Thin Permeable Layers Indicator in Geological Formations
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Solution Overview
Problem
Current methods struggle to accurately identify permeable thin layers in tight massive and shaly clastic formations, which are crucial for enhancing well performance and hydrocarbon reservoir productivity, as conventional log analysis often fails to distinguish between productive and non-productive layers due to geological complexity.
Innovation Solution
A petrophysical workflow combining standard logging data, neutron spectroscopy, advanced mud logging, advanced statistic and deterministic petrophysical analysis, nuclear magnetic resonance, and image log interpretations is used to analyze and integrate data from multiple sources, providing calibrated values for porosity, water saturation, intrinsic permeability, and mineral volumes, and identifying productive reservoir layers through a multimineral, NMR, shaly sand, resistivity image log, and neutron spectroscopy models.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional log analysis is used to identify permeable layers, then the analysis process is simple and quick, but the accuracy of distinguishing productive and non-productive layers is poor due to geological complexity
Solution Approach 1:
The patent combines multiple independent data sources including standard logging data, neutron spectroscopy, advanced mud logging, nuclear magnetic resonance, and image log interpretations into a unified petrophysical workflow. This merging of multiple measurement and analysis approaches enables more accurate identification of productive layers by cross-validating results across different methodologies, thereby resolving the contradiction between measurement precision and device complexity.
Solution Approach 2:
The patent segments the petrophysical analysis into five distinct model methodologies, each focusing on specific aspects such as multimineral petrophysical evaluation, NMR bound fluid analysis, shaly sand analysis, resistivity image log analysis, and neutron spectroscopy. This segmentation allows each component to be optimized independently while contributing to the overall accurate identification of productive layers, balancing complexity with precision.
2Reliability
If multiple data sources are combined to improve identification accuracy, then the reliability of productive layer identification improves, but the time required for analysis increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing and parameterizing input data from multiple sources before integrating them into the five model methodologies. This preliminary preparation includes calibrating data, establishing baseline parameters, and organizing data structures that facilitate efficient processing during the main analysis phase, thereby reducing overall analysis time while maintaining high reliability through comprehensive data integration.
3Ease of operation
If porosity measurements are used as the primary indicator for identifying permeable layers, then the identification process is straightforward, but errors in porosity measurements significantly impact the results
Solution Approach 1:
The patent introduces intermediary parameters and calibration processes that mediate between raw porosity measurements and the final identification of productive layers. By using multiple intermediary analyses (such as NMR bound fluid analysis, shaly sand analysis, and resistivity image log analysis) that cross-validate porosity data, the system reduces the impact of measurement errors while maintaining ease of operation through automated integration of multiple datasets.
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 improves the success rate of well placement, optimizes well placement in effective reservoirs, reduces well costs, enhances hydrocarbon production rates, and identifies high-success probability areas for drilling, allowing for real-time evaluation and decision-making, while being porosity-independent and reducing the impact of errors in porosity measurements.
Implementation Method 1
A nuclear magnetic resonance (NMR) model is executed using at least outputs of the MM petrophysical evaluation model, the executing resulting in generating porosity arrays from multiple Time 1 (T1) and Time 2 (T2) spectrums estimated using multiple bound fluid cutoffs
Implementation Method 2
A neutron spectroscopy for rock quality indices model is executed, the executing resulting in determining reservoir quality from ratios of measured elements determined from spectrometry
Data Source
AI summary
Systems and methods include a computer-implemented method for identifying productive reservoir layers. Model input data from an exploration and producing (E&P) database is parameterized by identifying numerical relationships between qualitative model input data and dynamic model qualification data. Execution results from executing five model methodologies are combined using the parameterized model input data: a multimineral (MM) petrophysical evaluation model, a nuclear magnetic resonance (NMR) model, a shaly sand analysis model, a resistivity image log analysis model, and a neutron spectroscopy for rock quality indices model. Productive reservoir layers are identified by integrating outputs of the five model methodologies. Geosteering, while drilling in layers, is restricted to the identified productive reservoir layers.