Coal Seam Gas Industry Research Articles

Iman Peoples Water Sovereignty: Extractive Industries in Central Queensland

The coal seam gas (CSG) industry has rapidly expanded in the Great Artesian Basin (GAB) over the past two decades, significantly impacting groundwater ecosystems and Indigenous communities that rely on these water sources for cultural, spiritual, and practical purposes. This article examines the intersection of Water Sovereignty, extractive industries, and key issues such as Social License to Operate (SLO), Indigenous Peoples' rights, informed consent, and the legal doctrine of Aqua Nullius. Through an Indigenous perspective, this research illuminates the multifaceted dimensions of water governance and the complexities that arise from the interactions between Indigenous stakeholders, industries, and regulatory frameworks. Through interviews with Elders of the Iman Peoples in central Queensland, the research highlights how current SLO frameworks neglect Free, Prior, and Informed Consent (FPIC) and fail to uphold ethical standards necessary for protecting Indigenous cultural continuity. Water security is a primary concern, with Traditional Owners emphasizing the need to safeguard surface and groundwater for future generations. This research informs policy at local and national levels while contributing to global discussions on Indigenous sovereignty, environmental justice, and cultural heritage protection. It calls for integrating Indigenous knowledge and rights into water governance, promoting respect, equity, and sustainability in managing natural resources.

Rehabilitation challenges for the onshore coal seam gas sector in Australia

The development of conventional oil and gas reserves followed by valorisation of coal seam gas (CSG) reserves in Queensland has seen the installation of over 16,000 wells. These wells are accompanied by thousands of kilometres of gathering lines, compression and water treatment facilities and transmission pipelines. The major rehabilitation challenges for the industry result less from technical challenges but rather from the sheer scale of the task. At present, the CSG industry continues to grow, and few of the CSG wells (other than exploration and appraisal wells) have yet reached the end of their life. The US experience presents a cautionary tale for adequate financial provisioning to mitigate the risks of orphaned wells. Secondly, the rehabilitation of multiple small parcels of land such as drill pads presents logistical challenges. Strategies to aggregate parcels for relinquishment will be required if the industry is to avoid thousands of individual land parcel evaluations at the time of relinquishment. These two rehabilitation challenges will be explored, risks for the industry and community assessed, and a call made for industry and government to work collaboratively to ensure an orderly and responsible rehabilitation program becomes an integral part of ongoing operations.

Technology opportunity analysis using hierarchical semantic networks and dual link prediction

Technology opportunity analysis using network analysis and link prediction has attracted the interest of both academia and industry. However, there are several unresolved issues with existing research, such as a lack of semantic relationships between nodes in a single-layer network, analyzing current technology trends rather than predicting future technology developments based on existing edges in a semantic network, and ignoring evaluation criteria for technology opportunity identification based on a single link prediction. This study proposes a new systematic methodology to address these issues and identify technology opportunities using a hierarchical semantic network and dual link prediction. The proposed methodology consists of three modules: 1) constructing the hierarchical semantic network based on SAO structures extracted from patents; 2) identifying technology opportunities in this semantic network through probabilistic-based link prediction; and 3) evaluating these opportunities via similarity-based link prediction. The viability and usefulness of the proposed methodology is proved by empirical analysis of the exploitation technology in the coal seam gas (CSG) industry. The results show that the hierarchical semantic network, including semantic and co-word relationships, can improve prediction accuracy. The dual link prediction can not only automatically identify technology opportunities with semantics, but also evaluate them to narrow down the problem-solving according to the novelty criteria. This study represents a contribution to existing research on technology opportunity analysis by integrating the hierarchical semantic network and dual link prediction.

Coal seam gas extraction and related landscape changes in the agricultural production area of Western Downs (Queensland, Australia)

In Australia, coal seam gas (CSG) could help to meet growing demands for cleaner alternatives to coal, thus making it important for the energy transition. By developing such an industry, not only does an energy resource become available, but large water volumes are also extracted, especially in the first stages of production. The purpose of this paper is to analyze the impacts of a large-scale coal seam gas industry on the landscape. Specifically, the research intends to explore how coal seam gas has changed the landscape of the agricultural production area in Western Downs, Queensland. In this Australian State, the water associated with coal seam gas production is considered an asset and the legislation in force encourages beneficial use of such resources. Comparison of aerial pictures highlights how, in a short timeframe, the local rural landscape has changed considerably due the extensive development of unconventional gas resources. The coal seam gas industry has provided the host region with opportunities for local development, but the issue of the sustainability of these new mining and non-mining activities still needs to be properly addressed.

Air quality monitoring based on chemical and meteorological drivers: Application of a novel data filtering-based hybridized deep learning model

Particulate matter (PM) or particle pollution include the tiny particles of dust and fly ash particles are expelled from coal-burning power plants. Coal combustion is an extremely prevalent source of air pollution, and resulting PM has substantial impacts on human health, especially in industrial zones. This paper aims to design a novel hybrid deep learning framework based on long short-term memory (LSTM) integrated with a two-stage data filtering technique to accurately predict the air quality indices (i.e., PM2.5 and PM10) in a chosen study region ‘Miles Airport, Queensland,’ that meets the needs of the coal seam gas industry in Australia. The data used to construct the novel hybrid two-stage data filtering technique based on LSTM comprising of six meteorological parameters (i.e., wind direction, wind speed, air temperature, relative humidity, solar radiation, and rainfall) and two environmental factors (i.e., ozone and total suspended particulate). In the first stage, two robust feature selection methods, namely, extreme gradient boosting (XGBoost) and the classification and regression tree (CART) approach, were adopted to explore the most significant predictors. Then in the second stage, the best subset regression (BSR) technique is utilized to determine the best subsets input combinations i.e., C1, C2, and C3 based on several particular metrics. The three BSR-based input combinations were employed in the LSTM model to estimate the PM2.5 and PM10. Furthermore, to validate the main hybrid framework, two advanced machine learning (ML) methods (i.e., LightGBM and ridge kernel regression (KRR)) and two traditional ML methods (i.e., Adaptive neuro-fuzzy inference system (ANFIS) and multilayer perceptron neural network (MLP)) were hybridized with the multi-level data filtering strategy by examining the optimal input combinations. Several statistical metrics, graphical tools, and diagnostic analyses evaluated the hybrid models. The outcomes of the PM2.5 simulation based on 2375 data samples showed that the LSTM-C3 containing all the selected predictors yielded the most promising accuracy, followed by the LightGBM-C3 and MLP-C3 models. On the other hand, the simulation of PM10 distribution demonstrated that the LSTM-C3 was superior to other models, followed by the KRR-C3 and LightGBM-C3 models.

Dewatering Optimization of Coal Seam Gas Production Wells: A Review and Future Perspectives

Coal seam gas (CSG), as a type of unconventional gas resource, is playing an increasingly important role in energy generation and transition to renewables, as the world takes action to meet the climate change challenge. To produce CSG, the reservoir first needs to be dewatered, but unreasonable dewatering rates can lead to various unfavorable outcomes, such as a reduction in productivity, capillary trapping, borehole instability, excessive coal fines generation, and frequent production well workovers due to particle-induced damage to pumps. Determining the optimum dewatering strategy is currently in a very primitive stage of development, and CSG operators rely on empirical and instinctive approaches to deal with this problem. This paper conducts a comprehensive literature review to (i) explain why dewatering rate optimization remains a great challenge for the CSG industry, (ii) illustrate the complexity and nature of the issue through a conceptual model, and (iii) investigate four key factors that dominate the dewatering optimization design, borehole stability, stress-dependent permeability, gas–water two-phase flow, and generation of coal fines. A detailed summary of current dewatering optimization practices in CSG reservoirs is then presented, focusing on the employed theories, methodology, key learnings, and limitations of each practice. Finally, the remaining knowledge gaps are highlighted, and recommendations for further future work are provided.

Discrete Bubble Flow in Granular Porous Media via Multiphase Computational Fluid Dynamic Simulation

The coal seam gas industry has raised public concerns about the potential risk of groundwater contamination, where gases leaked from coal seams are thought to pollute groundwater. However, the basic principles and controlling parameters for gas seepage from deep ground formations to the ground surface have not been fully understood. As a possible mechanism for gas transport in the subsurface environment, discrete bubble flow was previously investigated using laboratory experiments by Ma et al. (Water Resour. Res, 2015, 51 (6), 4359–4373). This study developed a multiphase computational fluid dynamic (CFD) model to simulate discrete bubbly flow in a two-dimensional granular porous media at the pore scale. Following the experimental setup from Ma et al. (Water Resour. Res, 2015, 51 (6), 4359–4373), a “point source” with preset bubble fluxes was specified in a simulating domain representing the flume size in the earlier experiments. There were around 7,000 granular particles within this domain to model the porous media. This numerical model was validated by conserving the gas mass in the simulating domain. The simulation results provide more physical insights into complex bubble transport behaviour in porous media through specific plume parameters. The breakthrough time of the bubble plume and the cross-sectional averaged velocity of ambient pore water flow were manifested to be proportional to the gas release rates in the logarithmic scales. Also, the bubble plume width was also observed to be proportional to the gas release rates. Moreover, the gas distribution on the top boundary could be observed. The outcomes were further tested against the scaling solutions proposed by Ma et al. (Water Resour. Res, 2015, 51 (6), 4359–4373) with disagreements. The limitations of this multiphase computational fluid dynamic model were finally discussed.

Concurrent 18. Presentation for: Simplicity and digitalisation – using GIS to reduce design and operation costs in gas gathering systems

Presented on Wednesday 18 May: Session 18 With the drive to continually reduce the cost of connecting new Coal Seam Gas (CSG) wells, Worley developed new methodologies using commercial off-the-shelf software, and custom development, to automate engineering and improve operational efficiency for pipelines and gathering systems. One of the recent developments is an automated and data-driven approach to produce Gathering Network Diagrams (GNDs), an alternative to conventional Piping & Instrumentation Diagrams (P&IDs). On a recent project, Worley implemented a GND workflow capable of showing ~40 wells per A3 deliverable in conformance with intelligent drafting standards. This reduced ~2000 as-built P&IDs to ~200 GNDs (and associated standard details) with a significant improvement in quality and usability. Prior to generation, a site walkdown was conducted to address mismatches between as-built P&IDs, Geographic Information System (GIS) and what was actually installed at site. Our client now has a suite of GNDs that match the installed asset and are produced from the corporate GIS database, essentially their Digital Twin. Worley then focused on integrating flow assurance software with GIS allowing data-driven execution of future design packages to be incorporated into the master GNDs. We successfully integrated with PipeSIM and GAP, allowing for seamless transfer of hydraulic inputs from GIS to the modelling software. Engineers can now synchronise the confirmed process design back to GIS without producing red-lined and/or CAD-drafted deliverables which greatly improves efficiency and reduces human error. This paper demonstrates how digital automation can be applied to the CSG industry to significantly reduce cost and schedule whilst enabling data-driven operational efficiencies. To access the presentation click the link on the right. To read the full paper click here

Simplicity and digitalisation – using GIS to reduce design and operation costs in gas gathering systems

With the drive to continually reduce the cost of connecting new Coal Seam Gas (CSG) wells, Worley developed new methodologies using commercial off-the-shelf software, and custom development, to automate engineering and improve operational efficiency for pipelines and gathering systems. One of the recent developments is an automated and data-driven approach to produce Gathering Network Diagrams (GNDs), an alternative to conventional Piping & Instrumentation Diagrams (P&IDs). On a recent project, Worley implemented a GND workflow capable of showing ~40 wells per A3 deliverable in conformance with intelligent drafting standards. This reduced ~2000 as-built P&IDs to ~200 GNDs (and associated standard details) with a significant improvement in quality and usability. Prior to generation, a site walkdown was conducted to address mismatches between as-built P&IDs, Geographic Information System (GIS) and what was actually installed at site. Our client now has a suite of GNDs that match the installed asset and are produced from the corporate GIS database, essentially their Digital Twin. Worley then focused on integrating flow assurance software with GIS allowing data-driven execution of future design packages to be incorporated into the master GNDs. We successfully integrated with PipeSIM and GAP, allowing for seamless transfer of hydraulic inputs from GIS to the modelling software. Engineers can now synchronise the confirmed process design back to GIS without producing red-lined and/or CAD-drafted deliverables which greatly improves efficiency and reduces human error. This paper demonstrates how digital automation can be applied to the CSG industry to significantly reduce cost and schedule whilst enabling data-driven operational efficiencies.

Identification of Breakout Behind Casing: Methodology to Obtain Openhole-Equivalent Caliper Measurements Through Slotted Liner Using the Density Tool

Growth in the coal seam gas industry in Queensland, Australia, has been rapid over the past 15 years, with greater than USD 70 billion invested in three liquified natural gas export projects supplied by produced coal seam gas. Annual production is of the order of 40 Bscm or 1,500 PJ, with approximately 80% of this coming from the Jurassic Walloon Coal Measures of the Surat Basin and 20% from the Permian Coal Measures of the Bowen Basin. The Walloon Coal Measures are characterized by multiple thin coal seams making up approximately 10% of the total thickness of the unit. A typical well intersects 10 to 20 m of net coal over a 200- to 300-m interval, interbedded with lithic-rich sandstones, siltstones, and carbonaceous mudstones. The presence of such a significant section of lithic interburden within the primary production section has led to a somewhat unusual completion strategy. To maximize connection to the gas-bearing coals, uncemented slotted liners are used; however, this leaves fluid-sensitive interburden exposed to drilling, completion, and produced formation fluids over the life of a well. External swellable packers and blank joints are therefore used to isolate larger intervals of interburden and hence minimize fines production. Despite these efforts, significant fines production still occurs, which leads to the failure of artificial lift systems and the need for expensive workovers or lost wells. Fines production has major economic implications, with anecdotal reports suggesting up to 40% of progressive cavity pump artificial lift systems in Walloon Coal Measures producers may be down at any one time. The first step in solving this problem is to identify the extent and distribution of fines production. The wellbore completion strategy above, however, precludes the use of mechanical calipers to identify fines-production-related wellbore enlargement. A new caliper-behind-liner technique has therefore been developed using a multiple-detector density tool. Data from the shorter-spacing detectors are used to characterize the properties of the liner as well as the density of the annular material. This is particularly important to evaluate as the annulus fill varies between gas, formation water, drilling and completion fluids, and accumulated fines. The longer-spacing detector measurements are then used in conjunction with pre-existing openhole formation density measurements to determine the thickness of the annulus, and hence hole size, compensating for liner and annulus properties.

Coal seam gas industry methane emissions in the Surat Basin, Australia: comparing airborne measurements with inventories.

Coal seam gas (CSG) accounts for about one-quarter of natural gas production in Australia and rapidly increasing amounts globally. This is the first study worldwide using airborne measurement techniques to quantify methane (CH4) emissions from a producing CSG field: the Surat Basin, Queensland, Australia. Spatially resolved CH4 emissions were quantified from all major sources based on top-down (TD) and bottom-up (BU) approaches, the latter using Australia's UNFCCC reporting workflow. Based on our TD-validated BU inventory, CSG sources emit about 0.4% of the produced gas, comparable to onshore dry gas fields in the USA and The Netherlands, but substantially smaller than in other onshore regions, especially those where oil is co-produced (wet gas). The CSG CH4 emission per unit of gas production determined in this study is two to three times higher than existing inventories for the region. Our results indicate that the BU emission factors for feedlots and grazing cattle need review, possibly requiring an increase for Queensland's conditions. In some subregions, the BU estimate for gathering and boosting stations is potentially too high. The results from our iterative BU inventory process, which feeds into TD data, illustrate how global characterization of CH4 emissions could be improved by incorporating empirical TD verification surveys into national reporting.This article is part of a discussion meeting issue ‘Rising methane: is warming feeding warming? (part 1)’.

A novel workflow based on physics-informed machine learning to determine the permeability profile of fractured coal seams using downhole geophysical logs

Permeability is arguably the most important parameter in gas pre-drainage and production practices in coal mining and coal seam gas industries. In fractured coal seams, permeability can significantly vary both laterally and vertically. Knowing the vertical variation of permeability in relatively thick coal seams is important in optimizing inseam pre-drainage design in underground coal mining and improving reservoir management and completion design efficiency in the coal seam gas industry. Existing empirical/statistical coal permeability models often yield poor results in estimating permeability profiles where there is significant variation in the characteristics of coal fracture systems. This study presents a novel workflow for evaluating the permeability profile of naturally fractured thick coal seams using downhole geophysical logging data. The workflow combines physics-based simulation, laboratory experiments, and a data-driven machine learning approach for estimating the permeability profile. As part of this workflow, several coal specimens from the study coal seam are first tested under different stresses to measure their permeability, density, and ultrasonic responses. Numerical simulation of the Navier-Stokes fluid flow and elastic wave propagation was then performed on a range of constructed coal block geometries with various fracture densities. In the next step, a physics-informed, neural network-based model (PINN) was trained using the data obtained from both laboratory experiments and numerical simulation. The model inputs included density, as well as compressional and shear wave velocities of the coal seam and its output is the trend of permeability variation across the coal seam interval. In-situ permeability measurement at a certain depth (e.g., from well pressure testing) was then used to convert the permeability trend to the entire permeability profile of the coal seam along its interval. The performance of the proposed workflow was finally evaluated using a suite of downhole geophysical logging data from a borehole intersecting two coal seams in an eastern Australian basin. This study demonstrates that the proposed workflow is relatively simple to implement and yet accurate for evaluating the permeability profile of a coal seam across its interval.

Impact of turbidity, hydraulic retention time, and polarity reversal upon iron electrode based electrocoagulation pre-treatment of coal seam gas associated water

The applicability of iron (steel) electrodes in a continuous electrocoagulation (EC) process to treat coal seam gas (CSG) associated water comprising of a high level of turbidity (421 NTU) was studied. Key objectives were to understand the influence of turbidity upon water quality when hydraulic retention time (HRT) and polarity reversal time (PRT) were tested using a bench top EC unit. Extension of HRT promoted removal of dissolved alkaline earth ions (Ca 23 to 45 %; Mg 60 to 97 %; Ba 25 to 54 %; Sr 10 to 19 %), silicates (90 to 93 %) and boron (8 to 12.7 %); whereas turbidity was optimally reduced at a HRT value of 30 s (98.5 %). Turbidity particularly promoted magnesium removal which may be due to destabilization of the clay suspension. However, clay particles also inhibited the rate of floc settling. The greater performance of EC with increasing HRT was achieved at a cost of increased consumption of electrodes and electricity. PRT reduced power consumption with an optimal value being at least 5 min (4.12 to 3.88 kWh/kL for PRT values of 1 and 5 min, respectively). Removal rates of dissolved species were not greatly influenced by PRT. Surface passivation at low PRT values produced higher amounts of hydrogen gas which caused the flocs to float. In summary, HRT, PRT, turbidity level and solution composition were critical parameters in relation to the potential use of EC in the CSG industry.

Numerical investigation of the effects of proppant embedment on fracture permeability and well production in Queensland coal seam gas reservoirs

This paper introduces the development of a new predictive model in support of proppant injection in naturally fractured coal seam gas (CSG) reservoirs. In the proposed model, the finite element method (FEM) is used for the prediction of proppant embedment and elastoplastic deformation of the coal. The lattice Boltzmann method (LBM) is applied to the modelling of fluid flow through propped fractures, in which the modified partially saturated method (MPSM) is implemented to characterise the fluid–solid interactions. Permeability diagrams of the fractures are then generated as functions of particle packing ratio and effective stress. Finally, these results are incorporated into a radial Darcy flow analytical solution to predict the productivity index of the CSG wells under various proppant injection pressures and cleat compressibilities. The developed model is applied to selected coal samples from the Bowen and Surat Basins in Queensland, Australia. Modelling results indicate that proppant injection leads to increased fracture permeabilities and enhanced well productivity indexes. The elastoplastic deformation results in smaller permeability increase and less production enhancement when compared to the traditional linear elastic models. Although focused on Australian coals, the developed workflow can be broadly applicable to the assessment of potential stimulation efficacy in other unconventional reservoirs. In addition, a better understanding and implementation of the proppant injection scheme can effectively improve the post-fracturing performance, particularly in low-permeability coal intervals, which benefits the CSG industry.

The golden-tailed gecko: a disturbance-tolerant species in a fragmenting environment

Abstract Context In central Queensland, Australia, the development of a coal-seam gas (CSG) industry is creating additional fragmentation of landscapes consisting of woodland and open forest that are already highly fragmented. Aims To assess the response to fragmentation of Strophurus taenicauda (golden-tailed gecko). The species is ‘near threatened’ in Queensland. Methods Occurrence and abundance were examined across three categories of patch size – small (≤10 ha), medium (10–100 ha) and large (≥100 ha) – across three geographic areas of the species’ range. Minimal impact (i.e. sighting only) active searches for geckos were conducted at night. A minimum of three replicate sites of each patch size category was surveyed in each of the three geographic areas. Eleven additional patches (each <4 ha and located in the southern geographic area) were surveyed to investigate how size and spatial isolation of small patches affected occurrence and abundance of S. taenicauda. At all sites a standardised set of 22 habitat variables was collected, and the presence of other species of arboreal gecko was recorded. Key results The species was located across patches of all sizes, including those as small as 1.11 ha. It was also located opportunistically in the matrix among patches and occurred in isolated trees within an urban area. The abundance of another commonly occurring arboreal gecko, Gehyra dubia (dubious dtella), was negatively correlated with S. taenicauda abundance in small patches. The most important habitat variable for S. taenicauda was average basal area of trees. As this increased, especially above 5.7 m2 ha−1, it was more likely to be present. When considering only the small patches, the main factors influencing presence and abundance of S. taenicauda were the average basal area of Callitris glaucophylla (white cypress) and grazing (negligible or absent). Conclusions Strophurus taenicauda is a species that is tolerant of disturbance and can persist in fragmented habitat, provided the fragments have adequate cover of white cypress. Implications The species appears to be resilient to the current level of CSG development within its geographic range.

Two companies collaborating to make real safety improvements: Arrow Energy, Shell QGC and the Queensland coal seam gas industry

Arrow Energy (Arrow) and Shell QGC have strong collaboration on safety, contributing to direct improvements. This collaboration represents over 50% of exposure manhours for the Queensland CSG sector. The following important trends were apparent. Personal safety injuries largely with low severity consequence – a large proportion were at, or near, full incident severity potential. First aid and recordable injuries predominantly caused by slip/trips, strains, caught-between, line of fire/tools/falls from height. High potential incidents (HiPos), or serious near misses, largely have different causes including: land transport; process safety; drops/heights; electrical; excavation; automated equipment. Recordable incidents resulted predominantly (over 75%) from routine, low risk activities (tasks not controlled under permit/critical control), and most involved workers conducting activities when working alone (75% or more). These critical insights differ from the industry accepted safety paradigm, whereby a greater number of low-consequence level incidents will likely have a direct numerical ratio relationship to more serious incidents (HiPos or fatalities), known as Heinrich’s triangle. This is because the causes of the low vs high severity incidents are different for the Arrow and Shell QGC datasets; addressing the causes of low severity incidents will not automatically in turn address the causes of the high severity incidents. Both companies have demonstrated marked improvements in safety performance of recordables when focused improvement programs were applied, and show that improvements to the total recordable case frequency rate (TRCFR) and low severity incidents should not be seen as impenetrable across the industry. Sharing of insights and trends then resulted in these organisations sharing their improvement strategies, most notably the Assist and Assure Program, and the Contractor Partnerships Program. Arrow was able to purposely address these trends, noting good improvements (TRCFR from 3.5 to 1.8); and drilling-related recordable incidents from eight in 2018 to just one in 2019, and 2020. In Shell QGC, the projects (construction) area saw a dramatic improvement with 583 days TRC-free (7 July 2017–11 January 2019); the direct result of leadership programs, workforce engagement and partnership programs with their two main construction contractors. The Assist and Assure Program in the drilling area, saw the recordable incidents improve to just two in 2017, compared with seven (2015) and six (2016).

Understanding social licence to operate for onshore gas development: How the underlying drivers fit together

Abstract Unconventional gas plays a significant role in transitioning to low carbon energy sources; however, its extraction is socially contested, and proponents increasingly face social licence issues. Explaining social acceptance for unconventional gas is complex, with multiple factors at play. This study uses comprehensive statistical modelling to explain social acceptance of a local coal seam gas (CSG) development in its pre-approval phase. Using a representative sample of 400 randomly selected residents in rural Australia, the statistical model explained 83% of variation in acceptance and measured interdependencies between eight factors determining acceptance. These factors were: effects from industry activity (perceived impacts and perceived benefits); distributional fairness (the spread of costs and benefits); the relational aspects between the host community and industry (perceptions of trust in industry, relationship quality and procedural fairness); governance of the industry (informal and formal governance, and trust in governing bodies); and knowledge (of the local CSG industry). Results showed that perceived impacts were the main driver of acceptance, with perceived benefits, distributional fairness, and trust in industry the next most important determinants. Relationship quality and procedural fairness predicted trust. Industry knowledge was only a minor determinant of acceptance. Those ‘rejecting’ CSG development had starkly more negative perceptions of the underlying factors than those ‘supporting’. However, both had higher self-rated knowledge than those who felt ‘lukewarm’ about CSG. This highly predictive statistical model can be used by industry to direct their efforts at the most important drivers of acceptance, such as benefit sharing and addressing concerns about impacts, and for planning their community engagement and communication. Policy makers and regulators can use the model to guide their expectations of industry when assessing projects for approval, including building trust through effective community engagement. Moreover, the research suggests that information is best targeted at residents with ‘lukewarm’ or less established views, and points to the importance of providing them with specific information about important factors underlying social acceptance such as benefit sharing, impact mitigation, procedural fairness, and governance, rather than general industry information. These insights help create the necessary preconditions for establishing a social licence to operate for an onshore gas development.

A hybrid fracture-micropore network model for multiphysics gas flow in coal

Coal is not only a combustible black sedimentary rock, but also a source and reservoir rock for natural gas, known as coal seam gas (CSG). A thorough understanding of gas transport through coal matrix micropores as well as coal cleats is of significance in both mining and CSG industries. Multiple physical mechanisms are identified during gas flow in coal. However, there is not a comprehensive modelling framework, where multiscale flow behaviours with multiphysics are coupled. In this paper, we develop a hybrid Fracture-MicroPore Network Model (FM-PNM), coupling viscous gas flow in fractures and gas diffusion in coal matrix. This model includes multiphysics gas flow mechanisms in coal, including gas sorption, diffusion, slip flow, and compressibility. With FM-PNM, methane flowing through fractured coal is simulated to compute apparent permeability and gas flow rates, which are critical for CSG developments. Besides, the process of gas emission from coal over time is simulated, where desorption curves are obtained and analysed at different scenarios. This multiphysics FM-PNM gives a promising framework for studying gas flow in fractured coal, which can be integrated with other works to study different coal internal structures, flow models and stress conditions. This model has a wide range of applications, including prediction of greenhouse gas (GHG) emission of coal mines, ventilation design during mining, CSG development, and CO2 storage management.

Process simulation of high pH reverse osmosis systems to facilitate reuse of coal seam gas associated water

• First simulation of HERO process for CSG associated water treatment. • AqMB software comprehensively evaluated water treatment process options. • Identified major costs as acid consumption and power for air stripper. • Substantially improved economics by softening with sodium exchanged resin. This study has revealed the potential for a high pH reverse osmosis system to desalinate coal seam gas (CSG) (or coal bed methane (CBM)) associated water to facilitate beneficial reuse. To promote technology uptake in the CSG industry greater insight was required to understand the optimal water treatment plant design, the influence of solution composition, and process economics. Therefore, AqMB software was used to simulate the treatment of three CSG water compositions. Unit operations included: Filtration; Softening; Air Stripping; pH raising; and Reverse Osmosis. In all cases, the product water met reuse guidelines. However, process economics were less favourable as solution concentration increased. Hydrochloric acid regenerant for the cation resin was prohibitively expensive (up to A$17,554,687 per annum) and its use required air stripping to remove carbon dioxide formed in acidic conditions (power consumption 70 % of total process demand). Acid solutions also required large amounts of sodium hydroxide (up to A$4,546,682 per annum) to reach pH 10 wherein silica species were soluble. Use of a sodium chloride regenerated resin plus removal of the air stripping stage substantially improved economics to the point that the operational expenditure was predicted to be A$417 per ML of water treated; a value comparable to conventional reverse osmosis desalination units. AqMB software was advantageous in that it; simulated the entire water treatment process, not just reverse osmosis; generated robust predictions; and estimated operational costs. As an outcome, rapid interrogation of water treatment process designs was now possible.

The Anti-Coal and Anti-Coal Seam Gas Campaigns as Components of the Climate Movement in Australia: Responses to Corporate Hegemony

ABSTRACT In this paper we examine the climate-driven anti-coal and anti-coal gas organizations and activist campaigns in Australia, a country that is a major force in global coal production and trade, and a growing coal seam gas producer and exporter. The sociopolitical context for our analysis is the close nexus that has existed historically between the coal industry and the Australian state, as well the more recent relationship between the state and the burgeoning coal seam gas industry. We review an array of major anti-coal groups in Australia and assess not only their protest activities but – to the degree they have articulated them – their respective stances on climate justice and on capitalism as a primary driver of climate injustice. While a few groups within the anti-coal movement tend to be anti-capitalist in their orientation, most advocate a largely green social democratic position which calls for ecological modernization as a major climate change mitigation strategy, without addressing whether this is achievable within the current globally dominant political economy.