Well Connectivity Evaluation Using Time Series Correlation
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Solution Overview
Problem
Current methods for evaluating connectivity between wells in a hydrocarbon production field are costly and require stopping production activities, making them inefficient and expensive.
Innovation Solution
A method involving the processing and filtering of time series data from well parameters to determine the maximal correlation coefficient and time shift, representing connectivity between wells, without the need to interrupt production activities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If drill stem test or well testing methods are used to measure connectivity, then measurement precision is improved, but loss of time and productivity worsen due to required production stoppage
Solution Approach 1:
The patent applies preliminary action by continuously collecting and storing production data (pressure, flow rate, injection rates) during normal field operations before any connectivity assessment is needed. This pre-collected data serves as the basis for later connectivity analysis without requiring production stoppage, thus maintaining productivity while enabling precise measurements when analysis is performed.
Solution Approach 2:
The patent replaces the mechanical well testing system (drill stem tests requiring physical intervention and production stoppage) with a data processing system that analyzes routinely collected production data. This substitution eliminates the need for mechanical testing operations while providing connectivity assessment through computational analysis of pressure and flow data.
2Measurement precision
If drill stem test or well testing methods are used to measure connectivity, then measurement precision is improved, but cost worsens due to production stoppage requirements
Solution Approach 1:
The patent applies preliminary action by continuously collecting and storing production data (pressure, flow rate, injection rates) during normal field operations before any connectivity assessment is needed. This pre-collected data serves as the basis for later connectivity analysis without requiring production stoppage, thus maintaining productivity while enabling precise measurements when analysis is performed.
Solution Approach 2:
The patent replaces the mechanical well testing system (drill stem tests requiring physical intervention and production stoppage) with a data processing system that analyzes routinely collected production data. This substitution eliminates the need for mechanical testing operations while providing connectivity assessment through computational analysis of pressure and flow data.
3Reliability
If marker methods are used to assess connectivity, then measurement capability is improved, but device complexity and cost worsen due to special equipment requirements
Solution Approach 1:
The patent applies universality by using existing multi-functional production equipment and routinely collected production data to perform connectivity assessment. The same sensors and data collection systems used for normal production monitoring are utilized for connectivity analysis, eliminating the need for specialized marker injection equipment or dedicated testing tools.
Solution Approach 2:
The patent applies self-service by enabling the production system to automatically generate connectivity assessment data through its normal operations. The production wells continuously generate pressure and flow data that can be analyzed for connectivity information without requiring external intervention, special markers, or additional equipment.
Data Source
AI summary
A method includes obtaining a first time series of a first well parameter from the first well and a second time series of a second well parameter from the second well, processing the first time series to obtain a processed first time series, filtering the second time series by removing dynamic variations from the second time series to obtain a filtered second time series representative of static variations of the second time series, and determining a correlation between the processed first time series and the filtered second time series at various time shifts between the processed first time series and the filtered second time series, and determining a maximal correlation coefficient and a time shift at maximal correlation between the processed first time series and the filtered second time series, the maximal correlation coefficient being representative of the connectivity between the first well and the second well.


