Reservoir Fluid Characterization Using Sonic Slowness Logs
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional reservoir characterization methods fail to accurately determine fluid types in unconventional reservoirs without seismic velocity data or elastic rock properties, leading to uncertainties in hydrocarbon and water differentiation.
Innovation Solution
A computer-implemented method that combines compressional and shear formation slowness with other parameters, such as bulk density and resistivity, to predict fluid types by cross-plotting ratios, eliminating the need for seismic velocity and elastic rock properties, and calibrates these parameters using core sample measurements and environmental corrections.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional reservoir characterization methods are used, then the process is simpler, but fluid type determination accuracy deteriorates in unconventional reservoirs without seismic velocity data
Solution Approach 1:
The patent transforms the problem by changing the parameter domain from seismic velocity (which is unavailable) to sonic slowness (which is available from well logs). By using compressional and shear sonic slowness measurements and transforming them into slowness ratios, the method achieves fluid type determination accuracy comparable to seismic methods while working within the constraints of unavailable seismic data.
Solution Approach 2:
The patent introduces sonic slowness as an intermediary parameter that bridges the gap between available well log data and the desired fluid type characterization. The compressional and shear slowness measurements serve as intermediaries that can be combined through cross-plotting to infer fluid properties without directly requiring seismic velocity data or elastic rock properties.
2Measurement precision
If seismic velocity and elastic rock properties are used, then fluid type classification is more accurate, but the requirement for pre-determined properties increases data acquisition complexity
Solution Approach 1:
The method enables the well log data to serve itself by using compressional and shear sonic slowness measurements directly from the well logs to determine fluid types. The cross-plotting of slowness ratios with other well log parameters allows the data to characterize the reservoir without requiring external seismic velocity data or pre-determined elastic rock properties, making the system self-sufficient.
Solution Approach 2:
The patent changes the parameter set from seismic velocity and elastic properties to sonic slowness parameters. By using compressional slowness (Dt), shear slowness (Ds), and their ratio (Dt/Ds), the method achieves fluid type determination using only well log data, eliminating dependency on pre-determined seismic and elastic properties.
3Reliability
If conventional methods are used, then data processing is faster, but uncertainty in hydrocarbon and water differentiation increases
Solution Approach 1:
The patent applies partial action by using only the necessary subset of available well log data (compressional and shear sonic slowness, bulk density, and resistivity) to achieve reliable fluid type differentiation. Rather than requiring complete seismic and elastic property datasets, the method uses sufficient well log parameters to cross-plot and classify fluids, reducing both uncertainty and processing time.
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 enables accurate fluid type classification in unconventional reservoirs, reducing uncertainty and saving time by integrating sonic slowness and porosity data without requiring pre-determined elastic rock properties or seismic velocity, thus improving reservoir characterization.
Implementation Method 1
calculating a compressional formation slowness and a shear formation slowness
Implementation Method 2
predicting a fluid type at the more than one well locations of the reservoir based on combining the compressional formation slowness and the shear formation slowness
Implementation Method 3
based on applying a petrophysical model to the plurality of measurement logs, determining the plurality of parameters
Implementation Method 4
the core sample measurements comprise: a porosity measurement, and a saturation measurement, and wherein the core sample measurements are based on nuclear magnetic resonance (NMR) of core samples
Data Source
AI summary
Some implementations of the present disclosure provide a method that include: accessing a plurality of measurement logs taken from more than one well locations of a reservoir, wherein the plurality of measurement logs encode, for each well location, a plurality of parameters; based on applying a petrophysical model to the plurality of measurement logs, determining the plurality of parameters; based on a portion of the plurality of parameters, calculating a compressional formation slowness and a shear formation slowness; and predicting a fluid type at the more than one well locations of the reservoir based on combining the compressional formation slowness and the shear formation slowness with at least one of the plurality of parameters.