Exploration Geophysics - Volume 54, Issue 6, 2023

Airborne spectral gamma-ray survey data were processed using Th-normalization technique for oil and gas exploration in the Qaret El-Soda area, Western Desert of Egypt. This technique was applied to suppress the effects of surface lithology, which are the main factors influencing the variation of radioelement content in rocks. Normalization of K and U by thorium yielded residual potassium and residual uranium estimates. Possible occurrences of new hydrocarbon microseepages were determined by mapping low values of residual potassium and high values of residual uranium relative to potassium, which are indicated as DRAD values, which were obtained by subtracting residual potassium from residual uranium values (eUresid – Kresid). Lower residual values of K, which were associated with higher DRAD anomaly values, highlight areas of prospective hydrocarbon accumulations. The obtained results from quantitative analysis and interpretation of aeromagnetic data show sufficiently thick sediments, probably suitable for the accumulation of hydrocarbons. This means that the study area may possess a potential for hydrocarbon exploration if supported by other detailed geophysical and geochemical exploration techniques.

Due to geological conditions, acquisition environment, and economic restrictions, acquired seismic data are often incomplete and irregularly distributed, and this affects subsequent migration imaging and inversion. Sparse constraint-based methods are widely used for seismic data interpolation, including fixed-base transform and dictionary learning. Fixed-base transform methods are fast and simple to implement, but the basis function needs to be pre-selected. The dictionary learning method is more adaptive, and provides a means of learning the sparse representation from corrupted data. K-singular value decomposition (K-SVD) is a classical dictionary learning method that combines sparse coding and dictionary updating iteratively. However, the dictionary atoms are updated column-by-column, leading to high computational complexity due to long SVD calculation times. In this study, we evaluated the dictionary learning method via l4-norm maximisation using an orthogonal dictionary, which is different from the traditional l0-norm or l1-norm minimisation, and interpolated the missing traces in the projection onto convex sets (POCS) framework. The optimal objection function is convex, but can be solved using a simple and efficient Matching, Stretching and Projection (MSP) algorithm, which greatly reduces the dictionary learning time. Numerical experiments using synthetic and field data demonstrate the effectiveness of the proposed method.

High-frequency contents of reflections are essential in the investigation and interpretation of thin-bed reservoirs. These beds can be even more complicated in carbonate rocks, as pore geometries influence final seismic responses. To address these complexities, we propose a seismic forward modeling workflow to investigate several thin-bed reservoirs in a carbonate oilfield with variable pore geometries. The new workflow enhances the existing forward models for the investigation of thin beds by integrating seismic petrophysics, geological model building, and 2D finite-difference elastic modeling. We used seismic petrophysics to ensure the consistency between petrophysical well logs and seismic data using rock physics modeling. Then, we introduced a new high-resolution workflow for velocity modeling to build a reliable geological model. Finally, the 2D finite-difference elastic modeling is employed to generate synthetic traces based on our geological model to obtain seismic responses for the existing thin-bed reservoirs. The forward models used in this study are a powerful tool for investigating thin layers because they enable high-resolution investigation of the given geological model in distinguishing lateral and vertical lithofacies changes. The new velocity modeling workflow, implemented in this research, is more reliable and effective than the conventional velocity property modeling approaches, which resulted in synthetic seismic sections with increased lateral and vertical resolutions and enhanced data from a thin bed. The main features of this workflow are the incorporation of well-log data into geological model building, combining the high-resolution data of horizontal seismic stacking velocity with vertical well logging, and the incorporation of a residual model to improve the seismic stacking velocity. We produced a more coherent section resembling the acquired 3D seismic data by applying the proposed workflow to data from an oil carbonate reservoir in the Fahliyan Formation within the Abadan Plain in SW Iran. It is concluded that the higher frequency synthetic sections from the proposed workflow can assist in resolving the seismic interpretation challenges. By applying the proposed workflow to the current data set, four thin-bed carbonate reservoirs were investigated with corresponding thicknesses of approximately 25, and 17 m at peak frequencies of 60, and 90 Hz, respectively.

 

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The amplitude Variations with Offset (AVO) technique is extensively used in hydrocarbon exploration and production for de-risking of the prospect before drilling. The interpretation of AVO results brings more confidence in the presence or absence of hydrocarbon. The AVO and Direct Hydrocarbon Indicator (DHI) analysis have their own inherent limitations. Therefore, to ensure the presence of true AVO in seismic, other attribute supports are crucial. The present prospect in this study was deposited as a channel-levee complex in the shelf-slope of the Krishna-Godavari basin. There is evidence of false AVO within this complex and thus additional attributes were used in this study to support the AVO analysis. The conventional AVO analysis shows the presence of Class-III AVO for a probable reservoir in seismic. However, it is unable to ensure the presence of hydrocarbon in the stacked reservoir unit. The spectral AVO technique was used to check AVO response at low frequencies for laminated and stacked reservoir units. Additionally, seismic attenuation and dispersion attribute studies were incorporated into the de-risking analysis. The drill result showed two distinct reservoir intervals in this prospect, which validated the predrill interpretation. This integrated approach added significant value in de-risking the prospect using AVO and other seismic attributes in complex geologic setups.

Random noise, which has a significant impact on subsequent processing and interpretation, easily interferes with seismic data. Current convolutional neural networks (CNN) use a single-stage technique to boost network capacity by exploiting the complicated network structure, but the performance of the network becomes saturated and prone to overfitting at a certain stage. Hence, we propose a two-stage U-Net denoising network with a supervised attention module (UNet-SAM). In this supervised algorithm, the first stage obtains the pre-denoising results, while the second stage achieves more accurate data. The supervised attention module (SAM) block is inserted in the first stage, extracting features with supervised attention to utilise as a priori information and guide the fine denoising in the second stage. The combination of the attention mechanism and two-stage strategy provides prior information that helps to train a network with better denoising performance. Experiments on synthetic and field data illustrate that the proposed UNet-SAM not only has a superior denoising effect but also retains more of the original effective signal.

Geophysical inversions give non-uniqueness solutions and unless constrained by appropriate initial values and geological constraints can give unrealistic results. One of the critical constraints can be the physical property values of different lithologies. We have compiled a density and magnetic susceptibility database consisting of thousands of measurements collated from different organisations and/or projects across the Abitibi greenstone belt. Statistical tools (histograms, quantile-quantile probability plots and boxplots) are applied to characterise systematically major and minor lithologies. We observed that the magnetic susceptibility frequently has a bimodal distribution, while density is typically unimodal. Our results are summarized in a table that includes the representative mean (or median) and a range of acceptable values. These values can be used to better understand the regional geology, but in this paper, we used the tabulated properties in a geophysical/petrophysical inversion of gravity data from the Chicobi area in the Abitibi subprovince to show the level of improvements that the petrophysical constraints can add to an unconstrained model. When our density database is used to seed the initial guess in a gravity inversion, an anomalous zone becomes apparent that was less evident on an unconstrained inversion.

This study combines the processing and interpretation of magnetic data with geological fieldwork. The purpose is to establish the geological map and rutile mineralization targets map of Minta area (Haute-Sanaga, Cameroon). To this, a strategic geological field survey was first conducted. It highlighted some geological structures, especially magnetite quartzites, which is considered as the potential sources of primary rutile mineralization. Several methods were applied to the magnetic data, including upward continuation (UC), vertical derivative (DZ), analytical signal (AS) and horizontal gradient magnitude (HGM). The regional-residual separation method based on the UC was used to develop the residual map which was very helpful to understand the distribution of magnetic anomalies related to rutile mineralization in this area. The structural model of the study area was established by combining the analysis of the AS and HGM maxima with the analysis of the topographic model. Since magnetic relief variation characterizes the lithological information changes according to the filtered map considered, a set of categorization processes based on the anomaly signals was applied to each of filtered maps according to the geological information sought. It is based on the interpretation of structural models, combined with previous works and the spatial distribution of geological data collected. This process produced a set of partial lithological models, which were then combined with the interpreted structural model to produce the geological map of the study area. This map also shows the spatial distribution of the various targets of potential rutile mineralization interpreted on the DZ model as rectilinear anomalies, with a signal range of 0.78–0.88 nT/m.

Subsurface karst features are significantly developed in Guangxi Province, China. This area mainly contains fractured subsurface rock, abundant karst channels, and widely distributed underground fissure networks. Such adverse geological conditions could potentially create hydrogeological hazards such as collapses, water inrush, and mud inrush during infrastructure construction. The Hejing limestone mine is an opencast mine in Pingnan County, Guangxi, that produces cement. Mining activities have altered the seepage fields in this area, causing large amounts of groundwater to flood into the mining pit; this has caused many ground collapses while severely reducing limestone production. More than 24 km of surface electrical resistivity tomography (ERT) profiles have been previously recorded in the region to identify potential karst positions and explore groundwater inrush paths. In this study. we employed surface and cross-borehole ERT surveys to delineate specific groundwater inrush paths on the eastern side of the mine and characterise karst distribution in the study area. Resistivity imaging results revealed some low-resistivity anomaly distributions and provided reliable geological information about the distribution of subsurface karst for future grouting work.