Machine Learning Model for Unconventional Horizontal Well Production Optimization
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Solution Overview
Problem
Current methods for forecasting and optimizing production in unconventional horizontal wells are limited in accuracy and do not effectively utilize a wide range of predictor parameters, leading to suboptimal production and economic evaluation.
Innovation Solution
A self-learning system employing machine learning models that incorporate predictor parameters such as well location, geological features, operator information, engineering features, and production data to generate models that anticipate and optimize production by determining physical parameter changes for rigs to implement, thereby enhancing production accuracy and economic evaluation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional forecasting methods are used, then the process is simple and easy to implement, but the prediction accuracy is low
Solution Approach 1:
The patent replaces traditional mechanical forecasting methods with machine learning algorithms that automatically learn patterns from historical data. The system uses computational models instead of manual analysis to predict well production, significantly improving accuracy while the automated nature manages the complexity burden.
Solution Approach 2:
The machine learning model continuously learns and improves from incoming data without requiring manual reconfiguration. The system self-optimizes by automatically adjusting parameters based on new production data, reducing the need for expert intervention despite the underlying complexity.
2Productivity
If a limited set of predictor parameters is used, then the data collection process is simple, but the optimization effectiveness is insufficient
Solution Approach 1:
The system collects multiple types of predictor parameters (geological features, well characteristics, operational data) that serve multiple functions in the analysis. These diverse data inputs collectively contribute to different aspects of production optimization, making the comprehensive data collection worthwhile despite increased complexity.
Solution Approach 2:
The patent divides the complex optimization problem into manageable segments by analyzing different parameter categories separately (geological, operational, production data) before integrating them into a unified model. This segmentation makes the complex data collection and analysis process more tractable.
Data Source
AI summary
Systems and methods for optimizing production of unconventional horizontal wells. A method for optimizing production of a resource from an unconventional horizontal well comprises compiling values for predictor parameters and target parameters for each of a plurality of known wells. The method includes generating a model for anticipating production of the unconventional horizontal well. The method comprises using a production optimizer and the model to determine a physical parameter change for increasing the anticipated production from the unconventional horizontal well. The method includes communicating the physical parameter change to a rig. The method comprises causing the rig to make the physical parameter change to the unconventional horizontal well.