Scope of Work:
Experience the depth and value of our pilot study, as we tailor the analysis to achieve your specific objectives:

a. Data Acquisition and Preparation:
In this phase, we work closely with you to acquire the necessary volumes of reflectivity data, horizon data, and well data. We ensure the data is accurately prepared and organized, setting the stage for a robust analysis.

b. Classification Analysis:
During the pilot study, our expert team will showcase the seamless blend of traditional deterministic tools and advanced AI/ML techniques to perform a comprehensive Classification of your seismic data. Witness how our AI-driven algorithms and deep neural network architectures uncover intricate geological features, faulting, lithofacies, and potential hydrocarbon reservoirs, delivering invaluable insights.

– Traditional Techniques: Observe the application of deterministic methods, such as seismic waveform inversion and attribute analysis, to identify lithology, porosity, and fluid content. This foundational step builds a solid foundation for subsequent AI/ML analyses.

– Advanced AI/ML Techniques: Experience the power of our state-of-the-art machine learning models, including deep neural networks, support vector machines, and clustering algorithms, which process vast amounts of data and identify complex patterns, significantly enhancing the accuracy of lithofacies and hydrocarbon identification.

c. Inversion Analysis:
Witness the seamless integration of traditional and AI/ML techniques during the pilot study, as we enhance the resolution and quality of your seismic data, providing a clearer view of subsurface structures and properties. By incorporating well data and low-frequency information, we estimate accurate wavelets and compute acoustic impedance, enabling you to make more informed reservoir-related decisions.

– Frequency Compensated Inversion (FCI): Observe traditional FCI methods in action as we extract detailed information about the subsurface, including acoustic impedance, density, and shear impedance. This process enhances the seismic data and improves the understanding of subsurface characteristics.

– AI/ML-Driven FCI: Experience how our AI/ML algorithms refine FCI results by incorporating geological constraints and supplementary data, overcoming limitations and increasing the accuracy and reliability of the inversion process.

d. Fault Analysis:
During the pilot study, gain essential insights from our comprehensive analysis, which involves a blend of traditional fault detection methods and cutting-edge AI/ML techniques, including convolutional neural networks. Experience firsthand how we expertly identify faults and characterize their attributes, providing you with invaluable data for interpretation and decision-making processes.

– Traditional Fault Detection: Observe established techniques, such as coherence analysis and curvature attribute analysis, in action, as we identify faults in the seismic data. This foundational step aids in fault localization and mapping.

– AI/ML-Driven Fault Analysis: Witness our application of convolutional neural networks (CNN), conducting a detailed and automated fault detection process, accurately capturing complex fault patterns and characteristics. This advanced technique improves fault characterization, leading to more precise geological interpretations.