Integrated Near Wellbore Performance Analyzer
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Solution Overview
Problem
Current predictive methodologies for near wellbore performance in the energy industry are insufficient, leading to inadequate prediction of complex nonlinear inflow characteristics and reservoir structural performance, resulting in poor business decisions due to uncertainty ranges.
Innovation Solution
An integrated processing system that includes mathematical models and simulation tools for predicting near wellbore region characteristics over time and space, allowing for multi-dimensional simulations and optimization of downhole components, enabling more accurate completion design and performance prediction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current predictive methodologies are used for near wellbore performance, then the analysis process is simple and quick, but the prediction accuracy is insufficient and uncertainty ranges are large
Solution Approach 1:
The patent combines multiple previously separate analysis tools and methodologies into a single integrated near wellbore performance analyzer. This integration merges formation evaluation, completion design, and performance prediction functions into one unified system, enabling comprehensive multi-dimensional simulations while maintaining a user-friendly interface that hides the underlying complexity.
Solution Approach 2:
The analyzer is designed as a universal platform that can perform multiple functions including formation characterization, completion design optimization, and performance prediction across different well types and reservoir conditions. This multi-functionality allows a single tool to replace multiple specialized tools while maintaining expertise in each area.
2Reliability
If simple analysis methods are used, then the computational time is short, but the ability to predict complex nonlinear inflow characteristics is inadequate
Solution Approach 1:
The system performs preliminary formation characterization and model calibration using available data before conducting full performance simulations. This preliminary action prepares the models in advance, reducing the computational time required for subsequent predictions while maintaining high reliability through pre-validated formation parameters.
Solution Approach 2:
The analyzer dynamically adjusts the level of simulation complexity based on the specific problem requirements and available data quality. For routine cases, simplified models provide quick results, while for complex scenarios, the system automatically engages more sophisticated nonlinear simulations to ensure prediction reliability.
3Loss of information
If comprehensive multi-dimensional simulations are performed, then the uncertainty ranges are reduced, but the computational resources and time required increase
Solution Approach 1:
The comprehensive simulation process is segmented into distinct modules: formation characterization, completion design, and performance prediction. Each module can be executed independently or in combination, allowing users to perform targeted analyses without always requiring full multi-dimensional simulations, thus maintaining productivity while reducing uncertainty when needed.
Solution Approach 2:
The system changes simulation parameters dynamically based on input data quality and problem complexity. When sufficient high-quality data is available, the system engages comprehensive simulations to reduce uncertainty. When data is limited or quick decisions are needed, the system uses simplified parameter sets that maintain reasonable accuracy while preserving analysis efficiency.
Data Source
AI summary
A method of performing aspects of an energy industry operation includes receiving input data at a processing system, the input data describing an assembly for performing the energy industry operation and properties of the formation, the assembly including a downhole component, the processing system configured to estimate production properties based on mathematical models including at least a model of the downhole component and one or more models for simulating fluid flow in the formation. The method also includes, based on the input data, generating a workflow that includes steps for estimating production properties using the models, receiving a selection from a user specifying a type of analysis to be performed and/or a level of complexity of analysis to be performed, customizing the workflow based on the user selection, estimating the production properties based on the models, where estimating is performed according to a procedure specified by the workflow.