Basement structural architecture and hydrocarbon conduit potential of polygonal faults in the Cooper‐Eromanga Basin,Australia

A thorough and complete understanding of the structural geology and evolution of the Cooper‐Eromanga Basin has been hampered by low‐resolution seismic data that becomes particularly difficult to interpret below the thick Permian coal measures. As a result, researchers are tentative to interpret the basement fault architecture within the basin, which is largely undefined. To provide a better understanding of the basement fault geometry, all available two‐dimensional seismic lines together with 12 three‐dimensional seismic surveys were structurally interpreted with assistance from seismic attribute analysis. The Upper Cretaceous Cadna‐owie Formation and top Permian reflectors were analysed using a common seismic attribute technique (incoherency) that was used to infer the presence of faults that may have otherwise been overlooked. Detailed basement fault maps for each seismic survey were constructed and used in conjunction with two‐dimensional seismic data interpretation to produce a regional basement fault map. Large north‐northeast–south‐southwest‐striking sinistral strike–slip faults were identified within the Patchawarra Trough appearing to splay from the main northeast–southwest‐striking ridge. These sinistral north‐northeast–south‐southwest‐striking faults, together with field‐scale southeast–northwest‐striking dextral strike–slip faults, are optimally oriented to have potentially developed as a conjugated fault set under a south‐southeast–north‐northwest‐oriented strike–slip stress regime. Geomechanical modelling for a regionally extensive system of Cretaceous polygonal faults was performed to calculate the Leakage Factor and Dilation Tendency of individual faults. Faults that extend into Lower Cretaceous oil‐rich reservoirs with strikes of between 060°N and 140°N and a high to near‐vertical dip angle were identified to most likely be acting as conduits for the tertiary migration of hydrocarbons from known Lower Cretaceous hydrocarbon reservoirs into shallow Cretaceous sediments. This research provides valuable information on the regional basement fault architecture and a more detailed exploration target for the Cooper‐Eromanga Basin, which were previously not available in literature.     

Statistical landslide susceptibility assessment of the Mansehra and Torghar districts,Khyber Pakhtunkhwa Province,Pakistan

This paper presents laboratory experiments and numerical simulations of effects of submerged obstacles on tsunami-like solitary wave and its run-up. This study was carried out for the breaking and non-breaking solitary waves on 1:19.85 uniform slope which contains a submerged obstacle. New laboratory experiments are performed to describe the mitigation of tsunami amplitude and run-up under the effect of submerged obstacles. We are based on experimental results obtained to validate the numerical model. The numerical modeling using COULWAVE aims essentially to show the effect of the obstacle on the shape of solitary wave and the limit of this effect. Using a multiple nonlinear regression, we have determined a model to estimate height of run-up according to the amplitude of the wave and the obstacle peak depth.     

Optimization of nonconventional wells under uncertainty using statistical proxies

The determination of the optimal type and placement of a nonconventional well in a heterogeneous reservoir represents a challenging optimization problem. This determination is significantly more complicated if uncertainty in the reservoir geology is included in the optimization. In this study, a genetic algorithm is applied to optimize the deployment of nonconventional wells. Geological uncertainty is accounted for by optimizing over multiple reservoir models (realizations) subject to a prescribed risk attitude. To reduce the excessive computational requirements of the base method, a new statistical proxy (which provides fast estimates of the objective function) based on cluster analysis is introduced into the optimization process. This proxy provides an estimate of the cumulative distribution function (CDF) of the scenario performance, which enables the quantification of proxy uncertainty. Knowledge of the proxy-based performance estimate in conjunction with the proxy CDF enables the systematic selection of the most appropriate scenarios for full simulation. Application of the overall method for the optimization of monobore and dual-lateral well placement demonstrates the performance of the hybrid optimization procedure. Specifically, it is shown that by simulating only 10% or 20% of the scenarios (as determined by application of the proxy), optimization results very close to those achieved by simulating all cases are obtained.     

Petrology and geochemistry of feldspathic impact‐melt breccia Abar al' Uj 012, the first lunar meteorite from Saudi Arabia

Abar al' Uj (AaU) 012 is a clast‐rich, vesicular impact‐melt (IM) breccia, composed of lithic and mineral clasts set in a very fine‐grained and well‐crystallized matrix. It is a typical feldspathic lunar meteorite, most likely originating from the lunar farside. Bulk composition (31.0 wt% Al₂O₃, 3.85 wt% FeO) is close to the mean of feldspathic lunar meteorites and Apollo FAN‐suite rocks. The low concentration of incompatible trace elements (0.39 ppm Th, 0.13 ppm U) reflects the absence of a significant KREEP component. Plagioclase is highly anorthitic with a mean of An_96.9Ab_3.0Or_0.1. Bulk rock Mg# is 63 and molar FeO/MnO is 76. The terrestrial age of the meteorite is 33.4 ± 5.2 kyr. AaU 012 contains a ~1.4 × 1.5 mm² exotic clast different from the lithic clast population which is dominated by clasts of anorthosite breccias. Bulk composition and presence of relatively large vesicles indicate that the clast was most probably formed by an impact into a precursor having nonmare igneous origin most likely related to the rare alkali‐suite rocks. The IM clast is mainly composed of clinopyroxenes, contains a significant amount of cristobalite (9.0 vol%), and has a microcrystalline mesostasis. Although the clast shows similarities in texture and modal mineral abundances with some Apollo pigeonite basalts, it has lower FeO and higher SiO₂ than any mare basalt. It also has higher FeO and lower Al₂O₃ than rocks from the FAN‐ or Mg‐suite. Its lower Mg# (59) compared to Mg‐suite rocks also excludes a relationship with these types of lunar material.     

Modes of climate variability as simulated by a coupled general circulation model. Part I: ENSO-like climate variability and its low-frequency modulation

The El Niño-Southern Oscillation (ENSO) is investigated in a multicentury integration conducted with the coupled general circulation model (CGCM) ECHAM3/LSG. The quasiperiodic interannual oscillations of the simulated equatorial Pacific climate system are due to subsurface temperature anomaly propagation and a positive atmosphere-ocean feedback. The gravest internal wave modes contribute to the generation of these anomalies. The simulated ENSO has a characteristic period of 5–8 years. Due to the coarse resolution of the ocean model the ENSO amplitude is underestimated by a factor of three as compared to observations. The model ENSO is associated with the typical atmospheric teleconnection patterns. Using wavelet statistics two characteristic interdecadal modulations of the ENSO variance are identified. The origins of a 22 and 35?y ENSO modulation as well as the characteristic ENSO response to greenhouse warming simulated by our model are discussed.     

Kernel Principal Component Analysis for Efficient,Differentiable Parameterization of Multipoint Geostatistics

This paper describes a novel approach for creating an efficient, general, and differentiable parameterization of large-scale non-Gaussian, non-stationary random fields (represented by multipoint geostatistics) that is capable of reproducing complex geological structures such as channels. Such parameterizations are appropriate for use with gradient-based algorithms applied to, for example, history-matching or uncertainty propagation. It is known that the standard Karhunen–Loeve (K–L) expansion, also called linear principal component analysis or PCA, can be used as a differentiable parameterization of input random fields defining the geological model. The standard K–L model is, however, limited in two respects. It requires an eigen-decomposition of the covariance matrix of the random field, which is prohibitively expensive for large models. In addition, it preserves only the two-point statistics of a random field, which is insufficient for reproducing complex structures. In this work, kernel PCA is applied to address the limitations associated with the standard K–L expansion. Although widely used in machine learning applications, it does not appear to have found any application for geological model parameterization. With kernel PCA, an eigen-decomposition of a small matrix called the kernel matrix is performed instead of the full covariance matrix. The method is much more efficient than the standard K–L procedure. Through use of higher order polynomial kernels, which implicitly define a high-dimensionality feature space, kernel PCA further enables the preservation of high-order statistics of the random field, instead of just two-point statistics as in the K–L method. The kernel PCA eigen-decomposition proceeds using a set of realizations created by geostatistical simulation (honoring two-point or multipoint statistics) rather than the analytical covariance function. We demonstrate that kernel PCA is capable of generating differentiable parameterizations that reproduce the essential features of complex geological structures represented by multipoint geostatistics. The kernel PCA representation is then applied to history match a water flooding problem. This example demonstrates that kernel PCA can be used with gradient-based history matching to provide models that match production history while maintaining multipoint geostatistics consistent with the underlying training image.