Wellbore Data Quality Scoring Methodology
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current formation testing methods for determining the quality of data in wellbores are subjective and limited, often resulting in inaccurate measurements due to factors like mud flow noise and supercharging, which can lead to significant financial and operational costs if incorrect data is used for decision-making.
Innovation Solution
A scoring methodology is introduced to objectively quantify formation test quality by evaluating parameters such as drawdown mobility, buildup stability, standard deviation of pressure stability, and supercharge potential, allowing for stratification of quality measurements and user-specific threshold inputs to enhance data accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If formation testing is performed to obtain wellbore measurements, then data quality is improved, but subjectivity in quality assessment leads to measurement accuracy deterioration
Solution Approach 1:
The patent replaces the subjective human judgment mechanism with an automated computational scoring system. The system uses a standardized formula that objectively calculates quality scores based on multiple measurable parameters (buildup stability, drawdown mobility, supercharge, radius of investigation), eliminating the variability and subjectivity inherent in manual quality assessment while maintaining measurement precision.
2Reliability
If multiple quality parameters are evaluated to improve assessment comprehensiveness, then measurement reliability is improved, but system complexity increases
Solution Approach 1:
The patent creates a universal quality scoring system that simultaneously evaluates multiple parameters (buildup stability, drawdown mobility, supercharge, radius of investigation) through a single standardized formula. This multi-functional approach allows comprehensive reliability assessment without proportionally increasing system complexity, as the same computational framework handles all parameter integrations.
Solution Approach 2:
The patent transforms multiple complex quality parameters into a single standardized quality score through mathematical transformation. By changing the representation from multiple independent parameters to a unified score, the system maintains comprehensive evaluation capability while simplifying the overall assessment structure and reducing operational complexity.
3Reliability
If automated quality scoring is implemented to reduce subjectivity, then measurement accuracy is improved, but computational requirements and processing time increase
Solution Approach 1:
The patent replaces time-consuming manual quality assessment with automated computational scoring. The standardized formula can be rapidly calculated using basic arithmetic operations on the four key parameters, significantly reducing assessment time while improving reliability through consistent, objective application of the scoring criteria across all measurements.
Data Source
AI summary
Methods for determining the quality of data gathered in a wellbore in a subterranean formation including (a) collecting a formation fluid sample in the wellbore in the subterranean formation using a formation tester for receiving the formation fluid, wherein the formation tester is lowered to at least one depth in the wellbore in the subterranean formation by a conveyor; (b) acquiring a wellbore measurement (“WM”) from the least one depth with the formation tester; (c) determining from the WM a measured quality value (“MQV”); (d) assigning a threshold value (“TV”) to the MQV; (e) assigning a range value (“RV”) to the MQV, based on geometric scaling of the TV, the RV defining the limits of the MQV above and below the TV; and (f) calculating a score value (“SV”) based on the MQV, the TV, and the RV, wherein the SV is a number between 0 and 2*TV, and wherein the quality of the WM increases as the SV increases.