Water Encroachment and Pay Zone Detection from Baseline Resistivity Logs
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Solution Overview
Problem
The mixing of fresher water with saline connate water during waterflooding recovery methods complicates the identification of water encroachment zones and pay zones in hydrocarbon reservoirs, as it affects the interpretation of resistivity logs, making it difficult to design effective completion and recovery operations.
Innovation Solution
A method involving rock core data, well logs, machine learning models, saturation-height function models, and Archie-type models is used to predict initial resistivity logs, allowing for the identification of water encroachment and pay zones by comparing these logs with current resistivity logs, thereby informing completion plans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If waterflooding recovery method is used to inject fresher water into the reservoir, then hydrocarbon recovery is improved, but the ability to identify water encroachment zones and pay zones using resistivity logs deteriorates due to mixing with saline connate water
Solution Approach 1:
The method performs preliminary classification of rock types and determination of saturation-height functions before waterflooding operations. By establishing the initial saturation log and predicted permeability log prior to water injection, the system creates a baseline that can be compared against current conditions, enabling accurate identification of water encroachment zones even as resistivity measurements change during recovery operations
Solution Approach 2:
The invention transforms the interpretation approach by changing from direct resistivity log analysis to a multi-parameter comparison method. It uses predicted permeability, initial saturation, rock type classification, and the ratio of current to initial resistivity as composite parameters to identify water encroachment zones, thereby overcoming the limitations of resistivity log interpretation in mixed water conditions
2Ease of operation
If traditional resistivity log interpretation is used during waterflooding, then the process is simple, but the identification of water encroachment zones and pay zones becomes unreliable
Solution Approach 1:
The method segments the reservoir into distinct rock types based on core data and well logs before performing saturation-height function analysis. This segmentation allows for rock-type-specific interpretation parameters, improving reliability while maintaining operational feasibility through automated classification algorithms that divide the complex reservoir into manageable segments
Solution Approach 2:
The invention introduces an intermediary comparison ratio (current resistivity log divided by initial resistivity log) that mediates between the simple measurement process and the complex interpretation requirement. This ratio, combined with predicted permeability and saturation-height functions, serves as an intermediary parameter that translates simple log measurements into reliable zone identification without requiring complex real-time analysis
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 identification of water encroachment and pay zones without requiring advanced logs, thus improving the effectiveness of completion and recovery operations by providing a reliable basis for designing well completion strategies.
Implementation Method 1
predicting, using an Archie-type model, an initial resistivity log based, at least in part, on the initial saturation log
Data Source
AI summary
Methods and systems are disclosed. The methods may include obtaining rock core data from a formation, obtaining well logs, which include a current resistivity log, from a well within the formation, and inputting the well logs into a trained machine learning (ML) model. The method further includes producing a predicted permeability log from the trained ML model, determining a rock type log based on the rock core data, and determining an initial saturation log based on the rock type log. The method still further includes predicting, using an Archie-type model, an initial resistivity log based on the initial saturation log, identifying at least one of a water encroachment zone and a pay zone along the well by comparing the initial resistivity log and the current resistivity log, and designing a completion plan for the well based on the at least one of the water encroachment zone and the pay zone.