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Sand production is a problem that affects hydrocarbon production from unconsolidated sandstone reservoirs. Several factors, such as the strength of the reservoir, its lithification and cementation and reduction in pore pressure, may cause sand to be separated from the rock and transported by hydrocarbons to the well. Producing sand commonly causes erosion and corrosion of downhole and surface equipment, leading to production interruptions and sometimes forces operators to shut-in wells. Several different methods of sand control are available to reduce the impact of sand production. The reviewed papers suggest that the most suitable methods for unconsolidated sandstone reservoirs are stand-alone screens and gravel packs. Because of the cost and complexity of gravel packs, stand-alone screens are usually the first choice. These screens have different geometries, and selection of the most suitable screen depends on the particle size distribution of the grains in the formation and other reservoir and production parameters. A screen retention test, run in a laboratory with screen samples and typical sands, is often used to ensure that the screen is suitable for the reservoir. This paper reviews the main causes of sand production, the properties of unconsolidated sandstones that predispose reservoirs to sand production problems and the selection criteria for the most suitable mitigation method. The process of selecting a screen using experimental screen retention tests is reviewed, and the limitations of these tests are also discussed. Some numerical simulations of experimental tests are also reviewed, since this represents a very cost-effective alternative to laboratory experiments.

Oil and gas exploration gradually changes to the deep and complex areas. The quality of seismic data restricts the effective application of conventional time-frequency analysis technology, especially in the case of low signal-to-noise ratio. To address this problem, we propose a curvelet-based time-frequency analysis method, which is suitable for seismic data, and takes into account the lateral variation of seismic data. We first construct a kind of curvelet adapted to seismic data. By adjusting the rotation mode of the curvelet in the form of time skewing, the scale parameter can be directly related to the frequency of the seismic data. Therefore, the curvelet coefficients at different scales can reflect the time-frequency information of the seismic data. Then, the curvelet coefficients, which represent the dominant azimuthal pattern, are converted to the time-frequency domain. Since the curvelet transform is a kind of sparse representation for the signal, the screening process of the dominant coefficient masks most of the random noise, which enables the method to adapt for the low signal-to-noise ratio data. Results of synthetic and field data experiments using the proposed method demonstrate that it is a good approach to identify weak signals from strong noise in the time-frequency domain.

Fluvial sediments with multi-scale channels are difficult to model using classical two-point statistical methods, e.g. sequential indicator simulation (SIS) or object-based modelling (ObjM). Multiple-point statistics (MPS) has been used to generate facies, fracture and porosity distributions based on pattern statistics derived from training datasets. However, the ability of these three methods to reproduce channel geometry and continuity is not clear, especially when using differently spaced conditional data. This paper presents a case study to compare the application of these three methods in reproducing channels from a section of Amazon River based on two differently spaced conditional data sets. Results show that: the reproduction accuracy is similar between MPS and SIS; MPS provides the most connected channel facies (or most channel continuity) as compared to SIS and ObjM; and using a hand-drawn facies based on the sampling points yield a similar accuracy to that achieved by using the reality facies distribution as the training image. Finally, we conclude that the application of MPS does not significantly increase the reproduction accuracy when compared to SIS channel models; however, MPS can generate realistic models with respect to channel geometry and continuity.

Understanding the controls on coal seam distribution and geometry is fundamental for planning coal seam gas production. In the Jurassic Surat Basin of South East Queensland, Australia, the spatial continuity of coal seams in the Walloon Coal Measures is highly variable and often difficult to map and predict, even with closely spaced (

A surrogate model approximates a computationally expensive solver. Polynomial Chaos is a method used to construct surrogate models by summing combinations of carefully chosen polynomials. The polynomials are chosen to respect the probability distributions of the uncertain input variables (parameters); this allows for both uncertainty quantification and global sensitivity analysis. In this paper we apply these techniques to a commercial solver for the estimation of peak gas rate and cumulative gas extraction from a coal seam gas well. The polynomial expansion is shown to honour the underlying geophysics with low error when compared to a much more complex and computationally slower commercial solver. We make use of advanced numerical integration techniques to achieve this accuracy using relatively small amounts of training data.

We present a new computational method of automatic normal moveout (NMO) correction that not only accurately flattens and corrects the far offset data, but simultaneously provides NMO velocity ( $$v_\mathrm{nmo}$$ ) for each individual seismic trace. The method is based on a predefined number of NMO velocity sweeps using linear vertical interpolation of different NMO velocities at each seismic trace. At each sweep, we measure the semblance between the zero offset trace (pilot trace) and the next seismic trace using a trace-by-trace rather than sample-by-sample based semblance measure; then after all the sweeps are done, the one with the maximum semblance value is chosen, which is assumed to be the most suitable NMO velocity trace that accurately flattens seismic reflection events. Other traces follow the same process, and a final velocity field is then extracted. Isotropic, anisotropic and lateral heterogenous synthetic geological models were built to test the method. A range of synthetic background noise, ranging from 10 to 30 %, was applied to the models. In addition, the method was tested on Hess’s VTI (vertical transverse isotropy) model. Furthermore, we tested our method on a real pre-stack seismic CDP gathered from a gas field in Alaska. The results from the presented examples show an excellent NMO correction and extracted a reasonably accurate NMO velocity field.

The demand for natural gas in Queensland, Australia has historically been supplied from conventional reservoirs. However, depletion in conventional sources has led producers to turn to extensive supplies in Queensland's coal resources. These coal seam gas (CSG) developments not only represent new supplies for the domestic market in eastern Australia, they are also the first time that CSG (aka coal bed methane or CBM) has been liquefied to serve the expanding world LNG market. In order to make this development occur, considerable infrastructure had to be installed, with field developments still on-going. This AUD$60 billion investment precipitated a major overhaul of state regulations to provide not only a safe and clean operating environment, but also to allay the concerns of certain stakeholders. The gas is primarily produced from thin high permeability coals in the Jurassic-age Walloon Coal Measures in the Surat Basin and from several relatively thick Permian-age coal seams in the Bowen Basin, of which the Baralaba Coal Measures and the Bandanna formation are the most important. There are numerous technical challenges with this production, such as fines production from the inter-burden clays, which can form a thick paste that is difficult to pump. Salt extraction by reverse osmosis, from associated water produced to depressurise the coal seams and enable the flow of gas, allows for the beneficial use of the water. Technical challenges also include mathematical modelling of the counter-current two-phase flow (gas and water) in the well annuli because conventional models in simulators only handle co-current two-phase flow in the well-bores. Also, the subject of on-going investigations is decommissioning of the large number of shallow wells over the next few decades in a safe and cost effective manner, with compressed bentonite being a promising option for well plugging. As with any major commercial development, in addition to the technical challenges there have been social challenges as well. These include interaction and coexistence of extensive surface operations with an established agricultural sector, interactions between gas production and ground water aquifers in water-stressed areas, and the cumulative social and economic impacts of 3 large projects on a rural area. Ultimately, the State of Queensland expects to produce more than 1800 BCF/annum, of which about 1400 BCF/annum will be exported as LNG. Depending on the demand and well productivity, up to 1000 CSG wells may be drilled per year for the next thirty years. A review of CSG resources, development, and challenges is presented in this paper to provide context for a stream of research findings that are emerging on the Queensland CSG experience.

Coal seam gas (CSG) is naturally occurring methane found in coal seams. Fractures act as the major channel for the gas to flow and play a significant role in evaluating the practicability of the CSG exploitation. Prediction of the subsurface coal seams and the distribution of the fractures within the coal seams is a challenge. This study attempted to predict coal distribution by applying spectral decomposition and post-stack seismic inversion on a data set of the Surat Basin, Queensland, Australia. Semblance and curvature attributes were computed to determine fracture distribution within the coal seams. According to the rock physics analysis, based on well data, coal beds had a low p-impedance as compared to surrounding lithologies. A model-based P-impedance inversion was executed to create a P-impedance volume. Extracted horizon slices successfully highlighted coal seams at different levels. Blind test for the P-impedance prediction at a well location revealed a reasonable match for coal prediction. High frequency (from 40~60 Hz) spectra delineated thin coal beds. Semblance and curvature predicted zones of high fracture density within various stratigraphic levels. The high density of fractures is along faults. This study successfully demonstrated the prediction of coal seams and fracture distribution by using seismic inversion spectral decomposition and seismic attributes. Hence, these techniques can be used for the field development of coal seam gas.

Laterally discontinuous coal measures are common in alluvial settings due to interaction with fluvial systems. Under these conditions it is difficult to accurately represent coalbeds and interburden sandstone bodies in static and dynamic models at a regional scale. These challenges are compounded in the Walloon Coal Measures by non-uniform drill spacing, which varies from clustered to sparse and insufficient outcrop exposures available to constrain the correlations. To address these issues, this study investigates a nested approach to facies modelling of the Upper Juandah Member of the Jurassic Walloon Coal Measures in the Surat Basin, Queensland, which contains some 3,600 wells, of which half were analysed for lithofacies distributions. This approach contrasts the application of truncated Gaussian simulation, object modelling and multiple-point geostatistical simulation. First, a regional scale structural model was developed based on the correlation of sub units within the basin and the lithofacies were then interpreted from normalised wireline logs. Then geometries of individual facies were defined from two local scale models (~6 × 6 km2) where dense drilling, 3D seismic and paleocurrent analysis data were available to constrain the models. Three training images, generated by object modelling, an analogue of one part of the Ob River, and an interactive method were subsequently used to model primary channels, channels and crevasse splays, respectively. Truncated Gaussian simulation was used in modelling the distribution of marginal and coal swamp. The final model is a combination of the model with primary channels and channels, and the model with marginal and coal swamp. This approach is the first trial in modelling swamp and channel distributions at a regional scale by integrating data from local models, depositional analogues and paleo-flow interpretation in the Surat Basin.