What is it about?
We present the concept of regionalized statistical pattern learning for conditioning an initial geo-spatial model to point and areal data. Especially, it applies to constraining geological process models to seismic data.
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Why is it important?
Calibrating process-based geological models to seismic data is critical and has been challenging for decades. The traditional approach to data calibration involves tuning the model input parameters by trial-and-error or through an automated inverse procedure. This can improve the model calibration to data but can hardly reach a fully satisfactory result. We adopt a multiple-point statistics (MPS) approach where a process-based geological model is used as a training image for statistical pattern recognition. First, we define a rock physics model from the process-based geological model and derive its seismic attributes through seismic forward modeling. Then, we use the process-based model and its seismic attributes as coupled training images for geological pattern recognition and regeneration under seismic data constraint.
Perspectives
The concept of regionalized MPS for well and seismic data conditioning, together with the gradual deformation method for production history matching leads to a geologically and mathematically consistent solution to integrated reservoir modeling.
Lin Ying Hu
Read the Original
This page is a summary of: REGIONALIZED MULTIPLE-POINT STATISTICAL SIMULATION FOR CALIBRATING PROCESS-BASED GEOLOGICAL MODELS TO SEISMIC DATA, Interpretation, July 2024, Society of Exploration Geophysicists,
DOI: 10.1190/int-2023-0123.1.
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