Integrated Formation Modeling Using Seismic and Downhole Fluid Analysis
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Solution Overview
Problem
Current formation modeling systems, particularly those using the Bantzle & Wang model, inaccurately describe reservoir fluids due to oversimplification and failure to capture spatial variations, leading to unreliable differentiation between hydrocarbon, gas, and water formations, especially in low resistivity pays.
Innovation Solution
An integrated modeling system that combines seismic data with downhole fluid analysis (DFA) data to refine and modify formation models, allowing for real-time fluid property measurement and integration into seismic inversion processes to accurately identify fluid distribution and characteristics within the reservoir.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If the Bantzle & Wang model is used to calculate fluid properties, then the modeling process is simplified, but the accuracy of reservoir fluid description deteriorates due to oversimplification and failure to capture spatial variations
Solution Approach 1:
The patent combines multiple data sources (seismic data, well logs, DFA data) and multiple modeling approaches (Bantzle & Wang model, rock physics models, machine learning models) into an integrated formation modeling system. This merging allows the system to maintain the computational simplicity of the Bantzle & Wang model while compensating for its inaccuracies through integration with other data sources and models, thereby resolving the contradiction between ease of manufacture and measurement precision.
Solution Approach 2:
The patent creates a composite modeling approach by integrating multiple models and data types. The formation model is constructed as a composite of seismic inversion results, rock physics model predictions, machine learning predictions, and DFA data constraints. This composite structure allows the system to leverage the simplicity of the Bantzle & Wang model while incorporating the accuracy benefits of more complex models and real measurement data.
2Loss of time
If traditional seismic inversion is used without DFA data integration, then the processing time is reduced, but the ability to differentiate between hydrocarbon, gas, and water formations deteriorates
Solution Approach 1:
The patent performs preliminary integration of DFA data into the formation modeling process by using it to constrain rock physics models and update formation models before final interpretation. The system pre-processes DFA data to extract fluid property constraints that are then incorporated into the modeling workflow, allowing faster processing while maintaining high differentiation accuracy between hydrocarbon, gas, and water formations.
Solution Approach 2:
The patent implements feedback mechanisms where DFA measurements are used to update and refine the formation model iteratively. The system uses DFA-derived fluid properties to constrain and validate model predictions, creating a feedback loop that continuously improves formation differentiation accuracy. This feedback approach allows the system to maintain high reliability without requiring excessive processing time.
3Use of energy by moving object
If the Bantzle & Wang model is used, then computational resources are reduced, but the reliability of identifying low resistivity pays deteriorates
Solution Approach 1:
The patent introduces DFA data and rock physics models as intermediaries between the simple Bantzle & Wang calculations and the complex task of identifying low resistivity pays. The DFA data provides direct measurements of fluid properties that act as a bridge, constraining the model predictions and improving the reliability of low resistivity pay identification without requiring excessive computational resources. The rock physics models serve as intermediaries that translate basic fluid properties into formation characteristics.
Data Source
AI summary
Integrated formation modeling systems and methods are described. An example method of performing seismic analysis of a subterranean formation includes obtaining seismic data of the formation, obtaining fluid from the formation and analyzing at least some of the fluid to determine a fluid parameter. The example method additionally includes generating a model of the formation based at least on the seismic data and modifying the model based on the fluid parameter.