Formation Top Picking Using Log Correlation and Depth Shift Optimization
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Solution Overview
Problem
Manual formation top picking in hydrocarbon reservoirs is prone to errors and individual biases due to the reliance on geoscientist inspection and lacks a standardized, automated process for accurately determining the depth at which formation bounding surfaces intersect wells.
Innovation Solution
An automated method using a computer processor to analyze well logs, generate initial depth estimates, form an objective function based on log correlations, and optimize depth shifts to determine the true intersection of formation bounding surfaces with wells, employing techniques like kriging for interpolation and cross-correlation for accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual identification of formation tops is used, then expertise and experience can be applied, but the process is time-consuming and labor-intensive
Solution Approach 1:
The patent replaces the manual mechanical process of formation top identification with an automated computer-based system that uses optimization algorithms and well log data processing. The system automatically identifies formation tops by analyzing electrical conductivity, density, and other well log parameters, eliminating the need for manual examination of well logs while maintaining or improving identification accuracy through systematic computational methods.
2Productivity
If automated methods are used for formation top identification, then time and labor are reduced, but accuracy and reliability may be compromised
Solution Approach 1:
The system incorporates feedback mechanisms where the optimization algorithm iteratively refines formation top identification by comparing predicted formation boundaries with actual well log data. The system uses feedback from multiple well log parameters (electrical conductivity, density, porosity) to continuously adjust and improve identification accuracy, ensuring that automated results match or exceed manual expert analysis quality.
Solution Approach 2:
The patent utilizes multiple well log parameters (electrical conductivity, density, porosity, neutron porosity) and changes the state of these parameters to identify formation tops. By analyzing variations in these physical parameters and their relationships, the system achieves accurate automated identification of formation boundaries without relying on manual interpretation, thereby maintaining high productivity and accuracy simultaneously.
3Measurement precision
If multiple well log parameters are analyzed, then identification accuracy improves, but computational complexity increases
Solution Approach 1:
The patent segments the well log analysis into distinct processing stages: data acquisition from multiple sources (electrical conductivity, density, porosity logs), separate optimization algorithm processing for each parameter type, and final integration of results. This segmentation allows the system to handle multiple complex parameters through modular computational steps, reducing overall system complexity while maintaining the ability to analyze all parameters for improved identification accuracy.
Data Source
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AI summary
A method including obtaining, by a computer processor (705), at least one key log in each of a set of training wells (102) located, at least partially, within a hydrocarbon reservoir (614), identifying a target formation bounding surface (202) in each of the set of training wells, and generating an initial depth surface for the target formation bounding surface (202) from the target formation bounding surface in each of the set of training wells. The method further including, determining from the initial depth surface an initial depth estimate of the target formation bounding surface at a location of a current well (106), forming an objective function based, at least in part on a. correlation between each key log in each of the set of training wells, and each corresponding key log in the current well, and optimizing the objective function by varying a depth shift between each of the set of training wells (102) and the current well (106), to determine an optimum depth shift that produces an extremum of the objective function. The method still further including combining the initial depth estimate of the target formation bounding surface at the location of the current well with the optimum depth shift to produce a final depth estimate of the target formation bounding surface (202) at the location of the current well (106).