Sequential Uncertainty Characterization for Hydrocarbon Data Collection
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Solution Overview
Problem
The oil and gas industry faces challenges in making informed business decisions due to limited knowledge of subsurface geological properties, which are costly to characterize using techniques like seismic imaging and appraisal drilling, requiring a more systematic and efficient method to reduce uncertainty in subsurface characterization.
Innovation Solution
A computer-implemented method that selects and implements a set of data collection components by quantitatively analyzing potential components for uncertainty reduction, using seismic and well-specific designs to collect data that characterizes subsurface quantities of interest, iteratively refining the data collection program to meet predefined uncertainty targets.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If seismic imaging and appraisal drilling techniques are used to characterize subsurface geological properties, then measurement precision and reliability of subsurface characterization is improved, but cost and device complexity increase significantly
Solution Approach 1:
The patent segments the subsurface characterization process into multiple phases (exploration, appraisal, development, production) with specific quantities of interest for each phase. This segmentation allows targeted data collection for each phase rather than comprehensive characterization at all times, reducing overall complexity while maintaining necessary precision for decision-making at each stage.
Solution Approach 2:
The patent performs preliminary uncertainty analysis to identify which quantities of interest require characterization before proceeding with data collection. By determining upfront which subsurface properties need to be characterized for specific business decisions, the method avoids unnecessary data collection activities, reducing complexity while ensuring measurement precision for critical parameters.
2Reliability
If comprehensive seismic imaging and appraisal drilling are conducted to reduce subsurface uncertainty, then reliability of subsurface model is improved, but loss of time and productivity decrease
Solution Approach 1:
The patent applies partial action by collecting only the specific data needed to reduce uncertainty for predefined quantities of interest at each phase, rather than conducting comprehensive characterization. The uncertainty analysis identifies the minimum necessary data collection to achieve sufficient model reliability for business decisions, reducing time loss while maintaining necessary reliability.
Solution Approach 2:
The patent implements a dynamic data collection program that adapts to the current phase of hydrocarbon extraction and the specific quantities of interest for that phase. The uncertainty analysis is performed sequentially, allowing the data collection strategy to evolve dynamically based on what has been learned and what decisions need to be made next, optimizing the balance between reliability and time efficiency.
3Measurement precision
If multiple data collection components are implemented to characterize all subsurface parameters, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent applies local quality by tailoring the data collection program to the specific quantities of interest for each extraction phase. Different phases require different levels of precision for different subsurface parameters. The uncertainty analysis identifies which parameters need precise characterization locally at each phase, rather than uniformly high precision across all parameters at all times, reducing the number of data collection components needed.
Solution Approach 2:
The patent changes the parameters being measured based on the phase and specific business needs. The uncertainty analysis identifies which quantities of interest require characterization at each phase, and the data collection program is adjusted accordingly. This parameter-based approach allows the system to focus measurement precision on critical parameters while reducing or eliminating data collection for non-critical parameters, reducing overall complexity.
Data Source
AI summary
A method for determining and implementing a data collection program is disclosed. Data is typically collected in order to develop a subsurface model that can characterize a subsurface to assist in hydrocarbon management. However, it may be difficult to determine how much, or what type of data, to obtain so that the subsurface model is of sufficient certainty. In particular, parameters that define the model and outputs of the model (defined as quantities of interest (QoIs)) are subject to uncertainty. In order to reduce the uncertainty of the QoIs to an acceptable level, data collection programs are iteratively selected based on sequential subsurface uncertainty characterization. In this way, the data collection programs, when implemented, may collect a sufficient amount of data to reduce uncertainty of the subsurface model for subsequent use in hydrocarbon management.