AI Reservoir 3D Modeling from Seismic, Well Logs, and Production Data
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Solution Overview
Problem
Reservoir modeling is a time-intensive and manually intensive process that involves lengthy steps such as geophysical interpretation, geological framework building, fault modeling, and dynamic simulation, which can be improved through automated methods using machine learning and artificial intelligence.
Innovation Solution
Integrate seismic and well log data to train machine learning models that generate static and dynamic reservoir 3D models, bypassing traditional manual processes by leveraging seismic attributes and well log data to create enhanced logs, which are then grouped into ensembles for model formation, and continuously updated with real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional manual reservoir modeling processes are used, then model accuracy can be maintained through expert interpretation, but the process is extremely time-intensive and requires weeks to complete
Solution Approach 1:
The patent replaces manual mechanical interpretation processes with machine learning algorithms and automated computational systems. ML models process seismic and well log data to generate reservoir models automatically, substituting human expert manual analysis with algorithmic processing that operates continuously without fatigue or delays.
Solution Approach 2:
The patent creates digital copies and representations of physical reservoir data through machine learning-generated models. Multiple ML models generate ensemble predictions that replicate and refine reservoir characteristics, allowing parallel processing of numerous model iterations without requiring sequential manual review of each scenario.
2Ease of operation
If multiple manual steps including geophysical interpretation, fault modeling, and dynamic simulation are performed sequentially, then comprehensive reservoir analysis is achieved, but the process becomes lengthy and manually intensive
Solution Approach 1:
The patent merges multiple separate modeling steps into an integrated automated workflow. ML models for seismic interpretation, fault detection, and dynamic simulation are combined into a unified system that processes data through all stages automatically, eliminating the need for sequential manual transitions between different analysis phases.
Solution Approach 2:
The patent employs universal machine learning frameworks that can perform multiple functions across different modeling stages. The same ML infrastructure handles geophysical interpretation, geological framework building, fault modeling, and dynamic simulation, allowing a single automated system to replace multiple specialized manual processes.
3Reliability
If traditional workflows are used, then interpretational control is maintained, but the process requires extensive manual intervention and is time-dependent
Solution Approach 1:
The patent implements feedback mechanisms where ML model predictions are continuously validated against actual well data and reservoir performance. The system learns from discrepancies between predicted and observed values, automatically adjusting parameters and refining models to maintain or improve accuracy while operating autonomously without manual intervention.
Data Source
AI summary
Methods and apparatus for generating one or more reservoir 3D models are provided. In one or more embodiments, a method can include training a first machine learning model to generate one or more integrated enhanced logs based, at least in part, on an integrated data set, wherein the integrated data set includes seismic data and well log data; generating one or more integrated enhanced logs from the first machine learning model; grouping the one or more integrated enhanced logs into an ensemble of integrated enhanced logs to form a static reservoir 3D model of a subterranean reservoir; inputting additional data to the first machine learning model to produce one or more updated integrated enhanced logs; and grouping the one or more updated integrated enhanced logs into an ensemble of updated integrated enhanced logs to form an updated 3D model.