18058934. METHODS AND SYSTEMS FOR PREDICTING FORMATION THERMAL PROPERTIES simplified abstract (Saudi Arabian Oil Company)
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
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
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.
- Training dataset derived from petrophysical and thermal properties of rock samples
- Machine learning model trained to predict thermal properties based on petrophysical properties
- Model can be validated and deployed for predicting thermal properties of subsurface rock formations
Potential Applications
This technology can be applied in the oil and gas industry for predicting thermal properties of subsurface rock formations, which can help in reservoir characterization and production optimization.
Problems Solved
This technology solves the problem of accurately predicting thermal properties of rock formations without the need for extensive and time-consuming laboratory testing.
Benefits
The benefits of this technology include improved efficiency in reservoir management, better decision-making in drilling operations, and overall cost savings in the oil and gas exploration process.
Potential Commercial Applications
One potential commercial application of this technology is in providing consulting services to oil and gas companies for reservoir analysis and production optimization.
Possible Prior Art
Prior art may include traditional methods of predicting thermal properties of rock formations, which often rely on manual measurements and calculations based on limited data samples.
Unanswered Questions
How does this technology compare to traditional methods of predicting thermal properties of rock formations?
This technology offers a more efficient and accurate way of predicting thermal properties compared to traditional methods that rely on manual measurements and calculations.
What are the limitations of this technology in predicting thermal properties of subsurface rock formations?
One limitation of this technology could be the accuracy of the predictions, which may vary depending on the quality and quantity of the input data used for training the machine learning model.
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.
