AI Framework for Reservoir Fluid Geodynamics Interpretation
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Solution Overview
Problem
The interpretation of downhole fluid analyses in oilfield operations is non-unique and heavily dependent on expert knowledge and creativity due to the complex interactions of phenomena over significant geological time, making it challenging to accurately model and predict fluid compositions and properties.
Innovation Solution
An artificial intelligence framework using probabilistic Bayesian networks, causal maps, or factor graphs to model and interpret reservoir fluid dynamics processes, allowing for the identification of parameter values and determination of uncertain interactions over space and time, thereby providing insights into fluid evolution and properties.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge and creativity are used to interpret downhole fluid analyses, then interpretation accuracy may improve, but the process becomes heavily dependent on individual expertise and is non-unique
Solution Approach 1:
The patent replaces the mechanical system of expert human analysis with an artificial intelligence system that uses probabilistic Bayesian networks, causal maps, and factor graphs to automatically interpret downhole fluid analyses. This substitution eliminates dependency on individual expert knowledge while maintaining interpretation accuracy through systematic probabilistic reasoning.
Solution Approach 2:
The patent transforms the interpretation process by changing from qualitative expert judgment to quantitative probabilistic parameters. By representing fluid evolution scenarios as probabilistic models with measurable parameters, the system enables objective, repeatable analysis that is not dependent on expert subjectivity.
2Reliability
If complex interactions of phenomena over geological time are modeled, then fluid evolution understanding improves, but model complexity and computational requirements increase
Solution Approach 1:
The patent segments the complex fluid evolution modeling into modular probabilistic components using Bayesian networks and factor graphs. Each node represents a specific phenomenon or parameter, and their relationships are defined through local probability functions. This segmentation allows the complex model to be built from manageable parts while maintaining overall accuracy.
Solution Approach 2:
The patent introduces probabilistic graphical models as intermediaries between the complex geological phenomena and the interpretation process. These graphical models serve as mediators that organize complex interactions into structured probability relationships, making the system computationally tractable while preserving the complexity of fluid evolution processes.
3Reliability
If probabilistic Bayesian networks and causal maps are used to model reservoir fluid dynamics, then interpretation objectivity improves, but computational processing requirements increase
Solution Approach 1:
The patent performs preliminary action by pre-defining the probabilistic Bayesian network structure, causal relationships, and factor graph configurations before actual fluid analysis interpretation. This pre-computation of model frameworks reduces the computational burden during actual interpretation, as the heavy lifting of model construction is done in advance.
Solution Approach 2:
The patent applies partial action by implementing probabilistic modeling only for the most critical fluid evolution scenarios and parameters. Rather than modeling all possible phenomena with equal detail, the system focuses computational resources on the most influential factors, achieving objectivity where it matters most while controlling computational costs.
Data Source
AI summary
Embodiments herein include a system and method for modeling and interpreting an evolution of fluids in an oilfield using artificial intelligence. Embodiments may include identifying, using at least one processor, one or more reservoir fluid dynamics processes or properties and generating a model for the one or more reservoir fluid dynamics processes or properties. Embodiments may include receiving, at the model, one or more parameter values corresponding to the one or more reservoir fluid dynamics processes or properties and displaying, at a graphical user interface, one or more results, based upon, at least in part, the model and the one or more parameter values.