Neural Network Shale Gas Production Forecasting
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Solution Overview
Problem
Current technologies face challenges in accurately predicting the long-term production of shale gas from wells due to uncertainties in well productivity and limitations in characterizing shale gas formations, making it difficult to assess commercial value and plan effective production strategies.
Innovation Solution
A method utilizing a trained neural network system that predicts production values by analyzing cumulative, average, and first/last production values for different intervals, allowing for the forecasting of future production based on historical data and adaptive weight adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional production forecasting methods are used, then the process is simple and easy to implement, but the prediction accuracy is insufficient and cannot reliably forecast long-term production
Solution Approach 1:
The patent replaces traditional mechanical/mathematical forecasting models with a neural network-based intelligent system. The neural network learns complex non-linear relationships between well characteristics and production performance, substituting conventional analytical methods with adaptive computational intelligence to achieve higher prediction accuracy.
Solution Approach 2:
The patent introduces a neural network as an intermediary between input well data and production forecasts. This intermediary layer processes and transforms raw well characteristics into meaningful production predictions, enabling the system to capture complex relationships that direct mathematical models cannot represent.
2Reliability
If more well characteristics and production data are analyzed, then the forecast reliability improves, but the data processing complexity and computational requirements increase
Solution Approach 1:
The patent performs preliminary data processing and feature extraction during the neural network training phase. By pre-processing historical well data and establishing the neural network model in advance, the system reduces the complexity of real-time data processing while maintaining high forecast reliability when making actual predictions.
Solution Approach 2:
The neural network automatically learns and adjusts its internal parameters and relationships from the provided well characteristics and production data without requiring manual intervention. The system self-optimizes by processing the input data through its trained architecture, reducing the need for complex external data processing procedures.
Data Source
AI summary
A method can include providing a trained neural network; providing a set of production values where the set includes, for example, a cumulative production value for an interval, an average production value for the interval, a first production value for the interval and a last production value for the interval; and predicting at least one production value for a subsequent interval based at least in part on the trained neural network and the provided set of production values. Various other apparatuses, systems, methods, etc., are also disclosed.