Reservoir Resistivity Data Quality Assessment
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Solution Overview
Problem
Current interpretation methods in oil and gas exploration lack the capability to assess the data quality of calculated reservoir-rock resistivity, which is crucial for accurate water/hydrocarbon assessment in formation reservoirs, especially in low-resistivity reservoirs.
Innovation Solution
The proposed solution involves systems and methods for evaluating the data quality of calculated reservoir-rock resistivity using integrated interpretation of multi-component induction (MCI), array compensated true resistivity (ACRt), and macro-resistivity imager logs, with new approaches for calculating true resistivity even when only conventional array induction is available.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If conventional array induction logging (ACRt) is used for reservoir resistivity measurement, then the logging tool can operate in complex well conditions, but the data quality of calculated reservoir-rock parameters cannot be assessed
Solution Approach 1:
The patent implements a feedback mechanism by calculating multiple independent estimates of reservoir-rock resistivity (Rsd1, Rsd2, Rsd3) from different log measurements and comparing them. The standard deviation of these estimates serves as a quality indicator that feeds back to assess data reliability, allowing users to identify regions where measurements may be unreliable
Solution Approach 2:
The patent introduces an intermediary quality indicator (standard deviation of Rsd estimates) that mediates between the raw log measurements and the final reservoir resistivity interpretation. This intermediary provides a quantitative measure of data quality without requiring additional logging tools
2Measurement precision
If multiple log types (MCI, ACRt, imager) are integrated for resistivity calculation, then the accuracy of reservoir-rock parameter determination is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent creates a universal interpretation framework that can process multiple log types (MCI, ACRt, imager logs) through a single integrated methodology. The same mathematical framework and quality assessment approach work across different log combinations, reducing the need for separate processing pipelines for each log type
Solution Approach 2:
The patent segments the resistivity calculation into distinct independent estimates (Rsd1 from MCI, Rsd2 from ACRt, Rsd3 from imager) that can be calculated separately and then combined. This segmentation allows each estimate to be processed independently using appropriate algorithms for that specific log type, simplifying the overall processing
3Ease of manufacture
If only conventional array induction (ACRt) is available, then the logging operation is simpler, but new approaches are required to calculate true resistivity accurately
Solution Approach 1:
The patent applies preliminary dip correction to the ACRt measurements before calculating reservoir resistivity. By correcting for formation dip effects in advance, the method enables accurate true resistivity calculation from conventional logs alone, without requiring additional complex measurements
Solution Approach 2:
The patent transforms the ACRt apparent resistivity measurements into true resistivity by applying mathematical transformations that account for dip angle and formation geometry. This parameter change converts operationally simple ACRt data into geologically accurate true resistivity values
Data Source
AI summary
Disclosed herein are embodiments of systems, methods, and computer program products for assessing the data-quality of the calculated reservoir-rock resistivity Rsd in laminar formations. For instance, in one embodiment, a computer-implemented method for assessing the data-quality of the calculated reservoir-rock resistivity Rsd in laminar formations comprises the steps of receiving multi-component induction (MCI) data and other sensor logs data associated with a laminar formation; determining a set of calculated reservoir-rock resistivity Rsd using at least one of the multi-component induction (MCI) data and other sensor logs data; and performing data quality assessments of the set of calculated reservoir-rock resistivity Rsd.