Novel sampling techniques for reservoir history matching optimisation and uncertainty quantification in flow prediction

Modern reservoir management has an increasing focus on accurately predicting the likely range of field recoveries. A variety of assisted history matching techniques has been developed across the research community concerned with this topic. These techniques are based on obtaining multiple models that closely reproduce the historical flow behaviour of a reservoir. The set of resulted history matched models is then used to quantify uncertainty in predicting the future performance of the reservoir and providing economic evaluations for different field development strategies. The key step in this workflow is to employ algorithms that sample the parameter space in an efficient but appropriate manner. The algorithm choice has an impact on how fast a model is obtained and how well the model fits the production data. The sampling techniques that have been developed to date include, among others, gradient based methods, evolutionary algorithms, and ensemble Kalman filter (EnKF). This thesis has investigated and further developed the following sampling and inference techniques: Particle Swarm Optimisation (PSO), Hamiltonian Monte Carlo, and Population Markov Chain Monte Carlo. The inspected techniques have the capability of navigating the parameter space and producing history matched models that can be used to quantify the uncertainty in the forecasts in a faster and more reliable way. The analysis of these techniques, compared with Neighbourhood Algorithm (NA), has shown how the different techniques affect the predicted recovery from petroleum systems and the benefits of the developed methods over the NA. The history matching problem is multi-objective in nature, with the production data possibly consisting of multiple types, coming from different wells, and collected at different times. Multiple objectives can be constructed from these data and explicitly be optimised in the multi-objective scheme. The thesis has extended the PSO to handle multi-objective history matching problems in which a number of possible conflicting objectives must be satisfied simultaneously. The benefits and efficiency of innovative multi-objective particle swarm scheme (MOPSO) are demonstrated for synthetic reservoirs. It is demonstrated that the MOPSO procedure can provide a substantial improvement in finding a diverse set of good fitting models with a fewer number of very costly forward simulations runs than the standard single objective case, depending on how the objectives are constructed. The thesis has also shown how to tackle a large number of unknown parameters through the coupling of high performance global optimisation algorithms, such as PSO, with model reduction techniques such as kernel principal component analysis (PCA), for parameterising spatially correlated random fields. The results of the PSO-PCA coupling applied to a recent SPE benchmark history matching problem have demonstrated that the approach is indeed applicable for practical problems. A comparison of PSO with the EnKF data assimilation method has been carried out and has concluded that both methods have obtained comparable results on the example case. This point reinforces the need for using a range of assisted history matching algorithms for more confidence in predictions

CASE STUDY AND ANALYSIS Of THE PERFORMANCE OF INDUSTRIAL ACID GAS REMOVAL UNIT(AGRU)AT ELNG (EGYPTIAN LIQUIEFIED NATRUAL GAS) COMPANY THROUGH PROCESS SIMULATION

Acid gas removal process, which is also known as sweetening process, is considered a very important industrial operational process which has taken place in many works. The main idea of this process is based on absorption, and the selection of the solvent is mainly based on its capability of removing acid gases from the feed gas such as carbon dioxide (C02) and hydrogen sulphide (H2S). Such acid gases found in the gas can cause operational problems like corrosion and equipment plugging. The solvent used for the absorption processes to sweeten the natural gas is classified into two types which are chemical and physical absorption. The most used absorption processes for sweetening the natural gas are using the chemical solvents such as alkanolamines or "amine". In this context, diglycolamine (DGA) is used in the aqueous solution to remove the acid gases from natural gas stream. In this research, existing process flow diagram of industrial Acid Gas Removal Unit (AGRU) will be modified in terms of solvent composition· used in the absorption process. Manipulating the ratio of solvent to moisture content in the solvent solution will replace the existing solvent composition. Simulation using Aspen Hysys is then performed to parameters, which are absorption colunm removal efficiency, power consumption, heating duty and cooling duty. The simulation results are expected to show improvement to the existing AGRU system used at ELNG (Egyptian Liquefied Natural Gas Company)

BARRIERS TO IMPLEMENTATION OF BUILDING INFORMATION MODELLING (BIM) IN THE PALESTINIAN CONSTRUCTION INDUSTRY

The objective of this study is to determine potential barriers faced by the implementation of BIM in the Palestinian construction industry. The data collection was obtained using a questionnaire-based survey of 270 professionals in the construction industry. The results obtained from the factor analysis clustered the BIM barriers in four components, namely: the lack of interest in BIM, the organization's resistance to change workflows, the lack of BIM knowledge, and the cost implementation and cultural barriers towards adopting new technology and training requirements. The main reason given for not using BIM in the construction industry in the Gaza Strip is due to the fact that the clients and other contracting parties did not require the use of BIM. This study adds to the current body of knowledge on BIM in developing countries, especially in Palestine. The contributed knowledge establishes a good platform for future

Network coding cooperation performance analysis in wireless network over a lossy channel, M users and a destination scenario

Network coding (NC), introduced at the turn of the century, enables nodes in a network to combine data algebraically before either sending or forwarding them. Random network coding has gained popularity over the years by combining the received packet randomly before forwarding them, resulting in a complex Jordan Gaussian Elimination (JGE) decoding process. The effectiveness of random NC is through cooperation among nodes. In this paper, we propose a simple, low-complexity cooperative protocol that exploits NC in a deterministic manner resulting in improved diversity, data rate, and less complex JGE decoding process. The proposed system is applied over a lossy wireless network. The scenario under investigation is as follows: M users must send their information to a common destination D and to exchange the information between each others, over erasure channels; typically the channels between the users and the destination are worse than the channels between users. It is possible to significantly reduce the traffic amon g users and destination, achieving significant bandwidth savings, by combining packets from different users in simple, deterministic ways without resorting to extensive header information before being forwarded to the destination and the M users. The key problem we try to address is how to efficiently combine the packets at each user while exploiting user cooperation and the probability of successfully recovering information from all users at D with k < 2M unique linear equations, accounting for the fact that the remaining packets will be lost in the network and there are two transmission stages. Simulation results show the behaviour for two and three transmission stages. Our results show that applying NC protocols in two or three stages decreases the traffic significantly, beside the fact that the proposed protocols enable the system to retrieve the lost packets rather than asking for ARQ, resulting in improved data flow, and less power consumption. In fact, in some protocols the ARQ dropped from the rate 10-1 to 10-4, because of the proposed combining algorithm that enables the nodes to generate additional unique linear equations to broadcast rather than repeating the same ones via ARQ. Moreover, the number of the transmitted packets in each cooperative stage dropped from M (M − 1) to just M packets, resulting to 2 M packets instead 2 (M2 −  1) when three stages of transmission system are used instead of one stage (two cooperative stages)

A study on factors that affecting on employees’ job satisfaction among supporting units staff at Malaysia Marine and Heavy Engineering Sdn Bhd (MMHE) / Lina Marliana Mohamed Noor

The concept of job satisfaction is defined as an individual’s attitude about work roles and the relationship to worker motivation. There could be no job satisfaction where there is no motivation. As we noticed, anytime and every organization in this world, the main thing that they should take into account is employees’ job satisfaction. Without having this, the whole organization might collapse as it always rely and depends on employees. Therefore, in any ways, the leader or the management should highlight on how to satisfy their employees. This is very necessary to make sure the employees can stay longer and keep loyal towards the organization. Hence, this research aims to investigate and identify the Job Satisfaction Factors among supporting units staff at Malaysia Marine and Heavy Engineering (MMHE) Sdn Bhd. At the end, this research will determine whether Job Satisfaction Factors such as Leadership Style, Organizational Culture, Organizational Learning and Job Characteristics will affect towards Employees’ Job Satisfaction at MMHE or not. By using all of the 60 Questionnaires, the findings highlighted on the four Job Satisfaction Factors as mentioned. Therefore, in order to determine the relationship between Job Satisfaction Factors and Employees’ Job Satisfaction, a Likert-type scale has been developed and tested. Therefore in this research, the researcher tries to identify the most influential factor and the relationship between dependent and independent variables

A neuro-fuzzy approach for stator resistance estimation of induction motor = pendekatan neuro-fuzzy untuk meramal rintangan stator pada motor induksi

During the operation of induction motor, stator resistance changes incessantly with the temperature of the working machine. This situation may cause an error in rotor resistance estimation of the same magnitude and will produce an error between the actual and estimated motor torque which can leads to motor breakdown in worst cases. Therefore, this project will propose an approach to estimate the changes of induction motor stator resistance using neuro-fuzzy. Then, it will be compared with conventional method like P1 estimator to see the effectiveness. The behaviour of the induction machine will be analyzed when the stator resistance is changed. Based on the changes, a corrective procedure will be applied to ensure the stabilities of the induction motor. Generally, this project can be divided into three main parts which are design of induction motor, design of neuro-fuzzy and PT estimator, and corrective procedure for the induction machine. The Newcastle Drives Simulation Library will be used to design the induction motor model and MATLAB SIMULINK will be used to design the stator current observer. The neuro-fuzzy estimator will be designed based on Sugeno Method Fuzzy Inference System