Autonomous Pressure Gradient Identification Using Discrete Optimization
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Solution Overview
Problem
Interpreting depth-pressure measurements in oil and gas exploration is challenging due to multiple possible solutions for a single set of measurements, requiring expert opinion and involving complex models affected by noise and uncertainty, which can lead to unreliable results.
Innovation Solution
A computational method and system that autonomously interpret pressure gradients from depth-pressure measurements by solving a mixed-integer linear programming problem, incorporating noise models and physical domain constraints to determine plausible solutions, using a solution-enumerating simplex-decomposition-generating algorithm to process noise and characterize uncertainty.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If multiple pressure gradient models (empirical, semi-empirical, machine learning) are used to interpret depth-pressure measurements, then the interpretation flexibility increases, but the reliability of the results decreases due to multiple possible solutions
Solution Approach 1:
The solution space is segmented into multiple discrete pressure gradient models (empirical, semi-empirical, machine learning). Each model represents a distinct segment of possible interpretations. The system evaluates each segment separately and combines results to provide a comprehensive view of all plausible solutions, thereby maintaining reliability while preserving flexibility.
Solution Approach 2:
The system changes the parameter of model selection by allowing dynamic switching between different pressure gradient models based on the specific measurement context. This enables the system to adapt to different scenarios (improving versatility) while maintaining rigorous evaluation criteria for each model type (preserving reliability).
2Measurement precision
If expert opinion is required to interpret depth-pressure measurements, then the accuracy of interpretation improves, but the automation level and efficiency decrease
Solution Approach 1:
The system performs self-service by automatically evaluating multiple pressure gradient models and selecting the most appropriate interpretation without requiring external expert intervention. The automated system incorporates built-in validation mechanisms that simulate expert judgment criteria, thereby maintaining high accuracy while achieving full automation.
Solution Approach 2:
The mechanical system of expert human judgment is replaced with an automated computational system that uses algorithmic evaluation of multiple models. This substitution maintains interpretation accuracy through rigorous mathematical evaluation while eliminating the need for human experts, thereby increasing automation level.
3Measurement precision
If complex models including compositional changes, capillary pressure, and relative permeability are used, then the measurement precision improves, but the device complexity and computational requirements increase
Solution Approach 1:
The complex model is segmented into distinct physical components (compositional changes, capillary pressure, relative permeability). Each component is evaluated separately through dedicated sub-models, allowing the system to manage complexity systematically while maintaining overall measurement precision through the integration of all components.
Data Source
AI summary
A method and system for identifying a fluid within a subterranean formation. The method may comprise obtaining one or more pressure measurements at one or more depths with a downhole fluid sampling tool, forming a depth-pressure measurement set form the one or more pressure measurements, creating a solution novelty threshold from at least the depth-pressure measurement set, constraining a solution space with the solution novelty threshold, and finding a solution-space-inscribed simplex within the solution novelty threshold. The method may further comprise generating a simplicial decomposition for a convex hull of the solution-space-inscribed simplex up to the solution novelty threshold, identifying at least one inscribed simplex within the convex hull of the solution-space-inscribed simplex, determining a novel simplex interior with the at least one inscribed simplex, and forming a plurality of solutions with the novel simplex interior.