Oil and Gas Portfolio Optimization with Dependency Modeling
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Solution Overview
Problem
Current oil and gas exploration portfolio optimization methods fail to account for geological, economic, and operational dependencies between prospects, leading to misrepresentation of true risk and reward, resulting in sub-optimal drilling sequences and lost opportunities.
Innovation Solution
A method and system that generate prospect inputs, store them in a data memory, and use a simulator module to create correlation matrices, determine drilling sequences based on a budget, and model these sequences using Monte Carlo simulation to find an optimal drilling sequence considering the dependencies, which is then translated for implementation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If prospects are ranked based on standalone analysis independent of other prospects, then the ranking process is simple and fast, but the true risk and reward of the portfolio is misrepresented
Solution Approach 1:
The patent combines individual prospect analysis with portfolio-level dependency analysis. The system integrates geological, economic, and operational dependencies between prospects to evaluate the portfolio as a unified system, rather than treating each prospect in isolation. This merging approach captures the true risk and reward characteristics of the portfolio while maintaining computational efficiency through structured dependency modeling.
Solution Approach 2:
The system implements feedback loops where drilling outcomes from one prospect update the risk and reward assessments of related prospects in the portfolio. As exploration progresses and new information becomes available, the dependency model dynamically adjusts rankings and recommendations, ensuring that the portfolio assessment remains accurate and reflects the current state of knowledge.
2Measurement precision
If dependencies between prospects are captured and quantified, then the true risk and reward assessment is improved, but the analysis complexity increases
Solution Approach 1:
The patent segments the complex dependency analysis into distinct modules: geological dependencies, economic dependencies, and operational dependencies. Each dependency type is modeled separately using standardized frameworks, allowing the system to manage complexity through modular design. This segmentation enables the integration of multiple dependency types without creating an intractably complex analysis system.
Solution Approach 2:
The system transforms complex qualitative dependencies into quantitative parameters that can be processed computationally. Geological, economic, and operational relationships are converted into measurable parameters and correlation coefficients, enabling the use of mathematical models and optimization algorithms to evaluate portfolio performance while maintaining analytical rigor.
3Measurement precision
If more information is gathered about prospects during exploration, then the risk and reward assessment of related prospects improves, but the time and resources required increase
Solution Approach 1:
The system performs preliminary dependency analysis and identifies high-value information needs before full-scale exploration begins. By pre-mapping the dependency network between prospects, the system can prioritize which prospects to explore first and what information would provide the greatest value for updating portfolio assessments, thereby reducing overall exploration time and resource requirements.
Solution Approach 2:
The prospect assessment system is designed to be dynamic and adaptive, continuously updating risk and reward evaluations as new information becomes available during exploration. Rather than requiring complete information before making decisions, the system adapts rankings and recommendations in real-time based on incoming data, allowing for efficient sequential decision-making that reduces total exploration time.
Data Source
AI summary
In accordance with one embodiment of the present disclosure, a method includes receiving exploration constraints, including a budget, receiving prospect inputs for a plurality of prospects, each prospect input having fixed inputs and dynamic inputs, generating correlation matrices based on the dynamic inputs, determining a set of drilling sequences for the plurality of prospects based on the budget, modeling, by Monte Carlo simulation, each drilling sequence of the set of drilling sequences within the prospect inputs, wherein each iteration of modeling is complete when the exploration constraints are reached, and generating an optimal drilling sequence, including a risk and a reward for the optimal drilling sequence.