subsurfaceAI

Module 18

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Module 18: Microseismic Data Analytics & Integration

Analyze and integrate microseismic data with completion data for comprehensive reservoir monitoring and evaluation. This module offers tools for animating microseismic events, estimating stimulated reservoir volumes (SRV), and visualizing microseismic and completion data together. Predict microseismic intensity from seismic attributes and leverage deep learning models to understand the contributions of various factors to SRV.

 
  1. Animation of Microseismic Events in Sync with Completion Data
    • Dedicated windows for 3-D and 4-D visualizations of microseismic events alongside surface attributes and completion data.
    • Animation of microseismic data in section, map, and 3-D views.
    • Real-time update with PHD database
    • Time-bar windows for the animation of microseismic events synchronized with completion data.
  2. Estimation of Stimulated Reservoir Volumes (SRV) and Microseismic Volumes
    • Calculation of 3-D microseismic intensity or microseismic attributes.
    • Estimation of in-stage microseismic intensity and attribute volumes within user-defined intervals.
    • Re-sampling of surface seismic attributes to micro-seismic volumes.
    • Estimation of stimulated reservoir volumes (SRV) for each stage or time intervals within each stage.
  3. Microseismic and Completion Data Visualization
    • Animation of microseismic events in both 3-D and 2-D windows (base map and seismic sections).
    • Visualization of completion time series data.
    • Definition of multiple vector plots from microseismic attributes and advanced filtering.
    • Visualization of microseismic vector plots in both 3-D and base map windows, alongside other well and seismic data.
    • Live link in cross plotting any completion and microseismic events in both 2-D and 3-D windows.
  4. 4-D Microseismic Visualization
    • Animation of in-stage 4-D microseismic volumes in gun-barrel, section, and map view, integrated with completion data.
  5. Prediction of Microseismic Intensity from Seismic Attributes
    • Implementation of a deep learning workflow to predict microseismic intensity maps after training with pre- or post-stack seismic attributes. The deep learning model is applicable to any horizon or interval within areas sharing similar geology.
  6. Data Analytics of Microseismic Data, SRV, and Completion Data
    • Distance between fracking stages and microseismic points.
    • Development of deep learning models between microseismic intensity and other microseismic attributes.
    • Construction of deep learning models between SRV, surface seismic attributes, microseismic data, and completion data to assess their relative contributions to SRV.
  7. Classification of Microseismic events
    • Accept any set of microseismic properties as the input.
    • Advanced machine learning methods for unsupervised classification
    • Fit-for-purpose classification of microseismic events.
    • Gain more insights of micro-seismicity.
  8. Diffusivity Plots
    • Apply multiple models to model spatial distribution of microseismic events.
    • Separate microseismic events into wet and dry classes.
  9. Iso-surfaces of Density Volumes
    • Generate iso-surfaces of microseismic density volumes with P20, P50, P75 percentiles.
    • Volumes of geobodies defined by iso-surfaces.