Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userStudying the Relationship Between hydrocarbons Saturation and Frequency Domain With Continuous Wavelet Transform (CWT)
دراسة علاقة الإشباع الهيدروكربوني مع تغيرات طيف التردد باستخدام تحويل النبضة المستمر
1356
0 40
0
(
0
)
Added by
Damascus University ورقة بحثية
Publication date
2010
fields
Geology
and research's language is
العربية
Created by
Shamra Editor
Ask ChatGPT about the research
تعد ظلال الترددات المنخفضة أحد أهم مؤشرات تجمعات النفط و الغاز. يمكن التقاط هذه الترددات عن طريق تحليل التردد – زمن، الذي يزود بميز ترددي عالٍ عند الترددات المنخفضة و ميز زمني عالٍ عند الترددات المرتفعة، هذا الشيء يعد مرغوبًا من أجل تحليل المعطيات الاهتزازية و تفسيرها لأن مكامن الهيدروكربونات تشخص بشكل أفضل عند الترددات المنخفضة. لقد طبق هذا التحليل على معطيات اهتزازية مأخوذة من تركيب فهدة و هو حاوٍ على النفط و يقع في هضبة حلب. إن إضافة محور التردد إلى المقطع الاهتزازي العادي ثنائي البعد يجعل المعطيات بأبعاد ثلاثية. بمقارنة المقاطع ثلاثية الأبعاد ذات التردد الوحيد نستطيع أن نشاهد الظلال الترددية المنخفضة بسطوع على المقاطع وحيدة التردد بشكل نسبي في تركيب فهدة عند الترددات المنخفضة. و قد أعطت شواذَّ سعوية عالية نسبيًا تحت النطاق الحاوي على النفط . ثم اختفت هذه الشواذ في مقاطع الترددات العالية.
Low frequency shadows is one of hydrocarbons indicators. It can be detected by means of a time-frequency decomposition which can provide higher frequency resolution at lower frequencies and higher time resolution at higher frequencies. This is desirable for analyzing seismic data, because the hydrocarbons in reservoir are diagnostic at lower frequencies. we have carried out such analyses with post-stack data sets on Fahda field which is located in Aleppo uplift, it contains oil. Adding a frequency axis to a 2D seismic section makes the data 3D axis. The comparison of the single frequency sections from such 3D volume can be utilized to detect low frequency shadows. A preferentially illuminated single frequency section at lower frequencies from Fahda field, shows high amplitude low frequency anomalies beneath oil zones. These anomalies disappear at higher frequencies.
References used
Addison, P. S. (2002). The illustrated wavelet transform handbook, London, UK
Castagna, J. Anno, P and Taner et al., (2005). Spectral Decomposition of Seismic Data with Continuous Wavelet Transform, School of Geology and Geophysics, University of Oklahoma,U.S.A. Phil Anno
Iske, A; Randen, T. (2005). Mathematical methods and modelling in hydrocarbon exploration and production. University of Leicester, Department of Mathematics, United Kingdom. Avner Friedman
rate research
Read More
In this study, basic methodologies and procedures for generation
synthetic time histories in time domain and frequency domain are
summarized. These synthetic time histories are matching Syrian
spectrum and compatible with wide range of buildings m
odels and
soil types according to the seismic parameters of Lattakia city. This
paper will discuss the Selection and scaling criteria of three real
time history records available in strong ground motion databases to
satisfy the Syrian spectrum, and the suitability as input to time
history analysis of civil engineering structures.
The evaluation of the capacity of signalized intersections is one of the most important
parameter used in the planning, design, operation and management of urban street
networks. The calculation of capacity depends on the estimation of the saturati
on flow of
the signalized intersections.
The highway Capacity Manual (HCM 2010) is used to estimate and analyze the saturation
flow. This method is widely used in many countries around the world, including Syria. It
reflects the local conditions in the United States of America; therefore it may not be
suitable for use in our local conditions.
This study includes estimation of the field saturation flow depending on the headways
between the vehicles in the queues, and comparing the field results of the saturation flow
with the values resulting from using the HCM. The study was conducted on three
intersections in the Tartous city. The analysis was based on data collection using video.
The final results confirmed that there is a clear difference between the field measurement
of the saturation flow and computation value, Which confirms that it cannot be used for
our local conditions.
Weather forecasting (especially rainfall) is one of the most important and challenging
operational tasks carried out by meteorological services all over the world. Itis furthermore
a complicated procedure that requires multiple specialized fields o
f expertise.
In this paper, a model based on artificial neural networks (ANNs) and wavelet Transform
is proposed as tool to predict consecutive monthly rainfalls (1933-2009) taken of
Homs Meteorological Station on accounts of the preceding events of rainfall data.
The feed-forward neural network with back-propagation Algorithm is used in the
learning and forecasting, where the time series of rain that detailed transactions and the
approximate three levels of analysis using a Discrete wavelet transform (DWT).
The study found that the neural network WNN structured )5-8-8-8-1(, able to predict
the monthly rainfall in Homs station on the long-term correlation of determination and root
mean squared-errors (0.98, 7.74mm), respectively.
Wavelet Transform technique provides a useful feature based on the analysis of the
data, which improves the performance of the model and applied this technique in ANNmodels
for rain because it is simple, as this technique can be applied to other models.
The evaluation of surface water resources is a necessary input to solving water
management problems, which includes finding a relationship between precipitation and
runoff, and this relationship is a high degree of complexity. The rain of the most
important
factors that greatly effect on rivers discharge, and process to prediction of these flows must
take this factor into account, and much of the attention and study, artificial neural networks
and is considered one of the most modern methods in terms of accuracy results in linking
these multiple factors and highly complex. In order to predict the runoff contained daily to
Lake Dam Tishreen 16 in Latakia, the subject of our research, the application of different
models of artificial neural networks (ANN), was the previous input flows and rain.
Divided the data set for the period between (2006-2012) into two sets: training and
test, has been processing the data before using them as inputs to the neural network using
Discrete Wavelet Transform technique, to get rid of the maximum values and the values of
zero, where t the analysis of time series at three levels of accuracy before they are used
sub- series resulting as inputs to the Feed Forward ANN that depend back-propagation
algorithm for training.
The results indicated that with the structural neural network (1-2-6) Wavelet-ANN
model, are the best in the representation of the characteristics studied and best able to
predict runoff daily contained to Lake Dam Tishreen 16 for a day in advance, where he
reached the correlation coefficient the root of the mean of squared-errors (R2 = 0.96,
RMSE = 1.97m3 / sec), respectively.
In our research we studied and analyzed the different types of
methods used for automatic building detection from the satellite
images, then, we proposed a general methodology for building
detection based on its geometrical boundary features using Hough
transform for the rectangular forms.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
