Measuring Productivity Gains from Deregulation of the Japanese Urban Gas Industry

The Japanese government initiated a series of regulatory reforms in the mid-1990s. The Japanese urban gas industry consists of various sized private and non-private firms. Numerous previous studies find that deregulation leads to productivity improvements. We extend the literature by analyzing deregulation, privatization, and other aspects of a regulated industry using unique firm level data. This study measures productivity to evaluate the effect of the deregulation reform. Using data from 205 firms from 1993 to 2004, we find that the deregulation effect differs depending on firm size. Competitive pressure contributes to advanced productivity. The deregulation of gas sales to commercial customers is the most important factor for advancing productivity. Copyright © 2013 by the IAEE. All rights reserved.

How innovation and R&D happen in the upstream oil & gas industry : insights from a global survey

Few would disagree that the upstream oil & gas industry has become more technology-intensive over the years. But how does innovation happen in the industry? Specifically, what ideas and inputs flow from which parts of the sector׳s value network, and where do these inputs go? And how do firms and organizations from different countries contribute differently to this process? This paper puts forward the results of a survey designed to shed light on these questions. Carried out in collaboration with the Society of Petroleum Engineers (SPE), the survey was sent to 469 executives and senior managers who played a significant role with regard to R&D and/or technology deployment in their respective business units. A total of 199 responses were received from a broad range of organizations and countries around the world. Several interesting themes and trends emerge from the results, including: (1) service companies tend to file considerably more patents per innovation than other types of organization; (2) over 63% of the deployed innovations reported in the survey originated in service companies; (3) neither universities nor government-led research organizations were considered to be valuable sources of new information and knowledge in the industry׳s R&D initiatives, and; (4) despite the increasing degree of globalization in the marketplace, the USA still plays an extremely dominant role in the industry׳s overall R&D and technology deployment activities. By providing a detailed and objective snapshot of how innovation happens in the upstream oil & gas sector, this paper provides a valuable foundation for future investigations and discussions aimed at improving how R&D and technology deployment are managed within the industry. The methodology did result in a coverage bias within the survey, however, and the limitations arising from this are explored.

Data as an asset : what the upstream oil & gas industry can learn about ‘big data’ from companies like Facebook

The upstream oil & gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data”—that is, the ability to apply more sophisticated types of analytical tools to information in a way that extracts new insights or creates new forms of value—is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil & gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This paper examines existing data management practices in the upstream oil & gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the Big Data revolution. The comparison shows that, in companies that are leading the Big Data revolution, data is regarded as a valuable asset. The presented evidence also shows, however, that this is usually not true within the oil & gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how upstream oil & gas companies could potentially extract more value from data, and concludes with a series of specific technical and management-related recommendations to this end.

Using Big Data to manage safety-related risk in the upstream oil and gas industry: A research agenda

Despite considerable effort and a broad range of new approaches to safety management over the years, the upstream oil & gas industry has been frustrated by the sector’s stubbornly high rate of injuries and fatalities. This short communication points out, however, that the industry may be in a position to make considerable progress by applying “Big Data” analytical tools to the large volumes of safety-related data that have been collected by these organizations. Toward making this case, we examine existing safety-related information management practices in the upstream oil & gas industry, and specifically note that data in this sector often tends to be highly customized, difficult to analyze using conventional quantitative tools, and frequently ignored. We then contend that the application of new Big Data kinds of analytical techniques could potentially reveal patterns and trends that have been hidden or unknown thus far, and argue that these tools could help the upstream oil & gas sector to improve its injury and fatality statistics. Finally, we offer a research agenda toward accelerating the rate at which Big Data and new analytical capabilities could play a material role in helping the industry to improve its health and safety performance.

Asset life prediction and maintenance decision-making using a non-linear non-Gaussian state space model

Estimating and predicting degradation processes of engineering assets is crucial for reducing the cost and insuring the productivity of enterprises. Assisted by modern condition monitoring (CM) technologies, most asset degradation processes can be revealed by various degradation indicators extracted from CM data. Maintenance strategies developed using these degradation indicators (i.e. condition-based maintenance) are more cost-effective, because unnecessary maintenance activities are avoided when an asset is still in a decent health state. A practical difficulty in condition-based maintenance (CBM) is that degradation indicators extracted from CM data can only partially reveal asset health states in most situations. Underestimating this uncertainty in relationships between degradation indicators and health states can cause excessive false alarms or failures without pre-alarms. The state space model provides an efficient approach to describe a degradation process using these indicators that can only partially reveal health states. However, existing state space models that describe asset degradation processes largely depend on assumptions such as, discrete time, discrete state, linearity, and Gaussianity. The discrete time assumption requires that failures and inspections only happen at fixed intervals. The discrete state assumption entails discretising continuous degradation indicators, which requires expert knowledge and often introduces additional errors. The linear and Gaussian assumptions are not consistent with nonlinear and irreversible degradation processes in most engineering assets. This research proposes a Gamma-based state space model that does not have discrete time, discrete state, linear and Gaussian assumptions to model partially observable degradation processes. Monte Carlo-based algorithms are developed to estimate model parameters and asset remaining useful lives. In addition, this research also develops a continuous state partially observable semi-Markov decision process (POSMDP) to model a degradation process that follows the Gamma-based state space model and is under various maintenance strategies. Optimal maintenance strategies are obtained by solving the POSMDP. Simulation studies through the MATLAB are performed; case studies using the data from an accelerated life test of a gearbox and a liquefied natural gas industry are also conducted. The results show that the proposed Monte Carlo-based EM algorithm can estimate model parameters accurately. The results also show that the proposed Gamma-based state space model have better fitness result than linear and Gaussian state space models when used to process monotonically increasing degradation data in the accelerated life test of a gear box. Furthermore, both simulation studies and case studies show that the prediction algorithm based on the Gamma-based state space model can identify the mean value and confidence interval of asset remaining useful lives accurately. In addition, the simulation study shows that the proposed maintenance strategy optimisation method based on the POSMDP is more flexible than that assumes a predetermined strategy structure and uses the renewal theory. Moreover, the simulation study also shows that the proposed maintenance optimisation method can obtain more cost-effective strategies than a recently published maintenance strategy optimisation method by optimising the next maintenance activity and the waiting time till the next maintenance activity simultaneously.

Strategies for the management and treatment of coal seam gas associated water

Coal seam gas (CSG) is a growing industry in Queensland and represents a potential major employer and deliverer of financial prosperity for years to come. CSG is a natural gas composed primarily of methane and is found trapped underground in coal beds. During the gas extraction process, significant volumes of associated water are also produced. This associated water could be a valuable resource, however, the associated water comprises of various salt constituents that make it problematic for beneficial use. Consequently, there is a need to implement various water treatment strategies to purify the associated water to comply with Queensland’s strict guidelines and to mitigate environmental risks. The resultant brine is also of importance as ultimately it also has to be dealt with in an economical manner. In some ways it can be considered that the CSG industry does not face a water problem, as this has inherent value to society, but rather has a “salt issue” to solve. This study analyzes the options involved in both the water treatment and salt recovery processes. A brief overview of the constituents present in Queensland CS water is made to illustrate the challenges involved and a range of treatment technologies discussed. Water treatment technologies examined include clarification (ballasted flocculation, dissolved air flotation, electrocoagulation), membrane filtration (ultrafiltration), ion exchange softening and desalination (ion exchange, reverse osmosis desalination and capacitance deionization). In terms of brine management we highlighted reinjection, brine concentration ponds, membrane techniques (membrane distillation, forward osmosis), thermal methods, electrodialysis, electrodialysis reversal, bipolar membrane electrodialysis, wind assisted intensive evaporation, membrane crystallization, eutectic freeze crystallization and vapor compression. As an entirety this investigation is designed to be an important tool in developing CS water treatment management strategies for effective management in Queensland and worldwide.

Coal Seam Gas Water from Maramarua, New Zealand: Characterisation and Comparison to United States Analogues

Groundwater from Maramarua has been identified as coal seam gas (CSG) water by studying its composition, and comparing it against the geochemical signature from other CSG basins. CSG is natural gas that has been produced through thermogenic and biogenic processes in underground coal seams; CSG extraction requires the abstraction of significant amounts of CSG water. To date, no international literature has described coal seam gas water in New Zealand, however recent CSG exploration work has resulted in CSG water quality data from a coal seam in Maramarua, New Zealand. Water quality from this site closely follows the geochemical signature associated with United States CSG waters, and this has helped to characterise the type of water being abstracted. CSG water from this part of Maramarua has low calcium, magnesium, and sulphate concentrations but high sodium (334 mg/l), chloride (146 mg/l) and bicarbonate (435 mg/l) concentrations. In addition, this water has high pH (7.8) and alkalinity (360 mg/l as CaCO3), which is a direct consequence of carbonate dissolution and biogenic processes. Different analyte ratios ('source-rock deduction' method) have helped to identify the different formation processes responsible in shaping Maramarua CSG water

Machine prognostics based on health state probability estimation

The ability to accurately predict the remaining useful life of machine components is critical for machine continuous operation and can also improve productivity and enhance system’s safety. In condition-based maintenance (CBM), maintenance is performed based on information collected through condition monitoring and assessment of the machine health. Effective diagnostics and prognostics are important aspects of CBM for maintenance engineers to schedule a repair and to acquire replacement components before the components actually fail. Although a variety of prognostic methodologies have been reported recently, their application in industry is still relatively new and mostly focused on the prediction of specific component degradations. Furthermore, they required significant and sufficient number of fault indicators to accurately prognose the component faults. Hence, sufficient usage of health indicators in prognostics for the effective interpretation of machine degradation process is still required. Major challenges for accurate longterm prediction of remaining useful life (RUL) still remain to be addressed. Therefore, continuous development and improvement of a machine health management system and accurate long-term prediction of machine remnant life is required in real industry application. This thesis presents an integrated diagnostics and prognostics framework based on health state probability estimation for accurate and long-term prediction of machine remnant life. In the proposed model, prior empirical (historical) knowledge is embedded in the integrated diagnostics and prognostics system for classification of impending faults in machine system and accurate probability estimation of discrete degradation stages (health states). The methodology assumes that machine degradation consists of a series of degraded states (health states) which effectively represent the dynamic and stochastic process of machine failure. The estimation of discrete health state probability for the prediction of machine remnant life is performed using the ability of classification algorithms. To employ the appropriate classifier for health state probability estimation in the proposed model, comparative intelligent diagnostic tests were conducted using five different classifiers applied to the progressive fault data of three different faults in a high pressure liquefied natural gas (HP-LNG) pump. As a result of this comparison study, SVMs were employed in heath state probability estimation for the prediction of machine failure in this research. The proposed prognostic methodology has been successfully tested and validated using a number of case studies from simulation tests to real industry applications. The results from two actual failure case studies using simulations and experiments indicate that accurate estimation of health states is achievable and the proposed method provides accurate long-term prediction of machine remnant life. In addition, the results of experimental tests show that the proposed model has the capability of providing early warning of abnormal machine operating conditions by identifying the transitional states of machine fault conditions. Finally, the proposed prognostic model is validated through two industrial case studies. The optimal number of health states which can minimise the model training error without significant decrease of prediction accuracy was also examined through several health states of bearing failure. The results were very encouraging and show that the proposed prognostic model based on health state probability estimation has the potential to be used as a generic and scalable asset health estimation tool in industrial machinery.

Coal seam gas waters - their geochemical signature and potential environmental implications

Coal Seam Gas (CSG) is a form of natural gas (mainly methane) sorbed in underground coal beds. To mine this gas, wells are drilled directly into an underground coal seam and groundwater (CSG water) is pumped out to the surface. This lowers the downhole piezometric pressure and enables gas desporption from the coal matrix. In the United States, this gas has been extracted commercially since the 1980s. The economic success of US CSG projects has inspired exploration and development in Australia and New Zealand. In Australia, Queensland’s Bowen and Surat basins have been the subject of increased CSG development over the last decade. CSG growth in other Australian basins has not matured to the same level but exploration and development are taking place at an accelerated pace in the Sydney Basin (Illawarra and the Hunter Valley, NSW) and in the Gunnedah Basin. Similarly, CSG exploration in New Zealand has focused in the Waikato region (Maramarua and Huntly), in the West Coast region (Buller, Reefton, and Greymouth), and in Southland (Kaitangata, Mataura, and Ohai). Figure 1 shows a Shcoeller diagram with CSG samples from selected basins in Australia, New Zealand, and the USA. CSG water from all of these basins exhibit the same geochemical signature – low calcium, low magnesium, high bicarbonate, low sulphate and, sometimes, high chloride. This water quality is a direct result of specific biological and geological processes that have taken part in the formation of CSG. In general, these processes include the weathering of rocks (carbonates, dolomite, and halite), cation exchange with clays (responsible for enhanced sodium and depleted calcium and magnesium), and biogenic processes (accounting for the presence of high bicarbonate concentrations). The salinity of CSG waters tends to be brackish (TDS < 30000 mg/l) with a fairly neutral pH. These particular characteristics need to be taken into consideration when assessing water management and disposal alternatives. Environmental issues associated with CSG water disposal have been prominent in developed basins such as the Powder River Basin (PRB) in the United States. When disposed on the land or used for irrigation, water having a high dissolved salts content may reduce water availability to crops thus affecting crop yield. In addition, the high sodium, low calcium and low magnesium concentrations increase the potential to disperse soils and significantly reduce the water infiltration rate. Therefore, CSG waters need to be properly characterised, treated, and disposed to safeguard the environment without compromising other natural resources.

Environmental incidents in a developing country and corporate environmental disclosures : a study of a multinational gas company

Purpose – The purpose of this paper is to examine the environmental disclosure initiatives of Niko Resources Ltd – a Canada-based multinational oil and gas company – following the two major environmental blowouts at a gas field in Bangladesh in 2005. As part of the examination, the authors particularly focus on whether Niko's disclosure strategy was associated with public concern pertaining to the blowouts. Design/methodology/approach – The authors reviewed news articles about Niko's environmental incidents in Bangladesh and Niko's communication media, including annual reports, press releases and stand-alone social responsibility report over the period 2004-2007, to understand whether news media attention as proxy for public concern has an impact on Niko's disclosure practices in relation to the affected local community in Bangladesh. Findings – The findings show that Niko did not provide any non-financial environmental information within its annual reports and press releases as a part of its responsibility to the local community which was affected by the blowouts, but it did produce a stand-alone report to address the issue. However, financial environmental disclosures, such as the environmental contingent liability disclosure, were adequately provided through annual reports to meet the regulatory requirements concerning environmental persecutions. The findings also suggest that Niko's non-financial disclosure within a stand-alone report was associated with the public pressures as measured by negative media coverage towards the Niko blowouts. Research limitations/implications – This paper concludes that the motive for Niko's non-financial environmental disclosure, via a stand-alone report, reflected survival considerations: the company's reaction did not suggest any real attempt to hold broader accountability for its activities in a developing country.

CO2 capture and gas separation on boron carbon nanotubes

Concern about the increasing atmospheric CO2 concentration and its impact on the environment has led to increasing attention directed toward finding advanced materials and technologies suited for efficient CO2 capture, storage and purification of clean-burning natural gas. In this letter, we have performed comprehensive theoretical investigation of CO2, N2, CH4 and H2 adsorption on B2CNTs. Our study shows that CO2 molecules can form strong interactions with B2CNTs with different charge states. However, N2, CH4 and H2 can only form very weak interactions with B2CNTs. Therefore, the study demonstrates B2CNTs could sever as promising materials for CO2 capture and gas separation.

Taking cues from outer space : the E&P sector can learn much about asset maintenance from the space and satellite industry

The E&P sector can learn much about asset maintenance from the space and satellite industry. Practitioners from both the upstream oil and gas industry and the space and satellite sector have repeatedly noted several striking similarities between the two industries over the years, which have in turn resulted in many direct comparisons in the media and industry press. The similarities between the two industries have even resulted in a modest amount of cross-pollinating between the respective supply chains. Because the operating conditions of both industries are so extreme, some oil and gas equipment vendors have occasionally sourced motors and other parts from aerospace contractors. Also, satellites are now being used to assess oil fires, detect subsidence in oil fields, measure oil spills, collect and transmit operational data from oil and gas fields, and monitor the movement of icebergs that might potentially collide with offshore oil and gas installations.

Assessment of Geogenic Contaminants in Water Co-Produced with Coal Seam Gas Extraction in Queensland, Australia : Implications for Human Health Risk

Organic compounds in Australian coal seam gas produced water (CSG water) are poorly understood despite their environmental contamination potential. In this study, the presence of some organic substances is identified from government-held CSG water-quality data from the Bowen and Surat Basins, Queensland. These records revealed the presence of polycyclic aromatic hydrocarbons (PAHs) in 27% of samples of CSG water from the Walloon Coal Measures at concentrations <1 µg/L, and it is likely these compounds leached from in situ coals. PAHs identified from wells include naphthalene, phenanthrene, chrysene and dibenz[a,h]anthracene. In addition, the likelihood of coal-derived organic compounds leaching to groundwater is assessed by undertaking toxicity leaching experiments using coal rank and water chemistry as variables. These tests suggest higher molecular weight PAHs (including benzo[a]pyrene) leach from higher rank coals, whereas lower molecular weight PAHs leach at greater concentrations from lower rank coal. Some of the identified organic compounds have carcinogenic or health risk potential, but they are unlikely to be acutely toxic at the observed concentrations which are almost negligible (largely due to the hydrophobicity of such compounds). Hence, this study will be useful to practitioners assessing CSG water related environmental and health risk.

Data as an asset: what the oil and gas sector can learn from other industries about “Big Data”

The upstream oil and gas industry has been contending with massive data sets and monolithic files for many years, but “Big Data” is a relatively new concept that has the potential to significantly re-shape the industry. Despite the impressive amount of value that is being realized by Big Data technologies in other parts of the marketplace, however, much of the data collected within the oil and gas sector tends to be discarded, ignored, or analyzed in a very cursory way. This viewpoint examines existing data management practices in the upstream oil and gas industry, and compares them to practices and philosophies that have emerged in organizations that are leading the way in Big Data. The comparison shows that, in companies that are widely considered to be leaders in Big Data analytics, data is regarded as a valuable asset—but this is usually not true within the oil and gas industry insofar as data is frequently regarded there as descriptive information about a physical asset rather than something that is valuable in and of itself. The paper then discusses how the industry could potentially extract more value from data, and concludes with a series of policy-related questions to this end.

Integrating engineering model eliciting activities in elementary school curricula

This paper argues for a future-oriented, inclusion of Engineering Model Eliciting Activities (EngMEAs) in elementary mathematics curricula. In EngMEAs students work with meaningful engineering problems that capitalise on and extend their existing mathematics and science learning, to develop, revise and document powerful models, while working in groups. The models developed by six groups of 12-year students in solving the Natural Gas activity are presented. Results showed that student models adequately solved the problem, although student models did not take into account all the data provided. Student solutions varied to the extent students employed the engineering context in their models and to their understanding of the mathematical concepts involved in the problem. Finally, recommendations for implementing EngMEAs and for further research are discussed.