Data-Driven In-Situ Injection for Adaptive Production Flow Monitoring
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Solution Overview
Problem
Accurate production monitoring flow models are difficult to develop due to differences between well and reservoir properties, production depletion over time, drilling and wellbore related effects, and limitations of flow loop test facilities, which conventional technology has struggled to address.
Innovation Solution
Utilizing a data-driven approach with distributed fiber optic sensing and subsurface sensors to measure inflow rates and generate well-specific production flow models, which can be customized and updated over time, incorporating zonal inflow control to optimize production flow.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional flow loop test facilities are used to develop production monitoring models, then device complexity is reduced, but measurement precision and reliability deteriorate due to noise environment and scale limitations
Solution Approach 1:
The patent replaces conventional mechanical flow loop test facilities with a data-driven modeling approach using fiber optic sensing and subsurface sensors. This substitution eliminates the physical test facility while achieving higher measurement precision through direct in-situ measurements and machine learning algorithms that adapt to well-specific conditions.
Solution Approach 2:
The patent introduces fiber optic sensing lines and subsurface sensors as intermediaries between the wellbore and the monitoring system. These sensors act as mediators that directly measure production parameters in-situ, providing accurate data without requiring complex physical test facilities or flow loops.
2Adaptability or versatility
If conventional production monitoring methods are used, then device complexity is low, but adaptability deteriorates due to inability to account for well-to-well variations and production depletion over time
Solution Approach 1:
The patent implements dynamic, adaptive flow models that continuously learn and adjust to well-specific conditions and production depletion over time. The machine learning algorithms are designed to adapt to changing reservoir characteristics and well performance, providing customized monitoring for each well while accounting for temporal variations.
Solution Approach 2:
The patent changes the approach from fixed conventional monitoring parameters to dynamic parameters that are continuously optimized based on in-situ measurements. The system adjusts flow model parameters, sensor thresholds, and prediction algorithms to match actual well behavior, enabling well-specific customization without requiring complex hardware modifications.
3Reliability
If in-situ sensor deployment is implemented, then measurement precision and reliability improve, but device complexity and installation difficulty increase
Solution Approach 1:
The patent employs fiber optic sensing lines that serve multiple functions: they act as both temperature sensors and acoustic/vibration sensors for production monitoring. This multi-functionality reduces the need for separate sensor systems while improving reliability through redundant measurement capabilities and cross-validation of data from different sensing modes.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables accurate and adaptive production monitoring by generating customized production flow models that improve well productivity through real-time adjustments, enhancing hydraulic fracturing efficiency and reducing uncertainties.
Implementation Method 1
receiving sensor data from at least one of a distributed fiber optic sensing line positioned along a wellbore
Data Source
AI summary
Aspects of the subject technology relate to systems and methods for optimizing production flow monitoring by utilizing data driven in-situ injection. Systems and methods are provided for receiving sensor data from at least one of a distributed fiber optic sensing line positioned along a wellbore and a plurality of subsurface and surface sensors, generating flow models based on the sensor data received from the at least one of the distributed fiber optic sensing line and the plurality of subsurface and surface sensors to optimize production flow, and generating flow profiles based on the flow models and the sensor data received from the at least one of the distributed fiber optic sensing line and the plurality of subsurface and surface sensors to adjust zonal inflow device.