Identification and Classification of Moving Vehicles on Road

It is important to know the road traffic density real time especially in cities for signal control and effective traffic management. In recent years, video monitoring and surveillance systems have been widely used in traffic management. Hence, traffic density estimation and vehicle classification can be achieved using video monitoring systems. The image sequences for traffic scenes are recorded by a stationary camera. The method is based on the establishment of correspondences between regions and vehicles, as the vehicles move through the image sequence. Background subtraction is used which improves the adaptive background mixture model and makes the system learn faster and more accurately, as well as adapt effectively to changing environments. The resulting system robustly identifies vehicles, rejecting background and tracks vehicles over a specific period of time. Once the (object) vehicle is tracked, the attributes of the vehicle like width, length, perimeter, area etc are extracted by image process feature extraction techniques. These features will be used in classification of vehicle as big or small using neural networks classification technique of data mining. In proposed system we use LABVIEW and Vision assistant module for image processing and feature extraction.  A feed-forward neural network is trained to classify vehicles using data mining WEKA toolbox. The system will solve major problems of human effort and errors in traffic monitoring and time consumption in conducting survey and analysis of data. The project will benefit to reduce cost of traffic monitoring system and complete automation of traffic monitoring system. Keywords: Image processing, Feature extraction, Segmentation, Threshold, Filter, Morphology, Blob, LABVIEW, NI, VI, Vision assistant, Data mining, Machine learning, Neural network, Back propagation, Multi layer perception, Classification, WEK

Hydrogen storage in depleted gas reservoirs: A comprehensive review

Hydrogen future depends on large-scale storage, which can be provided by geological formations (such as caverns, aquifers, and depleted oil and gas reservoirs) to handle demand and supply changes, a typical hysteresis of most renewable energy sources. Amongst them, depleted natural gas reservoirs are the most cost-effective and secure solutions due to their wide geographic distribution, proven surface facilities, and less ambiguous site evaluation. They also require less cushion gas as the native residual gases serve as a buffer for pressure maintenance during storage. However, there is a lack of thorough understanding of this technology. This work aims to provide a comprehensive insight and technical outlook into hydrogen storage in depleted gas reservoirs. It briefly discusses the operating and potential facilities, case studies, and the thermophysical and petrophysical properties of storage and withdrawal capacity, gas immobilization, and efficient gas containment. Furthermore, a comparative approach to hydrogen, methane, and carbon dioxide with respect to well integrity during gas storage has been highlighted. A summary of the key findings, challenges, and prospects has also been reported. Based on the review, hydrodynamics, geochemical, and microbial factors are the subsurface\u27s principal promoters of hydrogen losses. The injection strategy, reservoir features, quality, and operational parameters significantly impact gas storage in depleted reservoirs. Future works (experimental and simulation) were recommended to focus on the hydrodynamics and geomechanics aspects related to migration, mixing, and dispersion for improved recovery. Overall, this review provides a streamlined insight into hydrogen storage in depleted gas reservoirs

Comparative study of green and synthetic polymers for enhanced oil recovery

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. Several publications by authors in the field of petrochemical engineering have examined the use of chemically enhanced oil recovery (CEOR) technology, with a specific interest in polymer flooding. Most observations thus far in this field have been based on the application of certain chemicals and/or physical properties within this technique regarding the production of 50–60% trapped (residual) oil in a reservoir. However, there is limited information within the literature about the combined effects of this process on whole properties (physical and chemical). Accordingly, in this work, we present a clear distinction between the use of xanthan gum (XG) and hydrolyzed polyacrylamide (HPAM) as a polymer flood, serving as a background for future studies. XG and HPAM have been chosen for this study because of their wide acceptance in relation to EOR processes. To this degree, the combined effect of a polymer’s rheological properties, retention, inaccessible pore volume (PV), permeability reduction, polymer mobility, the effects of salinity and temperature, and costs are all investigated in this study. Further, the generic screening and design criteria for a polymer flood with emphasis on XG and HPAM are explained. Finally, a comparative study on the conditions for laboratory (experimental), pilot-scale, and field-scale application is presented

Date-leaf carbon particles for green enhanced oil recovery

Green enhanced oil recovery (GEOR) is an environmentally friendly enhanced oil recovery (EOR) process involving the injection of green fluids to improve macroscopic and microscopic sweep efficiencies while boosting tertiary oil production. Carbon nanomaterials such as graphene, carbon nanotube (CNT), and carbon dots have gained interest for their superior ability to increase oil recovery. These particles have been successfully tested in EOR, although they are expensive and do not extend to GEOR. In addition, the application of carbon particles in the GEOR method is not well understood yet, requiring thorough documentation. The goals of this work are to develop carbon nanoparticles from biomass and explore their role in GEOR. The carbon nanoparticles were prepared from date leaves, which are inexpensive biomass, through pyrolysis and ball-milling methods. The synthesized carbon nanomaterials were characterized using the standard process. Three formulations of functionalized and non-functionalized date-leaf carbon nanoparticle (DLCNP) solutions were chosen for core floods based on phase behavior and interfacial tension (IFT) properties to examine their potential for smart water and green chemical flooding. The carboxylated DLCNP was mixed with distilled water in the first formulation to be tested for smart water flood in the sandstone core. After water flooding, this formulation recovered 9% incremental oil of the oil initially in place. In contrast, non-functionalized DLCNP formulated with (the biodegradable) surfactant alkyl polyglycoside and NaCl produced 18% more tertiary oil than the CNT. This work thus provides new green chemical agents and formulations for EOR applications so that oil can be produced more economically and sustainably

A study in the dynamic kill for the control of induced surface blowouts

Vita

A study in the dynamic kill for the control of induced surface blowouts

Vita

Oil and Gas Wells: Enhanced Wellbore Casing Integrity Management through Corrosion Rate Prediction Using an Augmented Intelligent Approach

Wellbore integrity management for oil and gas wells plays a vital role throughout the typical lifespan of a well. Downhole casing leaks in oil- and gas-producing wells significantly affect their shallow water horizon, the environment, and fresh water resources. Additionally, downhole casing leaks may cause seepage of toxic gases to fresh water zones and the surface, through the casing annuli. Forecasting of such leaks and proactive measures of prevention will help eliminate their consequences and, in turn, better protect the environment. The objective of this study is to formulate an effective, robust, and accurate model for predicting the corrosion rate of metal casing string using artificial intelligence (AI) techniques. The input parameters used to train AI models include casing leaks, the percentage of metal loss, casing age, and average remaining barrier ratio (ARBR). The target parameter is the corrosion rate of the metal casing string. The dataset from which the AI models were trained was comprised of 250 data points collected from 218 wells in a giant carbonate reservoir that covered a wide range of practically reasonable values. Two AI tools were used: artificial neural networks (ANNs) and adaptive network-based fuzzy inference systems (ANFISs). A prediction comparison was made between these two tools. Based on the minimum average absolute percentage error (AAPE) and the highest coefficient of determination (R2) between the measured and predicted corrosion rate values, the ANN model proposed here was determined to be best for predicting the corrosion rate. An ANN-based empirical model is also presented in this study. The proposed model is based on the associated weights and biases. After evaluating the new ANN equation using an unseen validation dataset, it was concluded that the ANN equation was able to make predictions with a significantly lower AAPE and higher R2. Use of the proposed new equation is very cost-effective in terms of reducing the number of sequential surveys and experiments conducted. The proposed equation can be utilized without an AI engine. The developed model and empirical correlation are very promising and can serve as a handy tool for corrosion engineers seeking to determine the corrosion rate without training an AI model

Application of Machine Learning in Evaluation of the Static Young’s Modulus for Sandstone Formations

Prediction of the mechanical characteristics of the reservoir formations, such as static Young’s modulus (Estatic), is very important for the evaluation of the wellbore stability and development of the earth geomechanical model. Estatic considerably varies with the change in the lithology. Therefore, a robust model for Estatic prediction is needed. In this study, the predictability of Estatic for sandstone formation using four machine learning models was evaluated. The design parameters of the machine learning models were optimized to improve their predictability. The machine learning models were trained to estimate Estatic based on bulk formation density, compressional transit time, and shear transit time. The machine learning models were trained and tested using 592 well log data points and their corresponding core-derived Estatic values collected from one sandstone formation in well-A and then validated on 38 data points collected from a sandstone formation in well-B. Among the machine learning models developed in this work, Mamdani fuzzy interference system was the highly accurate model to predict Estatic for the validation data with an average absolute percentage error of only 1.56% and R of 0.999. The developed static Young’s modulus prediction models could help the new generation to characterize the formation rock with less cost and safe operation