Sub-erosion Rate Estimation Model for Gas Wells
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Solution Overview
Problem
Current methods for estimating erosion and sub-erosion rates in gas wells, such as those based on API 14E standards, are conservative and not suited for high-pressure, high-temperature wells with specialty alloys, often underestimating sub-erosion rates and requiring extensive development time.
Innovation Solution
A computer-implemented method using historical and forecasted field life data to generate a sub-erosion production rates model, which identifies high-erosion risk spots by evaluating well parameters with three different methods and providing graphical outputs, allowing for more detailed and robust analysis of well architecture and material loss.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional API 14E standards and single-step approaches are used to estimate sub-erosion rates, then the methodology is simple and widely applicable, but the sub-erosion rates are underestimated and not accurate for high-pressure, high-temperature wells with specialty alloys
Solution Approach 1:
The well architecture is divided into multiple discrete sections or nodes along its length, allowing erosion analysis to be performed at each specific location rather than as a single average value. This segmentation enables identification of high-risk spots and provides location-specific sub-erosion rate estimates, improving accuracy for complex well configurations with varying pressure, temperature, and flow conditions at different depths
Solution Approach 2:
The system performs iterative evaluation of erosion risks by dynamically adjusting and comparing results across multiple analysis methods (at least three different methodologies) until convergence is achieved. This dynamic approach allows the model to adapt to the specific characteristics of high-pressure, high-temperature wells with specialty alloys, producing more accurate and reliable sub-erosion rate estimates than static single-step methods
2Reliability
If detailed evaluation of entire well architecture is performed to identify high-erosion risk spots, then measurement precision and reliability are improved, but development time increases significantly
Solution Approach 1:
The system performs preliminary automated identification of high-risk erosion spots by analyzing well architecture parameters, pressure-temperature profiles, and flow conditions before conducting detailed erosion calculations. This preliminary screening focuses computational resources on critical sections of the well, enabling reliable erosion risk assessment without requiring exhaustive analysis of every well section, thus reducing overall development time
Solution Approach 2:
The system implements iterative feedback loops where initial erosion risk assessments inform subsequent analysis iterations. Results from preliminary evaluations feed back into refined calculations, allowing the model to converge on accurate sub-erosion rates more efficiently. This feedback mechanism maintains high reliability by continuously refining results while avoiding unnecessary computational steps in low-risk areas, balancing accuracy with development time
Data Source
AI summary
Systems and methods include a computer-implemented method: Historical and forecasted field life data for existing gas wells is received, including pressure and temperature data at different depths of the existing gas wells over the life of the existing gas wells. Using the historical and forecasted field life data for the existing gas wells, a sub-erosion production rates model is generated for in situ estimation of sub-erosion for gas wells. Well parameters for a subject gas well are received. Using the well parameters of the subject gas well, the sub-erosion production rates model is executed to identify high-erosion risk spots in the subject gas well, including using iteratively evaluating results using three different methods. Outputs of the sub-erosion production rates model are provided for presentation to a user in a graphical user interface (GUI).