LFDAS and DTS Well Logging Inversion for Reservoir Production
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Solution Overview
Problem
Current well logging techniques face challenges in achieving high spatial and temporal resolution, especially in horizontal wells, and often require mechanical tools that are prone to wear and require frequent calibration, leading to increased costs and less reliable measurements.
Innovation Solution
The method employs fiber-optic Distributed Temperature Sensing (DTS) and Distributed Acoustic Sensing (DAS) to measure borehole temperatures and flow velocities, respectively, and applies a Markov Chain Monte Carlo based stochastic inversion to determine the statistical distribution of production allocations that fit both temperature and velocity measurements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If mechanical logging tools (spinner flow meters with temperature sensors) are used, then production logging measurements can be obtained, but the tools suffer from mechanical wear, require frequent calibration and replacement, and need borehole cleanup to prevent sensor damage
Solution Approach 1:
The patent replaces mechanical logging tools (spinner flow meters with moving impellers and bearings) with fiber-optic sensing technology that uses optical principles. The fiber-optic cable contains no mechanical moving parts, eliminating wear and friction issues. Temperature sensors are integrated into the fiber-optic system rather than being separate mechanical components, removing the need for mechanical calibration and borehole cleanup.
Solution Approach 2:
The patent extracts the sensing function from mechanical tools and relocates all sensing instruments to the surface. The fiber-optic cable acts as a passive transmission medium carrying optical signals, while the active sensing and processing equipment remains on the surface, eliminating the need for mechanical components downhole.
2Measurement precision
If mechanical logging tools are used, then production data can be collected, but calibration requires multiple extra logging runs at various speeds, significantly increasing logging time and cost
Solution Approach 1:
The patent replaces mechanical flow measurement (spinner meters requiring rotation at different speeds for calibration) with acoustic sensing through the fiber-optic cable. DAS detects flow-induced acoustic signals and vibrations directly, providing accurate flow velocity measurements without mechanical calibration. The system measures flow characteristics through acoustic wave propagation in the fluid, eliminating the need for multiple calibration runs.
3Reliability
If traditional logging tools are used in horizontal wells, then measurements can be obtained, but fluid segregation reduces measurement reliability
Solution Approach 1:
The patent creates a universal measurement system based on fiber-optic sensing that functions effectively in both vertical and horizontal well configurations. The distributed acoustic and temperature sensing along the fiber-optic cable provides consistent measurement capability regardless of well orientation, adapting to horizontal well fluid dynamics without requiring mechanical adjustments or specialized tool configurations.
Solution Approach 2:
The patent replaces mechanical flow meters that rely on fluid flow to rotate impellers with acoustic sensing that detects flow characteristics through sound wave propagation. This substitution eliminates the dependency on specific flow patterns caused by fluid segregation in horizontal wells, as acoustic sensing can detect flow presence and characteristics even when fluid distribution is non-uniform.
4Measurement precision
If fiber-optic sensing is used, then measurements can be obtained along the entire fiber length with high spatial resolution, but the system requires sophisticated signal processing and inversion algorithms
Solution Approach 1:
The patent introduces a sophisticated inversion algorithm as an intermediary between the raw fiber-optic sensing data and the final production logging results. The Markov Chain Monte Carlo-based inversion process acts as a computational mediator that transforms the complex distributed temperature and acoustic signals into meaningful production allocation estimates, handling the high-dimensional data processing required for distributed sensing.
Solution Approach 2:
The patent uses synthetic test data as a computational copy of real well conditions to validate and calibrate the inversion algorithms. By creating virtual representations of production scenarios and testing the signal processing system against these synthetic datasets, the methodology ensures accurate interpretation of actual fiber-optic measurements while refining the complex processing algorithms.
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 approach provides improved spatial and temporal resolution for well logging, reduces costs by eliminating the need for mechanical tools, and enhances the reliability of measurements, especially in horizontal wells, while allowing for real-time optimization of reservoir planning and hydrocarbon production.
Implementation Method 1
Fiber-optic sensing technology has been developed in oil industry recently. One technique for substantially instantaneous temperature measurement is fiber optic Distributed Temperature Sensing (DTS) technology.
Implementation Method 2
uses Distributed Acoustic Sensing (DAS) to measure borehole flow velocities by tracking temperature slugging signals
Implementation Method 3
Fiber-optic sensing can provide measurements along the entire fiber length (as long as 10 miles) with a spatial resolution in terms of feet
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.