油气勘探风险评价与决策技术研究

PetroChina and other national petroleum incorporations need rigorous procedures and practical methods in risk evaluation and exploration decision at home and abroad to safeguard their international exploration practice in exploration licence bidding, finding appropriate ratio of risk sharing with partners, as well as avoiding high risk projects and other key exploration activities. However, due to historical reasons, we are only at the beginning of a full study and methodology development in exploration risk evaluation and decision. No rigorous procedure and practical methods are available in our exercises of international exploration. Completely adopting foreign procedure, methods and tools by our national incorporations are not practical because of the differences of the current economic and management systems in China. The objective of this study is to establish a risk evaluation and decision system with independent intellectual property right in oil and gas exploration so that a smooth transition from our current practice into international norm can take place. The system developed in this dissertation includes the following four components: 1. A set of quantitative criteria for risk evaluation is derived on the basis of an anatomy of the parameters from thirty calibration regions national wide as well as the characteristics and the geological factors controlling oil and gas occurrence in the major petroleum-bearing basins in China, which provides the technical support for the risk quantification in oil and gas exploration. 2. Through analysis of existing methodology, procedure and methods of exploration risk evaluation considering spatial information are proposed. The method, utilizing Mahalanobis Distance (MD) and fuzzy logic for data and information integration, provides probabilistic models on the basis of MD and fuzzy logic classification criteria, thus quantifying the exploration risk using Bayesian theory. A projection of the geological risk into spatial domain provides a probability map of oil and gas occurrence in the area under study. The application of this method to the Nanpu Sag shows that this method not only correctly predicted the oil and gas occurrence in the areas where Beibu and Laoyemiao oil fields are found in the northwest of the onshore area, but also predicted Laopu south, Nanpu south and Hatuo potential areas in the offshore part where exploration maturity was very low. The prediction of the potential areas are subsequently confirmed by 17 exploration wells in the offshore area with 81% success, indicating this method is very effective for exploration risk visualization and reduction. 3. On the basis of “Methods and parameters of economic evaluation for petroleum exploration and development projects in China”, a ”pyramid” method for sensitivity analysis was developed, which meets not only the need for exploration target evaluation and exploration decision at home, but also allows a transition from our current practice to international norm in exploration decision. This provides the foundation for the development of a software product “Exploration economic evaluation and decision system of PetroChina” (EDSys). 4. To solve problem in methodology of exploration decision, effort was made on the method of project portfolio management. A drilling decision method was developed employing the concept of geologically risked net present value. This method overcame the dilemma of handling simultaneously both geological risk and portfolio uncertainty, thus casting light into the application of modern portfolio theory to the evaluation of high risk petroleum exploration projects.

Training needs analysis of healthcare library staff, undertaken for South Yorkshire Workforce Development Confederation

Urquhart, C., Durbin, J. & Spink, S. (2004). Training needs analysis of healthcare library staff, undertaken for South Yorkshire Workforce Development Confederation. Aberystwyth: Department of Information Studies, University of Wales Aberystwyth. Sponsorship: South Yorkshire WDC (NHS)

Load Profiling in Distributed Real-Time Systems

Load balancing is often used to ensure that nodes in a distributed systems are equally loaded. In this paper, we show that for real-time systems, load balancing is not desirable. In particular, we propose a new load-profiling strategy that allows the nodes of a distributed system to be unequally loaded. Using load profiling, the system attempts to distribute the load amongst its nodes so as to maximize the chances of finding a node that would satisfy the computational needs of incoming real-time tasks. To that end, we describe and evaluate a distributed load-profiling protocol for dynamically scheduling time-constrained tasks in a loosely-coupled distributed environment. When a task is submitted to a node, the scheduling software tries to schedule the task locally so as to meet its deadline. If that is not feasible, it tries to locate another node where this could be done with a high probability of success, while attempting to maintain an overall load profile for the system. Nodes in the system inform each other about their state using a combination of multicasting and gossiping. The performance of the proposed protocol is evaluated via simulation, and is contrasted to other dynamic scheduling protocols for real-time distributed systems. Based on our findings, we argue that keeping a diverse availability profile and using passive bidding (through gossiping) are both advantageous to distributed scheduling for real-time systems.

Review of Aerospace Engineering Cost Modelling: The Genetic Causal Approach

The primary intention of this paper is to review the current state of the art in engineering cost modelling as applied to aerospace. This is a topic of current interest and in addressing the literature, the presented work also sets out some of the recognised definitions of cost that relate to the engineering domain. The paper does not attempt to address the higher-level financial sector but rather focuses on the costing issues directly relevant to the engineering process, primarily those of design and manufacture. This is of more contemporary interest as there is now a shift towards the analysis of the influence of cost, as defined in more engineering related terms; in an attempt to link into integrated product and process development (IPPD) within a concurrent engineering environment. Consequently, the cost definitions are reviewed in the context of the nature of cost as applicable to the engineering process stages: from bidding through to design, to manufacture, to procurement and ultimately, to operation. The linkage and integration of design and manufacture is addressed in some detail. This leads naturally to the concept of engineers influencing and controlling cost within their own domain rather than trusting this to financers who have little control over the cause of cost. In terms of influence, the engineer creates the potential for cost and in a concurrent environment this requires models that integrate cost into the decision making process.

Challenging the SD benefits attributed to sport Mega-Events hosting

Sport Mega-event hosting faces opposition that is manifested with different intensity during the different phases of the event, from its inception as an idea to its delivery and legacy. Some Social Movements Organisations (SMOs) have acted as indefatigable monitors of the Sustainable Development (SD) dimension of sporting events in general and, in some of the most recent sport mega-events, in particular the Olympics, they have served as important advisors and facilitators. Nevertheless, in many cases we see enthusiastic supporters turning to vehemently challenging whatever positives have been associated with hosting the event. In addition, there is opposition to sport Mega-events in their entirety. That type of opposition tends to employ a holistic prism that manages to identify multiple interconnected negative aspects of hosting a sport mega-event and incorporate them into an anti-systemic discourse. It is important to bear in mind that irrespective of many proclamations to the opposite as far as megas are concerned (projects and/or events), a number of studies have demonstrated that citizen participation and democratic accountability in decision-making have been notoriously absent. After all, the idea of citizen participation in the planning of sport mega-event is essentially the public response to a plan conceived by others. There were, of course, some notable cases of democratic consultation at the early stages of bidding to host a sport mega-event but these more democratic approaches resulted in the failure of the bid (for e.g. Toronto 1996). The knowledge of this by the groups that initiated the hosting idea and the bidding process has led to discouraging in depth public consultation that may fit perfectly to the democratic process but not to the tight schedules of associated projects completion. That produces ‘autocracy against which opposition may arise’ (Hiller, 2000, p. 198). It is this democratic deficit that has led to important instances of social contestation and protest mobilizations by citizen groups as well as the more regular corps of social activists. From a perspective borrowed from the sociology of protest and social movements, sport mega-events hosting can operate as an issue that stimulates protest activities by an existing protest milieu and new actors as well as an important mobilizing resource. In fact, some scholars have also argued that the Olympic Games were an important frame for the transnational activism that was marked by anti-globalization protest in Seattle in 1999 (Cottrell & Nelson, 201; Lenskyj, 2008). In addition, it’s important not to lose sight of other acts dissent that take place in relatively close proximity, about a year before the event when most infrastructural and societal changes brought by hosting the event and impact start to become apparent by the host communities, like the rioting of August 2011 in the London Olympic Boroughs and the 2012 riots of June 2013 in Sao Paulo and other Brazilian cities. This paper starts by outlining the SD claims made in the bidding to host the summer Olympic Games by five prospective hosts (Sydney; Athens; Beijing; London and Rio) proceeds towards examining the opposition and challenges that was manifested in relation to these claims. In Particular it provides an assessment of protest-events over the aforementioned different phases of sport mega-events hosting. A different picture emerges for each of the host nation that is partly explained by local, national and global configuration of protest politics. Whereas the post-event legacy of the first two hosts of the Games can be assessed and that way see the validity of claims made by challengers in the other phases, in the other three cases, the implementation of Olympic Games Impact (OGI) studies offers the tool for discussing the post-event phase for Beijing and London and engage in a speculative exercise for the case of Rio. Judging by available findings, the paper concludes that the SD aspiration made in the bid documents are unlikely to be met and social contestation based on the same issues is likely to increase due to the current global economic crisis and BRICS, like China and Brazil, having entered the process of becoming global economic hegemons.

Willingness to pay for cataract surgery in rural Southern China.

PURPOSE: To study willingness to pay for cataract surgery, and its associations, in Southern China. DESIGN: Cross-sectional willingness-to-pay interview incorporating elements of the open-ended and bidding formats. PARTICIPANTS: Three-hundred thirty-nine persons presenting for cataract screening in Yangjiang, China, with presenting visual acuity (VA) < or = 6/60 in either eye due to cataract. METHODS: Subjects underwent measurement of their VA and a willingness-to-pay interview. Age, gender, literacy, education, and annual income also were recorded. MAIN OUTCOME MEASURES: Maximum amount that the subjects would be willing to pay for cataract surgery. RESULTS: Among 325 (95.9%) subjects completing the interview, 169 (52.0%) were 70 years or older, 213 (65.5%) were women, and 217 (66.8%) had an annual income of <5000 renminbi (5000 = US 625 dollars). Eighty percent (n = 257) of participants were willing to pay something for surgery (mean, 442+/-444 renminbi [US 55 dollars+/-55]). In regression models, older subjects were willing to pay less (8 renminbi [US 1 dollar] per year of age; P = 0.01). Blind subjects were significantly more likely (odds ratio, 5.7; 95% confidence interval, 1.7-19.3) to pay anything for surgery, but would pay on average 255 renminbi (US 32 dollars) less (P = 0.004). Persons at the highest annual income level (>10,000 renminbi [US 1250 dollars]) would pay 50 dollars more for surgery than those at the lowest level (<5000 renminbi) (P = 0.0003). The current cost of surgery in this program is 500 renminbi (US 63 dollars). CONCLUSIONS: Sustainable programs will need to attract younger, more well-to-do persons with better vision, while still providing access to the neediest patients.

Willingness to pay for potential enhancements to a low-cost cataract surgical package in rural southern China.

PURPOSE: To assess determinants of patients' willingness to pay (WTP) for potential components of a multi-tiered cataract surgical package offered by a non-governmental organization (NGO) in rural China. DESIGN: Cross-sectional study. METHODS: Demographic and clinical data were collected from 505 patients presenting for cataract screening or surgery in Yangjiang, China. Willingness to pay for potential enhancements to the current surgery package was assessed using a bidding format with random payment cards. RESULTS: Among 426 subjects (84.4%) completing interviews, the mean age was 73.9 ± 7.3 years, 67.6% were women and 73% (n = 310) would pay for at least one offering, with 33-38% WTP for each item. Among those who would pay, the mean WTP for food was US$1.68 ± 0.13, transportation US$3.24 ± 0.25, senior surgeon US$50.0 ± 3.36 and US$89.4 ± 4.19 for an imported intra-ocular lens (IOL). The estimated total recovery from these enhancements under various assumptions would be US$20-50 (compared to the current programme price of US$65). In multivariate models, WTP for the senior surgeon increased with knowledge of a person previously operated for cataract (OR = 2.13, 95% CI 1.42-3.18, p < 0.001). Willingness to pay for the imported IOL increased with knowledge of a previously operated person (OR = 1.85, 95% CI 1.24-2.75, p < 0.01) and decreased with age >75 years (OR = 0.61, 0.40-0.93, p < 0.05). CONCLUSIONS: Opportunities exist to increase cataract programme revenues through multi-tiered offerings in this setting, allowing greater subsidization of low-income patients. Personal familiarity with cataract surgery is important in determining WTP. © 2011 The Authors. Acta Ophthalmologica © 2011 Acta Ophthalmologica Scandinavica Foundation.

Intelligent decision making in electricity markets: simulated annealing Q-Learning

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM is a multi-agent electricity market simulator to model market players and simulate their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. MASCEM is integrated with ALBidS, a system that provides several dynamic strategies for agents’ behavior. This paper presents a method that aims at enhancing ALBidS competence in endowing market players with adequate strategic bidding capabilities, allowing them to obtain the higher possible gains out of the market. This method uses a reinforcement learning algorithm to learn from experience how to choose the best from a set of possible actions. These actions are defined accordingly to the most probable points of bidding success. With the purpose of accelerating the convergence process, a simulated annealing based algorithm is included.

Dynamic artificial neural network for electricity market prices forecast

This paper presents an artificial neural network applied to the forecasting of electricity market prices, with the special feature of being dynamic. The dynamism is verified at two different levels. The first level is characterized as a re-training of the network in every iteration, so that the artificial neural network can able to consider the most recent data at all times, and constantly adapt itself to the most recent happenings. The second level considers the adaptation of the neural network’s execution time depending on the circumstances of its use. The execution time adaptation is performed through the automatic adjustment of the amount of data considered for training the network. This is an advantageous and indispensable feature for this neural network’s integration in ALBidS (Adaptive Learning strategic Bidding System), a multi-agent system that has the purpose of providing decision support to the market negotiating players of MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

A data-mining-based methodology for transmission expansion planning

In recent decades, all over the world, competition in the electric power sector has deeply changed the way this sector’s agents play their roles. In most countries, electric process deregulation was conducted in stages, beginning with the clients of higher voltage levels and with larger electricity consumption, and later extended to all electrical consumers. The sector liberalization and the operation of competitive electricity markets were expected to lower prices and improve quality of service, leading to greater consumer satisfaction. Transmission and distribution remain noncompetitive business areas, due to the large infrastructure investments required. However, the industry has yet to clearly establish the best business model for transmission in a competitive environment. After generation, the electricity needs to be delivered to the electrical system nodes where demand requires it, taking into consideration transmission constraints and electrical losses. If the amount of power flowing through a certain line is close to or surpasses the safety limits, then cheap but distant generation might have to be replaced by more expensive closer generation to reduce the exceeded power flows. In a congested area, the optimal price of electricity rises to the marginal cost of the local generation or to the level needed to ration demand to the amount of available electricity. Even without congestion, some power will be lost in the transmission system through heat dissipation, so prices reflect that it is more expensive to supply electricity at the far end of a heavily loaded line than close to an electric power generation. Locational marginal pricing (LMP), resulting from bidding competition, represents electrical and economical values at nodes or in areas that may provide economical indicator signals to the market agents. This article proposes a data-mining-based methodology that helps characterize zonal prices in real power transmission networks. To test our methodology, we used an LMP database from the California Independent System Operator for 2009 to identify economical zones. (CAISO is a nonprofit public benefit corporation charged with operating the majority of California’s high-voltage wholesale power grid.) To group the buses into typical classes that represent a set of buses with the approximate LMP value, we used two-step and k-means clustering algorithms. By analyzing the various LMP components, our goal was to extract knowledge to support the ISO in investment and network-expansion planning.

ANN based day-ahead spinning reserve forecast for electricity market simulation

Electricity market players operating in a liberalized environment require adequate decision support tools, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services represent a good negotiation opportunity that must be considered by market players. This paper deals with short-term predication of day-ahead spinning reserve (SR) requirement that helps the ISO to make effective and timely decisions. Based on these forecasted information, market participants can use strategic bidding for day-ahead SR market. The proposed concepts and methodologies are implemented in MASCEM, a multi-agent based electricity market simulator. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

ANN based day-ahead ancillary services forecast for electricity market simulation

Adequate decision support tools are required by electricity market players operating in a liberalized environment, allowing them to consider all the business opportunities and take strategic decisions. Ancillary services (AS) represent a good negotiation opportunity that must be considered by market players. Based on the ancillary services forecasting, market participants can use strategic bidding for day-ahead ancillary services markets. For this reason, ancillary services market simulation is being included in MASCEM, a multi-agent based electricity market simulator that can be used by market players to test and enhance their bidding strategies. The paper presents the methodology used to undertake ancillary services forecasting, based on an Artificial Neural Network (ANN) approach. ANNs are used to day-ahead prediction of non-spinning reserve (NS), regulation-up (RU), and regulation down (RD). Spinning reserve (SR) is mentioned as past work for comparative analysis. A case study based on California ISO (CAISO) data is included; the forecasted results are presented and compared with CAISO published forecast.

Adaptive learning in agents behaviour: a framework for electricity markets simulation

Electricity markets are complex environments with very particular characteristics. A critical issue regarding these specific characteristics concerns the constant changes they are subject to. This is a result of the electricity markets’ restructuring, which was performed so that the competitiveness could be increased, but it also had exponential implications in the increase of the complexity and unpredictability in those markets scope. The constant growth in markets unpredictability resulted in an amplified need for market intervenient entities in foreseeing market behaviour. The need for understanding the market mechanisms and how the involved players’ interaction affects the outcomes of the markets, contributed to the growth of usage of simulation tools. Multi-agent based software is particularly well fitted to analyze dynamic and adaptive systems with complex interactions among its constituents, such as electricity markets. This dissertation presents ALBidS – Adaptive Learning strategic Bidding System, a multiagent system created to provide decision support to market negotiating players. This system is integrated with the MASCEM electricity market simulator, so that its advantage in supporting a market player can be tested using cases based on real markets’ data. ALBidS considers several different methodologies based on very distinct approaches, to provide alternative suggestions of which are the best actions for the supported player to perform. The approach chosen as the players’ actual action is selected by the employment of reinforcement learning algorithms, which for each different situation, simulation circumstances and context, decides which proposed action is the one with higher possibility of achieving the most success. Some of the considered approaches are supported by a mechanism that creates profiles of competitor players. These profiles are built accordingly to their observed past actions and reactions when faced with specific situations, such as success and failure. The system’s context awareness and simulation circumstances analysis, both in terms of results performance and execution time adaptation, are complementary mechanisms, which endow ALBidS with further adaptation and learning capabilities.

Análise do mercado de serviços de regulação de frequência secundária e terciária no sistema eléctrico português

Dissertação para obtenção do grau de Mestre em Engenharia Electrotécnica Ramo Energia

Adaptive learning in agents behaviour: A framework for electricity markets simulation

Electricity markets are complex environments, involving a large number of different entities, playing in a dynamic scene to obtain the best advantages and profits. MASCEM (Multi-Agent System for Competitive Electricity Markets) is a multi-agent electricity market simulator that models market players and simulates their operation in the market. Market players are entities with specific characteristics and objectives, making their decisions and interacting with other players. This paper presents a methodology to provide decision support to electricity market negotiating players. This model allows integrating different strategic approaches for electricity market negotiations, and choosing the most appropriate one at each time, for each different negotiation context. This methodology is integrated in ALBidS (Adaptive Learning strategic Bidding System) – a multiagent system that provides decision support to MASCEM's negotiating agents so that they can properly achieve their goals. ALBidS uses artificial intelligence methodologies and data analysis algorithms to provide effective adaptive learning capabilities to such negotiating entities. The main contribution is provided by a methodology that combines several distinct strategies to build actions proposals, so that the best can be chosen at each time, depending on the context and simulation circumstances. The choosing process includes reinforcement learning algorithms, a mechanism for negotiating contexts analysis, a mechanism for the management of the efficiency/effectiveness balance of the system, and a mechanism for competitor players' profiles definition.