Development of a Full-Field Dynamic Model to Support Pressure Maintenance Projects in the World's Largest Clastic Oil Field, The Greater Burgan Field, Kuwait

This article argues the following five claims: 1. Plato’s description of the origins of cosmos in the Timaeus is not a myth, nor something unlikely: when he called it an eikos mythos or eikos logos, he meant a likely or trustworthy account on this very subject. 2. Among the details in this account, the following are prominent and surprising: a) the world was fashioned in time, in that precise point that was the beginning of time; b) several kinds of duration can be distinguished in cosmology (mainly eternity, sempiternity, perpetuity and time); and c) space is an entity characterized by movement and tension. 3. In the Statesman, Plato repeats much the same thing, adding this time the strange notion that the universe’s circular movement is periodically reversed. 4. In spite of the important differences in detail, there is a striking similarity between Plato’s account of the origins of the world and the explanation adopted by much of modern cosmology. 5. What Plato shares with so many instances of recent thought is here termed “cosmological imaginativity”. A first section of the paper deals exclusively with the Timaeus. Claims 1 and 2a are supported by a revision of the meanings of mythos and logos, followed by brief reference and discussion of the argument at Timaeus 27d, leading to the conclusion that Plato affirms that the ever-changing world has indeed had a beginning in time. Claim 2b describes five different types of duration, corresponding to Forms, the Demiurge, Space, the [empirical] world and its contents, physical objects. The second section is concerned with the myth in the Statesman, discussing it as a parallel and describing its peculiar turn to the Timaeus’ cosmology and cosmogony, a complex spheric and dynamic model. After digressing into some important ideas in modern cosmology, touching especially on affinities of some of Einstein’s ideas with of Plato’s own, the paper closes with a discussion of cosmological imaginativity, oriented to recover and recognize fully Plato’s greatness as a cosmologist.

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Extracting full-field dynamic strain response of a rotating wind turbine using photogrammetry

10.1117/12.2084633 ◽

2015 ◽

Cited By ~ 1

Author(s):

Javad Baqersad ◽

Peyman Poozesh ◽

Christopher Niezrecki ◽

Peter Avitabile

Keyword(s):

Wind Turbine ◽

Dynamic Strain ◽

Strain Response ◽

Full Field ◽

Field Dynamic

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Parabolic trough collector field dynamic model: Validation, energetic and exergetic analyses

Applied Thermal Engineering ◽

10.1016/j.applthermaleng.2018.11.093 ◽

2019 ◽

Vol 148 ◽

pp. 777-786 ◽

Cited By ~ 8

Author(s):

Jose A. Carballo ◽

Javier Bonilla ◽

Manuel Berenguel ◽

Patricia Palenzuela

Keyword(s):

Dynamic Model ◽

Model Validation ◽

Parabolic Trough ◽

Parabolic Trough Collector ◽

Field Dynamic

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A Dynamic Model of the World Copper Industry (Un modele dynamique de l'industrie mondiale du cuivre) (Un modelo dinamico de la industria mundial del cobre)

International Monetary Fund Staff Papers ◽

10.2307/3866605 ◽

1978 ◽

Vol 25 (4) ◽

pp. 779 ◽

Cited By ~ 9

Author(s):

Denis Richard

Keyword(s):

Dynamic Model ◽

Copper Industry ◽

Modèle Dynamique ◽

The World

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Applications of Artificial Neural Networks for Seismic Facies Classification

Journal of Petroleum Technology ◽

10.2118/0221-0068-jpt ◽

2021 ◽

Vol 73 (02) ◽

pp. 68-69

Author(s):

Chris Carpenter

Keyword(s):

Neural Network ◽

Seismic Data ◽

Reservoir Characterization ◽

Probabilistic Neural Network ◽

Oil Field ◽

Seismic Facies ◽

Data Set ◽

Full Field ◽

Facies Classification

This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper SPE 200577, “Applications of Artificial Neural Networks for Seismic Facies Classification: A Case Study From the Mid-Cretaceous Reservoir in a Supergiant Oil Field,” by Ali Al-Ali, Karl Stephen, SPE, and Asghar Shams, Heriot-Watt University, prepared for the 2020 SPE Europec featured at the 82nd EAGE Conference and Exhibition, originally scheduled to be held in Amsterdam, 1-3 December. The paper has not been peer reviewed. Facies classification using data from sources such as wells and outcrops cannot capture all reservoir characterization in the interwell region. Therefore, as an alternative approach, seismic facies classification schemes are applied to reduce the uncertainties in the reservoir model. In this study, a machine-learning neural network was introduced to predict the lithology required for building a full-field Earth model for carbonate reservoirs in southern Iraq. The work and the methodology provide a significant improvement in facies classification and reveal the capability of a probabilistic neural network technique. Introduction The use of machine learning in seismic facies classification has increased gradually during the past decade in the interpretation of 3D and 4D seismic volumes and reservoir characterization work flows. The complete paper provides a literature review regarding this topic. Previously, seismic reservoir characterization has revealed the heterogeneity of the Mishrif reservoir and its distribution in terms of the pore system and the structural model. However, the main objective of this work is to classify and predict the heterogeneous facies of the carbonate Mishrif reservoir in a giant oil field using a multilayer feed-forward network (MLFN) and a probabilistic neural network (PNN) in nonlinear facies classification techniques. A related objective was to find any domain-specific causal relationships among input and output variables. These two methods have been applied to classify and predict the presence of different facies in Mishrif reservoir rock types. Case Study Reservoir and Data Set Description. The West Qurna field is a giant, multibillion-barrel oil field in the southern Mesopotamian Basin with multiple carbonate and clastic reservoirs. The overall structure of the field is a north/south trending anticline steep on the western flank and gentle on the eastern flank. Many producing reservoirs developed in this oil field; however, the Mid- Cretaceous Mishrif reservoir is the main producing reservoir. The reservoir consists of thick carbonate strata (roughly 250 m) deposited on a shallow water platform adjacent to more-distal, deeper-water nonreservoir carbonate facies developing into three stratigraphic sequence units in the second order. Mishrif facies are characterized by a porosity greater than 20% and large permeability contrast from grainstones to microporosity (10-1000 md). The first full-field 3D seismic data set was achieved over 500 km2 during 2012 and 2013 in order to plan the development of all field reservoirs. A de-tailed description of the reservoir has been determined from well logs and core and seismic data. This study is mainly based on facies log (22 wells) and high-resolution 3D seismic volume to generate seismic attributes as the input data for the training of the neural network model. The model is used to evaluate lithofacies in wells without core data but with appropriate facies logs. Also, testing was carried out in parallel with the core data to verify the results of facies classification.

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Enabling full field physics based OPC via dynamic model generation

10.1117/12.2261222 ◽

2017 ◽

Cited By ~ 1

Author(s):

Michael Lam ◽

Chris Clifford ◽

Ananthan Raghunathan ◽

Germain Fenger ◽

Kostas Adam

Keyword(s):

Dynamic Model ◽

Model Generation ◽

Full Field

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