In-Situ Gas Composition Control for High-H2S Natural Gas Production
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Solution Overview
Problem
Existing natural gas production from formations with high hydrogen sulfide (H2S) concentrations is challenging due to high capital and operating costs, environmental risks, and the need for costly anti-corrosive measures, which are exacerbated by simulation model-based approaches that lack real-time accuracy.
Innovation Solution
A system using real-time pressure, volume, and temperature (PVT) data from downhole samples and a machine learning model to predict gas injection volumes, enabling in-situ alteration of gas composition and reducing H2S production, thereby enhancing CO2 sequestration and improving gas quality.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If gas is produced from formations with high H2S concentrations, then natural gas can be extracted, but high capital and operating costs are incurred due to anti-corrosive facilities and H2S management
Solution Approach 1:
The patent applies this principle by injecting CO2 into the formation to react with H2S and convert it into elemental sulfur and water, transforming the harmful H2S into beneficial products. This eliminates the need for expensive anti-corrosive facilities and H2S management infrastructure while maintaining gas production.
Solution Approach 2:
The patent changes the chemical composition parameters of the formation by injecting CO2, which alters the H2S concentration and transforms it into other substances. This parameter change reduces H2S-related costs and complexity of infrastructure.
2Reliability
If simulation model-based approaches are used to predict injection volumes, then gas composition can be altered, but real-time accuracy is insufficient leading to suboptimal injection parameters
Solution Approach 1:
The patent implements a feedback mechanism by continuously monitoring downhole conditions (pressure, temperature, composition) and using this real-time data to adjust CO2 injection parameters. This closed-loop control system improves both the reliability of gas composition alteration and the precision of injection volume prediction.
Solution Approach 2:
The patent replaces simulation model-based prediction with a machine learning model that processes real-time sensor data. This substitution provides more accurate and responsive injection volume predictions, improving measurement precision.
3Device complexity
If H2S concentrations are reduced through injection, then capital investment and operating costs decrease, but real-time monitoring and control are required to achieve desired gas composition
Solution Approach 1:
The patent employs a self-regulating system where downhole sensors automatically monitor composition and trigger CO2 injection when H2S levels exceed thresholds. The machine learning model autonomously adjusts injection parameters based on real-time data, reducing the need for manual intervention and expensive infrastructure.
Solution Approach 2:
The real-time monitoring system provides continuous feedback on formation conditions, enabling automated adjustment of injection parameters to maintain desired gas composition while minimizing infrastructure requirements.
4Manufacturing precision
If CO2 is injected into formations, then H2S concentrations are reduced and gas quality improves, but the volume and timing of injection must be precisely controlled to avoid subsurface pressure issues
Solution Approach 1:
The patent uses dynamic control where CO2 injection rate and timing are continuously adjusted based on real-time downhole pressure and composition data. The machine learning model predicts optimal injection parameters that maintain subsurface pressure within safe limits while achieving precise gas composition control.
Solution Approach 2:
Real-time monitoring of subsurface pressure and composition provides feedback that enables precise control of injection volume and timing, preventing pressure issues while achieving desired gas quality.
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 reduces capital investment, minimizes H2S-related risks, and enhances gas production efficiency by monitoring subsurface compositional changes in real-time, reducing the need for surface H2S management and lowering operational costs.
Implementation Method 1
A system using real-time pressure, volume, and temperature (PVT) data from downhole samples and a machine learning model to predict gas injection volumes
Implementation Method 2
altering gas composition in-situ during production
Implementation Method 3
injecting gas (e.g., methane or CO2) into a formation to adjust the composition of gas produced from the formation
Implementation Method 4
reduce production of H2S or other undesired components
Data Source
AI summary
Methods and systems for producing gas from a subsurface formation through a production well and/or sequestering gas in the subsurface formation through an injection well can include monitoring pressure, temperature, and composition of fluids in the subsurface formation using sensors installed downhole in an injection well. The pressure, the temperature, and the composition of fluids in the subsurface formation can be monitored using sensors installed downhole in the production well(s) and/or observation well(s). This approach can predict a volume of gas injection through required to alter the composition of fluids in the subsurface formation to provide a specific fluid composition in the production well. It can also include injecting the predicted volume of gas through the injection well using pumps associated with the injection well as well as, in some cases, producing the fluids in the subsurface formation to surface.