Numerical Approach for the Prediction of Formation and Hydraulic Fracture Properties Considering Elliptical Flow Regime in Tight Gas Reservoirs
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Publication Date
Sun Mar 31 2024
Journal Name
Iraqi Geological Journal
Permeability Prediction and Facies Distribution for Yamama Reservoir in Faihaa Oil Field: Role of Machine Learning and Cluster Analysis Approach
Empirical and statistical methodologies have been established to acquire accurate permeability identification and reservoir characterization, based on the rock type and reservoir performance. The identification of rock facies is usually done by either using core analysis to visually interpret lithofacies or indirectly based on well-log data. The use of well-log data for traditional facies prediction is characterized by uncertainties and can be time-consuming, particularly when working with large datasets. Thus, Machine Learning can be used to predict patterns more efficiently when applied to large data. Taking into account the electrofacies distribution, this work was conducted to predict permeability for the four wells, FH1, FH2, F
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Publication Date
Sat Jun 01 2019
Journal Name
Geoderma
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Publication Date
Sun Feb 10 2019
Journal Name
Journal Of The College Of Education For Women
The hydrological regime of the Tigris River in the city of Baghdad
Water drainage pattern in the rivers and changed the nature of the renewed feeding areas
in the basin in terms of topographic and geological conditions and climate in addition to the
human role in organizing the process flow within these basins. This study addressed the
development of the Tigris River Hydrological in the city of Baghdad and found that the
annual rate of water drainage in the Tigris River was driven down very significantly,
especially in the past twenty years, and since 1996 up to 2014 record flow rates of less than
the overall rate of discharge of water, a (950 m3 / s ), in addition to the quarterly decrease the
discharge rates, especially since the beginning of the year 2000 and took converge all fo
Publication Date
Fri Jan 01 2021
Journal Name
Macromolecular Characterization Of Hydrocarbons For Sustainable Future
Feasibility of the Gas and Downhole Water Sink-Assisted Gravity Drainage (GDWS-AGD) Process to Enhance the Recovery of Oil in Reservoirs with Strong Aquifer
Gas and Downhole Water Sink, Gravity Drainage, GDWS-AGD, Enhance the Recovery of Oil
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Publication Date
Tue Nov 09 2021
Journal Name
Abu Dhabi International Petroleum Exhibition & Conference
Numerical Simulation of Gas Lift Optimization Using Artificial Intelligence for a Middle Eastern Oil Field
Abstract<p>Artificial lift techniques are a highly effective solution to aid the deterioration of the production especially for mature oil fields, gas lift is one of the oldest and most applied artificial lift methods especially for large oil fields, the gas that is required for injection is quite scarce and expensive resource, optimally allocating the injection rate in each well is a high importance task and not easily applicable. Conventional methods faced some major problems in solving this problem in a network with large number of wells, multi-constrains, multi-objectives, and limited amount of gas. This paper focuses on utilizing the Genetic Algorithm (GA) as a gas lift optimization algorit</p>
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evolutionary algorithm
gas lift
artificial intelligence
upstream oil & gas
machine learning
optimization problem
correlation
presented
allocation
reservoir pressure
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(5)
Publication Date
Tue Dec 23 2025
Journal Name
Journal Of Engineering
Publication Date
Mon Apr 22 2019
Journal Name
Spe
Evaluation of Gas and Downhole Water Sink-Assisted Gravity Drainage GDWS-AGD Process in Saturated Oil Reservoirs with Infinite-Acting Aquifer
Abstract<p>A hybrid Gas-Enhanced and Downhole Water Sink-Assisted Gravity Drainage (GDWS-AGD) process has been suggested to enhance oil recovery by placing vertical injectors for CO2 at the top of the reservoir with a series of horizontal oil-producing and water-drainage wells located above and below the oil-water contact, respectively. The injected gas builds a gas cap that drives the oil to the (upper) oil-producing wells while the bottom water-drainage wells control water cresting. The hybrid process of GDWS-AGD process has been first developed and tested in vertical wells to minimize water cut in reservoirs with bottom water drive and strong water coning tendencies. The wells were dual-compl</p>
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GDWS-AGD
GAGD
Gravity Drainage
Gas and Downhole Water Sink
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Publication Date
Sat Jan 01 2022
Journal Name
Swinburne University Of Technology
Publication Date
Sun Jan 01 2023
Journal Name
Journal Of Intelligent Systems
Optimizing genetic prediction: Define-by-run DL approach in DNA sequencing
DL
define-by-run
DNA sequencing
bioinformatics
personalized medicine
Abstract: The utility of DNA sequencing in diagnosing and prognosis of diseases is vital for assessing the risk of genetic disorders, particularly for asymptomatic individuals with a genetic predisposition. Such diagnostic approaches are integral in guiding health and lifestyle decisions and preparing families with the necessary foreknowledge to anticipate potential genetic abnormalities. The present study explores implementing a define-by-run deep learning (DL) model optimized using the Tree-structured Parzen estimator algorithm to enhance the precision of genetic diagnostic tools. Unlike conventional models, the define-by-run model bolsters accuracy through dynamic adaptation to data during the learning process and iterative optimization
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Publication Date
Thu Nov 01 2018
Journal Name
International Journal Of Biomathematics
A non-conventional hybrid numerical approach with multi-dimensional random sampling for cocaine abuse in Spain
This paper introduces a non-conventional approach with multi-dimensional random sampling to solve a cocaine abuse model with statistical probability. The mean Latin hypercube finite difference (MLHFD) method is proposed for the first time via hybrid integration of the classical numerical finite difference (FD) formula with Latin hypercube sampling (LHS) technique to create a random distribution for the model parameters which are dependent on time [Formula: see text]. The LHS technique gives advantage to MLHFD method to produce fast variation of the parameters’ values via number of multidimensional simulations (100, 1000 and 5000). The generated Latin hypercube sample which is random or non-deterministic in nature is further integ
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