaluation index.…”

Section: B Ahp-fce Methods Selectementioning

confidence: 99%

Comprehensive evaluation study for urban emergency rescue capability

2011

2011 2nd IEEE International Conference on Emergency Management and Management Sciences

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This article first takes full accounts of the properties of urban emergency rescue capability, and the features of each evaluation method, then describes the procedures and principles for evaluate urban emergency rescue capabilities with the combination of AHP(Analytic Hierarch Process) and FCE(Fuzzy Comprehensive Evaluation) method. At last, with case study, establishing the urban emergency rescue capability evaluation system, using of AHP to determine weights, and take southern city Z as an example for evaluation with FCE method, and provided an effective reference model for evaluation of urban emergency rescue capability.

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Section: B Ahp-fce Methods Selectementioning

confidence: 99%

Comprehensive evaluation study for urban emergency rescue capability

2011

2011 2nd IEEE International Conference on Emergency Management and Management Sciences

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“…Fields are described as "structurally complex" but there is no generally accepted scalar metric to describe what that means. There have been previous attempts to quantify the impact of different structural complexities on production behavior (e.g., Manzocchi, Carter, et al, 2008;Manzocchi, John Matthews, et al, 2008;Tveranger et al, 2008;Yunxia et al, 2008). Manzocchi, Carter, et al (2008), Manzocchi, John Matthews, et al, 2008) applied a matrix based on fault density and fault transmissibility to describe structural complexity but also recognized that fault orientation and number of fault populations were also key factors.…”

Section: Structural Complexity (Scaled From 0 To 5)mentioning

confidence: 99%

“…Fields are described as “structurally complex” but there is no generally accepted scalar metric to describe what that means. There have been previous attempts to quantify the impact of different structural complexities on production behavior (e.g., Manzocchi, Carter, et al, 2008; Manzocchi, John Matthews, et al, 2008; Tveranger et al, 2008; Yunxia et al, 2008).…”

Section: Stratigraphic and Structural Controlling Parametersmentioning

confidence: 99%

Spatial and temporal distribution of hydrocarbon production on the UK continental shelf

Osah,

Howell

2023

Resource Geology

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Hydrocarbon production from oil and gas fields is controlled by a variety of interconnected factors with a hierarchy of significance that is, for the most part, difficult to untangle. This article documents and investigates the spatial and temporal distribution of key hydrocarbon field parameters on the UK Continental Shelf. Data have been compiled from publicly available sources for 424 fields. Variables are considered as “descriptive parameters,” “control parameters” and “outcomes”. Descriptive parameters are metadata such as field name, location, etc. Control Parameters include depositional environment, present depth of burial, porosity, permeability, reservoir formation pressure, reservoir temperature, average net‐to‐gross, number of fault populations, hydrocarbon API, field area, bulk rock volume, well density, number of wells (production and injection), well spacing, gas oil ratio, reservoir thickness, fluid saturation, compartmentalization (quantitated by number of observable non‐communicating fault compartments), structural complexity (scaled from 0 to 5), field production strategy, trap type and stratigraphic heterogeneity. Outcomes are used to assess field performance and include final recovery factor (estimated), maximum production rate, and cumulative monthly production. Analysis of the database illustrates a number of empirical observations regarding hydrocarbon production on the UKCS. The Jurassic plays have been the most successful in the region in terms of total volumes produced while the Permian reservoirs of the SNS account for the majority of the gas. Most of the UKCS reservoirs record top depths between 2000 and 4500 m with good reservoir quality. The best reservoir quality is observed in reservoirs that were deposited within deep marine systems. The largest hydrocarbon reserves are found in the Northern North Sea basin in these deep marine (as well as paralic and shallow marine) reservoirs. Using the data from this article and affiliated data, potential exists for extracting insight beyond spatio‐temporal distributions.

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Relationship Analysis of the Degree of Fault Complexity and the Water Irruption Rate, Based on Fractal Theory

Wang

et al. 2015

Mine Water Environ

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Fault structure significantly affects the likelihood of mine water inrush events. In this study, fractal theory was applied to the distribution of fault structures in a deep mining area of the No. 2 mine, Pingdingshan City, China. The results revealed a fractal dimension ranging from 0.602 to 1.672 for fault structures in 35 planes. Using the fractal dimension as a fundamental characteristic, the complexity of fault structures was quantified and categorized into five levels, with the overall complexity of the fault network in the study area classified as moderate. Nine mine water inrush events were used to establish a relationship between the fault fractal dimension and the water irruption rate, which was found to increase with the fractal dimension. Finally, differences in fault fractal dimension and fault complexity level are discussed and analyzed.

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A Grey Fuzzy Comprehensive Model for Evaluation of Geological Structure Complexity

Cited by 4 publications

References 6 publications

Comprehensive evaluation study for urban emergency rescue capability

Comprehensive evaluation study for urban emergency rescue capability

Spatial and temporal distribution of hydrocarbon production on the UK continental shelf

Relationship Analysis of the Degree of Fault Complexity and the Water Irruption Rate, Based on Fractal Theory

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