Module 16: Geostatistical Integration Workflow of Well Data and Seismic Attributes

SubsurfaceAI 2024 introduces Module 16: Geostatistical Integration of Well Data and Seismic Attributes, a state-of-the-art geostatistical toolkit designed for the sophisticated integration of well data with seismic attributes. This module revolutionizes data conditioning, well data upscaling, and the application of advanced kriging and conditional simulation techniques. Equipped with powerful uncertainty analysis tools, it enables users to assess the reliability of interpreted data, thereby enhancing decision-making processes.

Key Features:

  1. Data Conditioning:
    • Empower users to meticulously select wells for generating conditioning data, laying the groundwork for in-depth geostatistical analysis, kriging, and conditional simulation across various geological horizons, intervals, or stratigraphic grids.
  2. Well Data Upscaling:
    • Provide a versatile selection of upscaling algorithms (mean, median, minimum, maximum, RMS, etc.), custom-fit for different well log types or formation characteristics. This ensures the finest representation of subsurface data.
  3. Data Analysis:
    • Enable a thorough examination of conditioning data with analytical tools for histogram distribution, correlation tables, and cross plots between upscaled well data and seismic attributes. It includes semi-variogram analysis and variogram model fitting, allowing users to identify seismic attributes highly sensitive to the target property via Principal Component Analysis, Neighborhood Component Analysis, and Step-wise Regression.
  4. Kriging Techniques:
    • Integrate a variety of kriging methods, such as Simple Kriging, Universal Kriging, and Collocated Kriging, to refine spatial data estimation using seismic attributes or other relevant properties.
  5. Conditional Simulation:
    • Produce equiprobable realizations of the target variable, strictly adhering to well conditioning data and utilizing seismic attributes as trend variables. These simulations are further constrained by seismic facies, significantly improving model accuracy and reliability.
  6. Uncertainty Analysis:
    • Offer tools for the precise calculation and visualization of uncertainty in target variables, showcasing estimations at p10, p50, and p90 levels. Uncertainty is depicted through histograms, maps, or grids, providing a transparent view of potential outcomes and facilitating risk management.

Module 16 of SubsurfaceAI 2024 establishes a new standard in geostatistical analysis, offering unparalleled precision and confidence in the integration of well data with seismic attributes. Step into the future of reservoir management and exploration with data-driven decision-making at its core.