Subsoil Formation Modeling via Adaptive Mesh and Stochastic Trajectories
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Solution Overview
Problem
Existing methods for determining subsoil formations in the hydrocarbon industry face challenges such as meshing biases, inability to account for sedimentary bodies during modeling, difficulty in satisfying well constraints, and improper identification of geological objects and facies due to pre-meshed models and probability-based constraints.
Innovation Solution
A method that involves receiving a model with a stratigraphic layer divided by a shoreline into continental and marine zones, determining weighted flow speed fields based on distance to the shoreline, and using these fields to calculate a global flow speed for each position, allowing for accurate determination of subsoil geological formations without pre-meshing, along with techniques like parametric surfaces for fluvial formations and stochastic processes for trajectory determination.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If meshing is performed before modeling, then the modeling process can be initiated, but the mesh size, orientation, and number induce bias in the modeling and cannot account for sedimentary bodies to be identified during modeling
Solution Approach 1:
The patent applies preliminary action by performing meshing before modeling, but optimizes this approach by using a preliminary mesh that is subsequently refined based on identified sedimentary bodies. The method involves an initial mesh generation followed by iterative refinement where sedimentary bodies are detected and the mesh is adapted to their geometry, thus maintaining productivity while improving precision.
Solution Approach 2:
The patent implements dynamics by making the mesh adaptive rather than static. The mesh structure evolves during the modeling process based on the identification of sedimentary bodies. The mesh parameters (size, orientation, number of elements) are dynamically adjusted to conform to the geological features being modeled, eliminating the bias introduced by fixed pre-meshing.
2Productivity
If pre-meshed models are used, then modeling can proceed, but it becomes difficult to accurately satisfy well constraints as log data must be transformed with blocking methods
Solution Approach 1:
The patent applies inversion by reversing the traditional approach: instead of transforming log data to fit the pre-meshed model, the method adjusts the mesh and modeling to conform to the well constraints and log data. The log data is used to define constraints that guide mesh generation and sedimentary body identification, ensuring accurate satisfaction of well constraints without blocking methods.
3Productivity
If probability-based constraints are used for modeling geological objects, then modeling can be performed, but specific relations between objects are not taken into account and cell association with facies is difficult
Solution Approach 1:
The patent applies local quality by transitioning from uniform probability-based constraints to location-specific constraints derived from well data and geological knowledge. Each region of the model receives constraints tailored to its specific geological context, allowing accurate representation of spatial relationships between geological objects and enabling proper association of cells with facies based on local geological conditions.
Data Source
AI summary
The present disclosure relates to a method for determination of a real subsoil geological formation. In at least one embodiment, the method includes receiving a model representing the real subsoil. The model includes a shore line and a fluvial formation connected to the shore line at a point of the model. The shore line divides the model into a marine zone and a continental zone. The method further includes determining a delta zone in the model based on the shore line and based on the fluvial formation, determining a stochastic trajectory in said delta zone, and determining a lobe formation in said delta zone based on the determined stochastic trajectory based on a stochastic process.