Reconstruction of Subsurface Potential Hydrocarbon Reservoirs through 3D Seismic Automatic Interpretation and Attribute Analysis focuses on enhancing the identification and characterization of hydrocarbon-bearing zones using advanced 3D seismic interpretation techniques. By integrating automated seismic horizon picking and fault detection algorithms with multi-attribute analysis, the study aims to delineate subsurface geological structures such as traps, faults, and stratigraphic features with greater precision. The approach leverages seismic attributes—including amplitude, coherence, curvature, and spectral decomposition—to extract critical reservoir indicators and improve geological model accuracy. This research contributes to reducing interpretation time, minimizing exploration risk, and increasing the reliability of reservoir predictions in complex subsurface settings.

Detecting small-scale features in complicated subsurface geology, like Carbonate, by seismic imaging is challenging due to diverse object qualities affecting propagating waves. The initial phase in machine learning (ML) is to provide enough data to update and mature the algorithm. Without various diffraction shapes, machine learning (ML) predictions may be inaccurate. Additionally, ML may miss the diffraction pattern in the data. After the learning phase, our system prioritizes target detection. This entails matching the target to the data and finding a signature. The system receives data in the form of images and features in this investigation. The learning algorithm may forecast the target based on input. Machine learning aims to decrease the difference between predictions and target values as much as possible. Diffraction amplitude preservation in varying velocity conditions depends on specific factors. In circumstances with multiple diffractions, typical filtering methods combine amplitudes, hence ML destruction is used to separate them.

3D Geo-Seismic interpretation involves identifying and characterizing underlying geological formations, lithological characteristics, and hydrocarbon-containing rocks. This research addresses geophysical seismic data analysis using specific methods. To study the complicated properties of deep geological systems that store hydrocarbons. The structural features and complex stratigraphic layers are thoroughly analyzed. The investigation found anomalies in the stratigraphy of the region through complex seismic sections. The study attempts to discover trap structures and examine phenomena like dim spots, flat spots, and bright spots, which are not generally found in regular seismic data, while considering possible hydrocarbon resources in the area. This study enhances 3D Geo-Seismic data in Australia's Cooper Basin using geophysical features. The contemporary method analyzes seismic data to identify hydrocarbon resources and investigate trap formations. The project aims to analyze subsurface geology using 3D seismic data, comprehend the local petroleum system, and improve target illumination for hydrocarbon formations. This study offers valuable insights on geophysics and hydrocarbon exploration by analyzing geological complexity. This study's approach informs future research and resource appraisal.

Fractured imaging is crucial for oil and gas development because it is heterogeneous and has low-impedance contrast, indicating geological complexity. Fractures and faults appear as diffracted waves in seismic data. Due to subsurface processes and the recording mechanism, seismic data comprise both reflected and diffracted events. When acoustic impedance contrast arises, faults, fractures, channels, rough edges, and karst sections cause seismic diffractions. To explain the diffraction hyperbolic pattern, this work uses a double square root (DSR) equation to simulate it with varying velocities and depths of point diffraction. We also examine diffraction separation and velocity analysis (semblance vs. hybrid transit time) for image velocity model development. Our research on a steep dipping fault model shows that dip frequency filtering (DFF) in the frequency–wavenumber (F-K) domain can separate seismic diffractions. Semblance and hybrid travel time (HTT) velocity models are used for imaging. HTT imaging of complicated objects and below shadow zones yields the best results.

Carbonate reservoirs hold a large share of global oil and gas reserves. About 60% of the world's oil and 40% of gas are in carbonate reservoirs. Poor seismic imaging and diagenetic reservoir variability make carbonate hydrocarbon exploration and development difficult. Due to the challenges of extremely diverse carbonate reservoir rocks, petroleum industry researchers and geoscientists prioritize their evaluation. For commercial viability, geoscientists, petrophysicists, and engineers must collaborate from pool exploration and delineation to production to extract as much information as possible to produce maximum hydrocarbons from the field. Inversion of seismic data alone infers characteristics without well-log data. Seismic inversion is used in oil and gas development and production to track reservoir facies and fluid contents. 3D seismic data, geological & petrophysical information, and electrologs from drilled wells are used to interpret and invert seismic data to understand reservoir geometry, facies variation, and intervening tight layers in the Miocene carbonate reservoir in Central Luconia. 

The Turkish basin has many geophysical problems, making it difficult to find simple and broad formations with big hydrocarbon reservoirs. However, the worldwide inventory of exploitable hydrocarbon reserves has declined. Thus, exploration and development are focused on extracting "new oil and natural gas" reserves from intricate overburden structures that cannot be found using traditional methods. Our research expertise must improve subsurface data processing and explore hidden hydrocarbons to meet country demands. Thrust faults, normal faults, and stratigraphic structures are complex geological subsurface formations that require advanced imaging and interpretation. Lack of wave propagation expertise limits subsurface imaging knowledge and methodology. Our desired target may not be photographed or illuminated if the wave propagation path bypasses its subsurface. Geological modeling and wave propagation for subsurface imaging will be the main focus of the research to solve the Turkish Basin problem.

The petroleum industry relies on seismic imaging to locate and extract small hydrocarbon reserves in complex structures. The main goal of diffraction data imaging is to enhance subsurface images by revealing structural topographies and revealing sharpness and internal features. High-resolution photos help interpreters identify minor occurrences, pitchouts, and margins of anomalies like faults, fractures, and salt bodies. Seismic imaging technology advances in recognizing diffused waves, enabling high-resolution imaging. This study introduces a low-rank symbol approximation approach for modeling seismic wave propagation. The simulated data is dispersion-free and used for D-data imaging. The model is modeled using low-rank (LR) and Finite difference (FD) approaches, with LR outperforming FD. The D-Data graphics indicate an increase in frequency from 0 to 10 Hz and 50 to 60 Hz. This research shows how to use this to evaluate subsurface features and improve seismic data resolution for oil reservoir exploration.

Diffracted rather than reflected waves make small-scale geological discontinuities difficult to identify and depict in seismic data. However, the combined reflected and diffracted image provides full wave information and can help interpreters identify faults, fractures, and surfaces in built-up carbonate. Diffraction imaging has a resolution below the normal seismic wavelength, but if the wavelength is significantly less than the discontinuity width, interference effects do not generate seismic diffractions. This work uses synthetic examples and real data to demonstrate the potential of diffraction separation for high-resolution seismic imaging and explain the optimal strategy for retaining diffraction. We use data from the carbonate Sarawak Basin to demonstrate the precision of separating diffractions using plane-wave destruction (PWD) and dip frequency filtering (DFF). The model and experimental data show that PWD preserves diffraction better than DFF. The final results show that diffraction separation and imaging can improve high-resolution seismic data of minor but important geological features.

Least-square pre-stack depth migration (LSM) co-processing removed survey design's effect on the final image and pinching mark compared to Kirchhoff PSM. In the field, 4D physical, geometric, and seismic variables are examined to determine the probability of an observed 4D difference independent of acquisition geometries. The 4D study was performed on two case studies offshore Abu Dhabi to establish which methodology and algorithm will ensure the entire and optimal 4D processing sequence relaxes seismic acquisition reproducibility.