AI Hydrogen Sulfide Prediction from Mudlogging Data
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Solution Overview
Problem
Current methods lack real-time monitoring and determination of hydrogen sulfide concentration downhole during well drilling, posing health and equipment risks due to its corrosive nature and hazardous effects.
Innovation Solution
A system utilizing mudlogging data and artificial intelligence to predict hydrogen sulfide concentration, which includes a gas extractor, shale shaker, and chromatography instrument, processing data with an AI model to adjust drilling operations and plan well completions and production.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional mudlogging methods are used to monitor gas composition, then basic gas detection is available, but real-time hydrogen sulfide concentration determination and prediction are not achieved
Solution Approach 1:
The patent replaces traditional mechanical/chemical analysis methods with an artificial intelligence model that processes mudlogging data to predict hydrogen sulfide concentration. The AI system analyzes patterns in existing mudlogging data (gas chromatography results, drilling parameters, geological information) to determine H2S levels in real-time, eliminating the need for direct downhole H2S measurement equipment.
Solution Approach 2:
The patent uses an intermediary AI model that acts as a mediator between available mudlogging data and hydrogen sulfide concentration determination. The AI model processes multiple input parameters (gas composition, drilling rate, geological data) and translates them into predictive H2S concentration information, enabling real-time monitoring without direct measurement.
2Measurement precision
If downhole hydrogen sulfide sensors are deployed, then direct measurement capability is improved, but equipment complexity and cost increase
Solution Approach 1:
The patent extracts the hydrogen sulfide prediction capability from complex downhole measurement equipment and relocates it to surface-based AI processing. By removing the need for specialized downhole H2S sensors, the system achieves H2S determination using existing mudlogging infrastructure combined with artificial intelligence analysis.
Solution Approach 2:
The patent creates a virtual model of hydrogen sulfide concentration through AI prediction based on correlated parameters from mudlogging data. Instead of physically measuring H2S downhole, the system creates an accurate digital representation of H2S levels by analyzing patterns in available data, effectively copying the measurement function through computation.
3Object-affected harmful factors
If real-time hydrogen sulfide monitoring is implemented, then health and safety risks are reduced, but data processing complexity increases
Solution Approach 1:
The patent implements a self-service system where the AI model automatically processes mudlogging data, identifies patterns indicating hydrogen sulfide presence, and generates real-time concentration predictions without requiring complex external analysis systems. The system serves itself by utilizing existing data infrastructure and computational resources.
Solution Approach 2:
The patent establishes a feedback loop where the AI model continuously processes incoming mudlogging data, compares predictions with actual conditions, and adjusts its analysis to improve accuracy. This feedback mechanism enables real-time monitoring and early warning capabilities while managing processing complexity through iterative optimization.
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
Enables real-time monitoring and adjustment of drilling operations to mitigate health and equipment risks, optimizing wellbore plans and production strategies based on predicted hydrogen sulfide levels.
Implementation Method 1
a gas extractor that separates gas from liquid and solid components of the received mud
Implementation Method 2
a shale shaker that removes the mud cuttings from the received mud
Implementation Method 3
a gas chromatography instrument that determines a chromatogram of the gas separated by the gas extractor
Data Source
AI summary
A method for determining the real-time concentration of hydrogen sulfide from mudlogging data using artificial intelligence, during a drilling operation, and adjusting drilling operations accordingly. The method includes obtaining mudlogging data while conducting a drilling operation and processing the mudlogging data with an artificial intelligence (AI) model to determine a predicted quantity of hydrogen sulfide. The method further includes determining a drilling operation condition based on the predicted quantity of hydrogen sulfide and adjusting, based on the determined drilling operation condition, the drilling operation. The method further includes determining a completions plan and a production plan for operating a well based on the predicted quantity of hydrogen sulfide.