NMR Fluid Identification via Multi-Spacing Inversion
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Solution Overview
Problem
Current NMR logging technologies face challenges in accurately determining fluid properties in earth formations due to overlapping NMR quantities for multiple fluids, leading to uncertainty in fluid typing and quantification, especially in reservoirs with complex conditions.
Innovation Solution
The use of a data acquisition tool equipped with NMR sensors that acquires echo trains data, performs inversions using equations to derive T1-T2 maps, and applies physical constraints to distinguish fluid types, enabling more precise fluid differentiation through forward modeling and second inversion processes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional NMR logging is used to measure fluid properties, then basic fluid detection is possible, but measurement precision deteriorates due to overlapping NMR quantities for multiple fluids
Solution Approach 1:
The patent segments the fluid identification process by dividing it into multiple distinct steps: acquiring NMR data at different spacings, performing initial inversion to obtain T2 distributions, conducting forward modeling, and executing a second inversion. This segmentation allows each step to address specific aspects of fluid characterization, improving overall measurement precision by systematically separating overlapping fluid signals that cannot be resolved in a single measurement step.
Solution Approach 2:
The patent introduces additional measurement dimensions by acquiring NMR data at multiple different spacings between the transmitter and receiver coils. This creates a multi-dimensional dataset that extends beyond traditional single-spacing measurements. By utilizing this additional dimensional information, the system can differentiate between fluids with overlapping NMR signatures, as each fluid responds differently to variations in spacing, thereby improving fluid typing accuracy.
2Measurement precision
If multiple NMR measurements are performed to improve fluid differentiation, then measurement precision improves, but device complexity increases due to multiple inversion processes
Solution Approach 1:
The patent performs preliminary actions by conducting the first inversion to obtain T2 distributions and performing forward modeling before the second inversion. These preliminary steps prepare the data by extracting initial fluid characteristics and simulating expected responses, which simplifies the subsequent second inversion process. This preliminary processing reduces the computational burden of the final inversion, making the overall complex process more manageable and efficient.
Solution Approach 2:
The patent introduces forward modeling as an intermediary step between the first inversion and the second inversion. This intermediary process acts as a bridge that translates the T2 distributions from the first inversion into predicted NMR responses, which are then compared with actual measurements in the second inversion. This intermediary step facilitates the complex multi-step analysis by providing a structured intermediate representation that simplifies the relationship between measurements and fluid properties.
3Measurement precision
If forward modeling and second inversion are applied to separate fluid signals, then measurement precision improves, but loss of time increases due to additional processing steps
Solution Approach 1:
The patent maintains continuity of useful action by ensuring that each processing step builds directly on the previous step without interruption. The first inversion continuously feeds into forward modeling, which in turn continuously feeds into the second inversion. This continuous workflow minimizes idle time and ensures that the computational process flows efficiently from one stage to the next, reducing overall processing time while maintaining the necessary multi-step analysis for accurate fluid signal separation.
Solution Approach 2:
The system performs self-service by automatically executing the multi-step inversion and forward modeling process without requiring manual intervention between steps. The computational workflow is designed to run autonomously, with each step automatically processing the output of the previous step and generating input for the next step. This automated self-service approach reduces the time that would otherwise be spent on manual data transfer and processing setup.
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 enhances the separation and identification of fluid signals, particularly between water and heavy oil, and other hydrocarbons, improving the accuracy of fluid typing and quantification in earth formations.
Implementation Method 1
NMR logging measures the induced magnet moment of hydrogen nuclei contained within fluid-filled pores in porous material, such as rocks
Data Source
AI summary
An apparatus comprising a data acquisition tool including NMR sensors, a data acquisition processor communicatively coupled with the NMR sensors, and a first memory storing instructions that cause the data acquisition processor to perform operations comprising acquiring data of earth formation fluid, varying at least one of a magnetic field gradient and an inter-echo time, and acquiring additional data. The apparatus further comprises a data processing unit comprising a second memory storing instructions that cause the data processor to perform operations comprising receiving data acquired by the data acquisition tool, constructing a mathematical model of the data, conducting a first inversion of the mathematical model to obtain a first set of NMR responses, performing a forward model of the first set of NMR responses obtained from the first inversion, conducting a second inversion to obtain a second set of NMR responses, and determining earth formation fluid properties.