18058934. METHODS AND SYSTEMS FOR PREDICTING FORMATION THERMAL PROPERTIES simplified abstract (Schlumberger Technology Corporation)
Contents
- 1 METHODS AND SYSTEMS FOR PREDICTING FORMATION THERMAL PROPERTIES
- 1.1 Organization Name
- 1.2 Inventor(s)
- 1.3 METHODS AND SYSTEMS FOR PREDICTING FORMATION THERMAL PROPERTIES - A simplified explanation of the abstract
- 1.4 Simplified Explanation
- 1.5 Potential Applications
- 1.6 Problems Solved
- 1.7 Benefits
- 1.8 Potential Commercial Applications
- 1.9 Possible Prior Art
- 1.10 Original Abstract Submitted
METHODS AND SYSTEMS FOR PREDICTING FORMATION THERMAL PROPERTIES
Organization Name
Schlumberger Technology Corporation
Inventor(s)
Simone Di Santo of Dhahran (SA)
Shouxiang Mark Ma of Dhahran (SA)
Ali Jasim A Al Solial of Dhahran (SA)
METHODS AND SYSTEMS FOR PREDICTING FORMATION THERMAL PROPERTIES - A simplified explanation of the abstract
This abstract first appeared for US patent application 18058934 titled 'METHODS AND SYSTEMS FOR PREDICTING FORMATION THERMAL PROPERTIES
Simplified Explanation
The patent application describes methods and systems for predicting thermal properties of a subsurface rock formation using machine learning techniques.
- Training dataset derived from petrophysical properties and thermal properties of rock samples
- Machine learning model trained to predict thermal properties based on input petrophysical properties
- Model validated and deployed for predicting thermal properties of subsurface rock formations
Potential Applications
The technology can be applied in the oil and gas industry for reservoir characterization and optimization of production strategies. It can also be used in geothermal energy exploration to assess the thermal properties of potential sites.
Problems Solved
This technology addresses the challenge of accurately predicting thermal properties of subsurface rock formations, which is crucial for various industries relying on subsurface data for decision-making.
Benefits
- Improved accuracy in predicting thermal properties - Time and cost savings in data analysis and interpretation - Enhanced decision-making for resource exploration and extraction
Potential Commercial Applications
"Predicting Thermal Properties of Subsurface Rock Formations for Enhanced Resource Exploration and Production"
Possible Prior Art
Prior art may include traditional methods of predicting thermal properties of rock formations, such as laboratory testing and numerical modeling techniques.
Unanswered Questions
How does this technology compare to traditional methods of predicting thermal properties of rock formations?
The article does not provide a direct comparison between this technology and traditional methods such as laboratory testing or numerical modeling.
What are the limitations of using machine learning models for predicting thermal properties of subsurface rock formations?
The article does not discuss any potential limitations or challenges associated with the use of machine learning models in this context.
Original Abstract Submitted
Methods and systems are provided for predicting thermal properties of a subsurface rock formation. A training dataset is derived from petrophysical properties of a plurality of formation rock samples and thermal properties of the plurality of formation rock samples. The training dataset is used to train a machine learning model that predicts label data representing the predefined set of thermal properties given input data representing the predefined set of petrophysical properties of an arbitrary formation rock sample. The machine learning model can be validated and deployed for use in predicting thermal properties of subsurface rock formations.
