Oil Production Analytics Using Virtual Bottomhole Pressure Data
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Solution Overview
Problem
The challenge in oil and gas production systems is the difficulty in ascertaining meaningful analytics from large datasets due to missing or inaccurate sensor data, particularly for bottomhole pressure, which hinders real-time performance assessment and optimization decisions.
Innovation Solution
A machine learning model is trained using historical data to generate estimated sensor data, including bottomhole pressure, enabling the calculation of productivity indices even when permanent downhole pressure gauges are unavailable.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If sensors are installed to provide accurate bottomhole pressure data, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent introduces machine learning models as intermediary systems that estimate bottomhole pressure using surface-measured parameters (flow rate, wellhead pressure, temperature) instead of requiring direct downhole pressure sensors. The ML model acts as a mediator between available surface data and the needed downhole pressure information, eliminating the need for complex downhole sensor installations while maintaining measurement accuracy within acceptable tolerances.
Solution Approach 2:
The patent creates virtual copies of physical sensor measurements through machine learning models. Instead of installing physical pressure sensors downhole, the system generates synthetic pressure data by training ML models on historical production data and surface measurements. These model-generated pressure values replicate the function of physical sensors without the installation complexity and cost of downhole equipment.
2Measurement precision
If more sensors are deployed to improve data accuracy, then measurement precision is improved, but loss of information from missing sensor data increases
Solution Approach 1:
The machine learning model performs self-service by automatically imputing missing sensor data using patterns learned from historical production data. The model continuously refines its estimates by comparing predicted values with actual measurements when available, enabling the system to operate effectively even when sensors fail or data is missing, thus preventing information loss without requiring redundant sensor deployments.
3Productivity
If real-time data processing is implemented to enable real-time analytics, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent applies preliminary action by pre-training machine learning models on historical production data before real-time analytics are needed. The models are trained offline using past well test data and production records, capturing the relationships between surface measurements and downhole conditions. During real-time operation, the pre-trained models simply infer current conditions from new surface measurements, enabling real-time analytics without complex real-time computational systems.
Data Source
AI summary
Implementations claimed and described herein provide systems and methods for analyzing natural resource production. The systems and methods use a machine learning model to generate estimated sensor data associated with input data. The machine learning model is built from historical data.