Evaluating the sources and impact of new east coast gas supply

2020 ◽  
Vol 60 (2) ◽  
pp. 464
Author(s):  
Daniel C. Levy
Keyword(s):  
East Coast ◽  
Average Duration ◽  
Medium Term ◽  
Viable Option ◽  
Short Term ◽  
Technical Evaluation ◽  
Gas Market ◽  
Well Level ◽  
Gas Supply

Rystad Energy has conducted a well level supply side study for the Australian east coast gas market, quantifying the widely expected supply shortfall and its timing. This paper presents these findings, along with an economic and technical evaluation of new sources of supply relief and their potential impacts on the market balance. The study suggests the east coast has adequate gas supply to meet demand until 2024, with an average excess of 73 billion cubic feet (Bcf) per annum over this period. However, in 2025 the market will shift to under-supply, starting at 93 Bcf in 2025 and increasing to over half a trillion cubic feet by 2030. Sufficient supply in the short term does not warrant complacency. With the average duration between discovery and first gas for the region being 7.1 years since 1990, even if new (traditional) supply is discovered in 2020, the market will still be undersupplied for at least 3 years. We have identified the four most likely sources of supply relief for the market, each with their own merits, difficulties and development timelines. These new sources include the Beetaloo Sub-basin shales of the Northern Territory, undeveloped coal seam gas acreage, electrifying liquefied natural gas (LNG) export facilities to preserve in-field usage, and finally, LNG importation. A combination of at least two of these sources is required to balance the east coast gas market to 2030. Of the options, LNG importation is the most viable to stave off undersupply in the medium term (3 to 7 years). While Beetaloo Sub-basin shale gas appears the most viable option for secure, long-term supply relief.

Evolution of the east coast gas market

2018 ◽  
Vol 58 (2) ◽  
pp. 513
Author(s):  
Philip Byrne
Keyword(s):  
East Coast ◽  
Regulatory Frameworks ◽  
The Us ◽  
Major Transition ◽  
Gas Market ◽  
Gas Fields ◽  
Eastern Seaboard ◽  
Gas Supply ◽  
Australian Industry

This extended abstract reviews how the east coast gas market is managing the major transition from being a ring-fenced domestic market to being part of an interconnected global trading market, and what still needs to be done to rebalance after half a decade of disruption. The east coast gas market has a great future ahead of it, but only if Australia acts quickly to open up access to new gas supply sources as existing gas fields mature and decline. The presence of a global liquefied natural gas (LNG) supply market on the east coast now provides an incentive for gas producers to invest in new provinces and new plays at a scale the domestic gas market could not have supported on its own. This can only be good for competition in the east coast gas market over the medium to long term, and potentially open up enormous supplies for the growth of Australian industry, akin to the US shale gas revolution. To make the most of the resources and infrastructure we now have on the eastern seaboard, there is a role for governments to play in ensuring access to resources and providing stable, coordinated, robust energy policy and regulatory frameworks that attract investment in further growth in the gas sector, the benefits of which will flow on to Australian industry more generally.

LNG demand: do we have enough gas? Our gas prices? Our gas future? Somebody help!

2015 ◽  
Vol 55 (2) ◽  
pp. 494 ◽  
Author(s):  
Olumide Adisa
Keyword(s):  
East Coast ◽  
Comprehensive Analysis ◽  
Market Forces ◽  
Financial Constraint ◽  
Demand Forecast ◽  
Market Models ◽  
Term Contract ◽  
Gas Market ◽  
Gas Supply

These are interesting times for the eastern Australian gas market with Liquefied Natural Gas (LNG) projects coming online. The previously steady and long-term contract market for domestic gas supply on the east coast will be subject to market forces that are in part determined on the global stage. How will the market respond to these changes? The answer requires a comprehensive analysis of several scenarios and sensitivities around market models, as well as sophisticated modelling to capture these possibilities. This requires a tool that allows detailed modelling of the physical delivery of gas from producing fields, through pipelines and storages (including linepack) to demand points, with the capability to model any physical/financial constraint along the supply chain. The future lies in these scenarios and sensitivities. Employing a model developed through PLEXOS® gas module, this extended abstract analyses the effect of LNG on the domestic gas prices and supply in the short-to-medium and long-term. To establish any potential risk of gas shortage or particularly high prices, an analysis of the market was carried out from 2014-2023. Running several sensitivities on the demand forecast in this period, LNG effects on the market operations are examined.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

Sampling estuarine copepods at different scales and resolutions: a study in Rio de la Plata

2018 ◽  
Vol 99 (5) ◽  
pp. 1059-1064 ◽  
Author(s):  
Sourav Paul ◽  
Danilo Calliari
Keyword(s):  
Sampling Effort ◽  
Rio De La Plata ◽  
Population Densities ◽  
Medium Term ◽  
Short Term ◽  
Río De La Plata ◽  
La Plata ◽  
Resource Limitations ◽  
Paracalanus Parvus

AbstractIn the Rio de la Plata salinity, temperature, chlorophyll a (chl a), and densities (ind. m−3) of the copepods Acartia tonsa and Paracalanus parvus were measured from January to November in 2003 by following a nested weekly and monthly design. Such sampling yielded two separate datasets: (i) Yearly Dataset (YD) which consists of data of one sampling effort per month for 11 consecutive months, and (ii) Seasonal Weekly Datasets (SWD) which consists of data of one sampling effort per week of any four consecutive weeks within each season. YD was assumed as a medium-term low-resolution (MTLR) dataset, and SWD as a short-term high-resolution (STHR) dataset. The hypothesis was, the SWD would always capture (shorter scales generally captures more noise in data) more detail variability of copepod populations (quantified through the regression relationships between temporal changes of salinity, temperature, chl a and copepod densities) than the YD. Analysis of both YD and SWD found that A. tonsa density was neither affected by seasonal cycles, nor temporal variability of salinity, temperature and chl a. Thus, compared to STHR sampling, MTLR sampling did not yield any further information of the variability of population densities of the perennial copepod A. tonsa. Analysis of SWD found that during summer and autumn the population densities of P. parvus had a significant positive relationship to salinity but their density was limited by higher chl a concentration; analysis of YD could not yield such detailed ecological information. That hints the effectiveness of STHR sampling over MTLR sampling in capturing details of the variability of population densities of a seasonal copepod species. Considering the institutional resource limitations (e.g. lack of long-term funding, manpower and infrastructure) and the present hypothesis under consideration, the authors suggest that a STHR sampling may provide useful complementary information to interpret results of longer-term natural changes occurring in estuaries.

scholarly journals Towards the best future for humankind: ZIPAR whitepaper.

2018 ◽  
Author(s):  
Marko Kovic ◽  
Christian Caspar ◽  
Adrian Rauchfleisch
Keyword(s):  
Medium Term ◽  
Short Term ◽  
Profound Impact ◽  
Future Challenges ◽  
The Way

Humankind is facing major challenges in the short-term, medium-term, and long-term future. Those challenges will have a profound impact on humankind’s future progress and wellbeing. In this whitepaper, we outline our understanding of humankind’s future challenges, and we describe the way in which we work towards identifying as well as managing them. In doing so, we pursue the overall goal of ZIPAR: We want to make the best future for humankind (ever so slightly) more probable.

scholarly journals Intralymphatic immunotherapy for allergic rhinoconjunctivitis: a systematic review and meta-analysis

2021 ◽  
Vol 0 (0) ◽  
pp. 0-0
Author(s):  
M.P. Hoang ◽  
K. Seresirikachorn ◽  
W. Chitsuthipakorn ◽  
K. Snidvongs
Keyword(s):  
Systematic Review ◽  
Meta Analysis ◽  
Specific Ige ◽  
Allergic Rhinoconjunctivitis ◽  
Medium Term ◽  
Short Term ◽  
Intralymphatic Immunotherapy ◽  
Specific Igg4 ◽  
Igg4 Level

BACKGROUND: Intralymphatic immunotherapy (ILIT) is a new route of allergen-specific immunotherapy. Data confirming its effect is restricted to a small number of studies. METHODOLOGY: A systematic review with meta-analysis was conducted. The short-term (less than 24 weeks), medium-term (24-52 weeks), and long-term (more than 52 weeks) effects of ILIT in patients with allergic rhinoconjunctivitis (ARC) were assessed. The outcomes were combined symptom and medication scores (CSMS), symptoms visual analog scale (VAS), disease-specific quality of life (QOL), specific IgG4 level, specific IgE level, and adverse events. RESULTS: Eleven randomized controlled trials and 2 cohorts (483 participants) were included. Compared with placebo, short term benefits of ILIT for seasonal ARC improved CSMS, improved VAS and increased specific IgG4 level but did not change QOL or specific IgE level. Medium-term effect improved VAS. Data on the long-term benefit of ILIT remain unavailable and require longer term follow-up studies. There were no clinical benefits of ILIT for perennial ARC. ILIT was safe and well-tolerated. CONCLUSION: ILIT showed short-term benefits for seasonal ARC. The sustained effects of ILIT were inconclusive. It was well tolerated.

scholarly journals A Study of Multi-Scale Relationship Between Investor Sentiment and Stock Index Fluctuation Based on the Analysis of BEMD Spillover Index

2021 ◽  
Vol 9 (4) ◽  
pp. 399-420
Author(s):  
Weiguo Chen ◽  
Shufen Zhou ◽  
Yin Zhang ◽  
Yi Sun
Keyword(s):  
Stock Price ◽  
Composite Index ◽  
Investor Sentiment ◽  
Stock Index ◽  
Financial Industry ◽  
Medium Term ◽  
Short Term ◽  
Frequency Components ◽  
Original Scale

Abstract According to behavioral finance theory, investor sentiment generally exists in investors’ trading activities and influences financial market. In order to investigate the interaction between investor sentiment and stock market as well as financial industry, this study decomposed investor sentiment, stock price index and SWS index of financial industry into IMF components at different scales by using BEMD algorithm. Moreover, the fluctuation characteristics of time series at different time scales were extracted, and the IMF components were reconstructed into short-term high-frequency components, medium-term important event low-frequency components and long-term trend components. The short-term interaction between investor sentiment and Shanghai Composite Index, Shenzhen Component Index and financial industries represented by SWS index was investigated based on the spillover index. The time difference correlation coefficient was employed to determine the medium-term and long-term correlation among variables. Results demonstrate that investor sentiment has a strong correlation with Shanghai Composite Index, Shenzhen Component Index and different financial industries represented by SWS index at the original scale, and the change of investor sentiment is mainly influenced by external market information. The interaction between most markets at the short-term scale is weaker than that at the original scale. Investor sentiment is more significantly correlated with SWS Bond, SWS Diversified Finance and Shanghai Composite Index at the long-term scale than that at the medium-term scale.

Meeting demand in a new era of east coast gas supply

2019 ◽  
Vol 59 (2) ◽  
pp. 686
Author(s):  
Will Pulsford
Keyword(s):  
East Coast ◽  
New Era ◽  
Demand Growth ◽  
Historical View ◽  
Operational Life ◽  
Gas Market ◽  
The Government ◽  
Gas Fields ◽  
Project Life ◽  
Gas Supply

Historically LNG projects have been established to monetise large gas finds in remote areas with little existing gas demand. The development of gas supply to the LNG project generally stimulated demand growth in the domestic gas market. As the supplying fields depleted, the LNG projects faced competition with domestic producers for declining gas supplies, but this was late in the project life when LNG plant capital had already been recovered. Recently, LNG export projects have been established within existing mature gas markets, most notably in Australia and North America. These plants now face competition with domestic gas consumers for access to feed gas from the beginning of their operational life when strong revenue has the greatest impact on the return earned on capital invested, with the greatest stress felt in Australia. This paper considers the underlying causes of domestic price rises experienced in Australia following the start-up of LNG export supplied from gas fields linked to the domestic market and the response by both plant developers/operators and the government. This historical view is used to inform forecasts of how the east coast gas market will react to the interplay between domestic and LNG plant demand, declining Bass Strait production, maturing CSG operations, LNG imports and completion of the Northern Gas Pipeline. In particular the ability of gas supply and pipeline capacity to meet the strongly seasonal domestic demand in Victoria and to a lesser extent NSW will be examined, together with the linkage to counter-cyclical seasonal demand for LNG from the Queensland LNG export plants in the key north Asian markets.

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