DAS-DTS Inversion for Reservoir Production Logging
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Solution Overview
Problem
Current well logging techniques face challenges in achieving high spatial and temporal resolution for measuring borehole temperatures and flow velocities, leading to less reliable production zone fluid allocation and increased costs due to mechanical issues with spinner-type flow meters and limitations in fiber-optic Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) technologies.
Innovation Solution
A novel method utilizing fiber-optic Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) combined with Markov Chain Monte Carlo based stochastic inversion to optimize hydrocarbon reservoir production, which includes measuring low-frequency DAS and DTS data during various flow periods, creating initial models, and iteratively inverting them to predict production profiles, potentially with the aid of a borehole heater to enhance temperature differentials.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If spinner-type flow meters are used to measure flow velocity, then flow velocity can be measured, but mechanical wear and friction affect measurement reliability and require frequent maintenance
Solution Approach 1:
The patent replaces mechanical spinner-type flow meters with fiber-optic Distributed Acoustic Sensing (DAS) technology that uses optical signals instead of mechanical moving parts. The DAS system measures flow velocity by detecting acoustic signals along the fiber optic cable, eliminating mechanical wear and friction issues while maintaining measurement capability.
2Measurement precision
If fiber-optic DTS is used to measure temperature, then continuous temperature profile can be obtained, but spatial and temporal resolution are insufficient for high-precision production logging
Solution Approach 1:
The patent combines fiber-optic Distributed Temperature Sensing (DTS) with Distributed Acoustic Sensing (DAS) into a single integrated system. This merging allows simultaneous acquisition of both temperature and flow velocity data with matched high spatial and temporal resolution, enabling reliable production logging through joint inversion of both datasets.
3Measurement precision
If calibration is performed downhole at various logging speeds, then measurement accuracy is improved, but logging time and operational complexity increase significantly
Solution Approach 1:
The fiber-optic sensing system performs self-calibration through the joint inversion process. By simultaneously inverting both DTS temperature data and DAS flow velocity data, the system automatically determines the correct measurement scale and calibration factors without requiring manual downhole calibration at various speeds, thereby eliminating time loss while maintaining accuracy.
4Ease of repair
If fiber-optic sensing is used to eliminate moving parts, then maintenance requirements are reduced, but spatial resolution and signal detection capability are limited
Solution Approach 1:
The patent employs dynamic signal processing techniques including wavelet transforms and advanced inversion algorithms that adapt to varying flow conditions. This dynamic approach enhances the spatial resolution and signal detection capability of the fiber-optic system without requiring physical changes to the sensing infrastructure, maintaining ease of repair while improving measurement precision.
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
This method provides improved spatial and temporal resolution for fluid allocation in hydrocarbon reservoirs, reducing uncertainty and increasing the reliability of production logging results, thereby optimizing hydrocarbon production and reducing costs by leveraging the strengths of fiber-optic sensing technologies.
Implementation Method 1
One technique for substantially instantaneous temperature measurement is fiber optic Distributed Temperature Sensing (DTS) technology. Temperatures are recorded along the optical sensor cable, thus not at points, but as a continuous temperature profile.
Implementation Method 2
uses Distributed Acoustic Sensing (DAS) to measure borehole flow velocities by tracking temperature slugging signals
Implementation Method 3
potentially with the aid of a borehole heater to enhance temperature differentials
Data Source
AI summary
A method of optimizing production of a hydrocarbon-containing reservoir by measuring low-frequency Distributed Acoustic Sensing (LFDAS) data in the well during a time period of constant flow and during a time period of no flow and during a time period of perturbation of flow and simultaneously measuring Distributed Temperature Sensing (DTS) data from the well during a time period of constant flow and during a time period of no flow and during a time period of perturbation of flow. An initial model of reservoir flow is provided using the LFDAS and DTS data; the LFDAS and DTS data inverted using Markov chain Monte Carlo method to provide an optimized reservoir model, and that optimized profile utilized to manage hydrocarbon production from the well and other asset wells.