Exploration
Poorandokht Soltani; Amin Roshandel Kahoo; Hamid Hassanpour
Abstract
Seismic methods are among the primary and most effective techniques for hydrocarbon exploration, as they enable comprehensive imaging and interpretation of the Earth's subsurface. However, accurate interpretation of seismic data requires detailed analysis of geological structures, often involving complex ...
Read More
Seismic methods are among the primary and most effective techniques for hydrocarbon exploration, as they enable comprehensive imaging and interpretation of the Earth's subsurface. However, accurate interpretation of seismic data requires detailed analysis of geological structures, often involving complex and subjective decision-making processes. Constructing an initial geological model that aligns with seismic observations is a critical first step, but it is inherently non-unique and heavily influenced by the interpreter’s experience and preferences. Among various subsurface structures, salt domes are of particular interest due to their unique physical characteristics and their critical role in hydrocarbon entrapment, drilling risk management, and subsurface storage applications. Their distinct seismic textures, compared to surrounding sediments, make them identifiable using seismic texture attributes. Nevertheless, the manual delineation of salt dome geobody is a time-consuming and potentially error-prone task, especially given the volume, redundancy, and complexity of the seismic attributes used. To overcome these challenges, we propose a novel unsupervised framework for automatically identifying salt dome geobody in 2D seismic sections. The method begins by extracting a diverse set of seismic texture attributes, including both conventional attributes and novel texture descriptors derived from advanced image analysis techniques. Following attribute extraction, a attribute selection phase using techniques such as Laplacian Score is employed to eliminate redundant, irrelevant, or highly correlated attributes, thereby enhancing model efficiency and interpretability. The reduced set of relevant attributes is then used as input for clustering algorithms based on metaheuristic optimization techniques. These algorithms aim to partition the seismic data into meaningful clusters that correspond to geological attributes, particularly salt domes. Validation against multiple expert interpretations demonstrates the robustness and high accuracy of the proposed method. Results emphasize the capability of unsupervised clustering approaches especially those guided by metaheuristic strategies—in reducing interpretation uncertainty and improving segmentation quality.
A. Khojamli; F. Doulati Ardejani; A. Moradzadeh; A. Nejati Kalateh; A. Roshandel Kahoo; S. Porkhial
Abstract
The Ardabil geothermal area is located in the northwest of Iran, which hosts several hot springs. It is situated mostly around the Sabalan Mountain. The Sabalan geothermal area is now under investigation for the geothermal electric power generation. It is characterized by its high thermal gradient and ...
Read More
The Ardabil geothermal area is located in the northwest of Iran, which hosts several hot springs. It is situated mostly around the Sabalan Mountain. The Sabalan geothermal area is now under investigation for the geothermal electric power generation. It is characterized by its high thermal gradient and high heat flow. In this study, our aim is to determine the fractal parameter and top and bottom depths of the magnetic sources. A modified spectral analysis technique named “de-fractal spectral depth method” is developed and used to estimate the top and bottom depths of the magnetized layer. A mathematical relationship is used between the observed power spectrum (due to fractal magnetization) and an equivalent random magnetization power spectrum. The de-fractal approach removes the effect of fractal magnetization from the observed power spectrum, and estimates the parameters of the depth to top and depth to bottom of the magnetized layer using the iterative forward modelling of the power spectrum. This approach is applied to the aeromagnetic data of the Ardebil province. The results obtained indicated variable magnetic bottom depths ranging from 10.4 km in the northwest of Sabalan to about 21.1 km in the north of the studied area. In addition, the fractal parameter was found to vary from 3.7 to 4.5 within the studied area.
Ali Nejati Kalateh; Amin Roshandel kahoo
Abstract
We inverse the surface gravity data to recover subsurface 3D density distribution with two strategy. In the first strategy, we assumed wide density model bound for inverting gravity data and In the second strategy, the inversion procedure have been carried out by limited bound density. Wediscretize the ...
Read More
We inverse the surface gravity data to recover subsurface 3D density distribution with two strategy. In the first strategy, we assumed wide density model bound for inverting gravity data and In the second strategy, the inversion procedure have been carried out by limited bound density. Wediscretize the earth model into rectangular cells of constant andunidentified density. The number of cells is often greater than the number of observation points thus we have an underdetermined inverse problem. The densities are estimated by minimizing a cost function subject to fitting the observed data. The synthetic results show that the recovered model from the first strategy is characterized by broad density distribution around the true model, butthat of the second strategy is closer to true models.We carry out inversion of gravity data taken over chromite deposit located at Hormozgan providence of Iran for estimating of subsurface density distribution. The recovered model obtained from second strategy has appropriate agreement with previous study.
