Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
Many ancient carbonate build-ups are impacted by meteoric diagenetic processes including karstification. However, very little is known about the acoustic properties and seismic expression of karstified reservoirs. This paper investigates potential seismic expressions of karstified buildups by means of synthetic seismic modelling. The overall stratigraphic architecture and rock physical properties are derived from published examples of southeast Asian carbonate buildups. Various karst systems, corresponding to distinct stages of karstification of varying intensity, have been superimposed to the background facies-related acoustic model. Three-dimensional synthetic seismic cubes have been computed from the karst-bearing acoustic model and by testing various wavelet frequencies. The proposed approach allowed several types of palaeokarsts to be reproduced, from dendritic karst and flank margin caves to cave networks, in amplitude sections. However, geometrical seismic attributes, like coherency, cannot be accurately reproduced with modelling based on the available literature data. In addition, the exploration of this synthetic seismic shows that non-stratiform diagenetic bodies (i.e leached carbonate wedge in a mixing zone), appears as stratiform even in very high resolution seismic. Thus, their detection and characterisation require advanced techniques or prior knowledge such as borehole data.
Many ancient carbonate build-ups are impacted by meteoric diagenetic processes including karstification. However, very little is known about the acoustic properties and seismic expression of karstified reservoirs. This paper investigates potential seismic expressions of karstified buildups by means of synthetic seismic modelling. The overall stratigraphic architecture and rock physical properties are derived from published examples of southeast Asian carbonate buildups. Various karst systems, corresponding to distinct stages of karstification of varying intensity, have been superimposed to the background facies-related acoustic model. Three-dimensional synthetic seismic cubes have been computed from the karst-bearing acoustic model and by testing various wavelet frequencies. The proposed approach allowed several types of palaeokarsts to be reproduced, from dendritic karst and flank margin caves to cave networks, in amplitude sections. However, geometrical seismic attributes, like coherency, cannot be accurately reproduced with modelling based on the available literature data. In addition, the exploration of this synthetic seismic shows that non-stratiform diagenetic bodies (i.e leached carbonate wedge in a mixing zone), appears as stratiform even in very high resolution seismic. Thus, their detection and characterisation require advanced techniques or prior knowledge such as borehole data.
Interpretation of geologic structures entails ambiguity and uncertainties. It usually requires interpreter judgment and is time consuming. Deep exploitation of resources challenges the accuracy and efficiency of geologic structure interpretation. The application of machine-learning algorithms to seismic interpretation can effectively solve these problems. We analyzed the theory and applicability of five machine-learning algorithms. Seismic forward modeling is a key connection between the model and seismic response, and it can obtain seismic data of known geologic structures. Based on the modeling data, we first optimized the seismic attributes sensitive to the target geologic structure and then we verified the accuracy of the five machine-learning algorithms by the cross-checking method. In this case, the random forest algorithm had the highest accuracy. So we examined the structural interpretation method based on a random forest using the 3D seismic reflection data from coalfield exploration. The prediction effect of this interpretation workflow is verified by comparison with known geologic structures on the plane and profile. The results suggest that the random forest algorithm is feasible to indicate geologic structure interpretations in the case of collapsed column and fault structures and it can effectively improve the efficiency of seismic interpretation and its accuracy. The machine-learning-based workflow provides a new technique for seismic structure interpretation in coal mining.
Karstification in carbonate platforms of the Miocene age in Central Luconia province, offshore Sarawak, Malaysia, has been discussed since the onset of exploration and initial discoveries in the region, with over 200 mapped platforms to date. An extensive drilling program over the last decade confirmed the existence of karst during the drilling process where issues such as total loss circulation and bit drops were common. Karst in Central Luconia has been proposed by several authors; however, detailed quantitative description of the observed features have not yet been conducted. This study involves systematic mapping of loss circulation depths, chalkified/rubble/vuggy zones described from cores, and vugs of >2 mm in size and moldic porosity observed on thin sections of the Jintan platform. These data supplement the interpretation of karst from multiple 3D seismic attributes. Seismic interpretation of the Jintan and M1 platforms revealed an extensive dendritic pattern which is on average 70–100 m deep and 3–5 km long, and circular geobodies of 1 km in width that exist on the upper part of the platform. Spectral decomposition, also known as time-frequency analysis, was used to enhance the interpretation of karst features on seismics within a specific wavelength. In this study, a comparison of three spectral decomposition methods applied on the 3D seismic cube of the Jintan and M1 platforms was undertaken to determine the method which allowed for better delineation of the karst features. The results show that the short-time Fourier transform (STFT) method using frequencies of 46, 54, and 60 Hz delineated most of the karst features compared to the continuous wavelet transform (CWT) Morlet and CWT Ricker wavelet methods. This paper aims to discuss the dimensions, evolution and geometry of the karst features quantitatively on three selected karst horizons named “K1”, “K2”, “K3”. Interpretation revealed that the dendritic karst features were found to be most prominent on the K2 horizon which lies below a conspicuous change of the external geomorphology of the platform. Backstepping of the platform margin by 12 km is observed in both platforms. Quantitative seismic interpretation shows that the karst observed in M1 platform is approximately 70–100 m deep, and the dendritic features are around 1–2 km in length and approximately 500 m wide; whereas, in the Jintan platform the dendritic features observed are up to 5 km in length with several 1 km wide circular/sinkhole features. More than 20 dendritic features orientated SE and NS were mapped mainly in the transitional area as well as the center of both platforms. The nature of the karst morphology in Central Luconia remains controversial; however, it is proposed to be of mixing zone karst origin.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.