View source for 17549015. Machine Learning with Physics-based Models to Predict Multilateral Well Performance simplified abstract (Saudi Arabian Oil Company)

=Machine Learning with Physics-based Models to Predict Multilateral Well Performance=


==Organization Name==

[[:Category:Saudi Arabian Oil Company|Saudi Arabian Oil Company]]


[[Category:Saudi Arabian Oil Company]]

==Inventor(s)==

[[:Category:Ayman Alkhalaf of Dhahran (SA)|Ayman Alkhalaf of Dhahran (SA)]][[Category:Ayman Alkhalaf of Dhahran (SA)]]

[[:Category:Said Almalki of Dhahran (SA)|Said Almalki of Dhahran (SA)]][[Category:Said Almalki of Dhahran (SA)]]

[[:Category:Obiomalotaoso Leonard Isichei of Dhahran (SA)|Obiomalotaoso Leonard Isichei of Dhahran (SA)]][[Category:Obiomalotaoso Leonard Isichei of Dhahran (SA)]]

==Machine Learning with Physics-based Models to Predict Multilateral Well Performance - A simplified explanation of the abstract==


This abstract first appeared for US patent application 17549015 titled 'Machine Learning with Physics-based Models to Predict Multilateral Well Performance

==Simplified Explanation==

The patent application describes a system and method for using machine learning and physics-based models to predict the performance of multilateral wells. Here are the key points:

* The method involves obtaining data related to well completion, inflow control valves, and reservoir attributes of multilateral wells.
* Production scenarios are generated based on this data.
* A physics-based model of the multilateral wells is used to simulate the production scenarios and obtain simulation data.
* A neural network based machine learning model is trained using the simulation data and target parameters associated with the multilateral wells.
* The trained machine learning model can then predict multilateral well production parameters.

Potential applications of this technology:

* Oil and gas industry: This technology can be used to optimize the production of multilateral wells, leading to improved efficiency and increased yields.
* Reservoir management: By accurately predicting well performance, this technology can help in making informed decisions regarding reservoir management and resource allocation.

Problems solved by this technology:

* Uncertainty in well performance: By combining physics-based models with machine learning, this technology provides a more accurate prediction of multilateral well performance, reducing uncertainty and improving decision-making.
* Complex well systems: Multilateral wells have multiple branches and inflow control valves, making their performance difficult to predict. This technology addresses this challenge by incorporating these factors into the prediction model.

Benefits of this technology:

* Improved efficiency: By accurately predicting well performance, operators can optimize production strategies and reduce downtime, leading to improved efficiency.
* Cost savings: Optimized production strategies can help reduce operational costs and maximize the economic value of multilateral wells.
* Enhanced decision-making: The combination of physics-based models and machine learning provides a comprehensive understanding of well performance, enabling better decision-making in reservoir management.


==Original Abstract Submitted==

A system and method for machine learning with physics-based models to predict multilateral well performance are provided. An exemplary method enables obtaining data associated with well completion, data associated with inflow control valves, and reservoir attributes of multilateral wells. Production scenarios are generated based on the data associated with well completion, the data associated with inflow control valves, and the reservoir attributes of the multilateral wells. The production scenarios are input into a physics-based model of the multilateral wells, and simulation data associated with the multilateral wells output from the physics-based model is obtained. A neural network based machine learning model is trained using the simulation data associated with the multilateral wells and target parameters associated with the multilateral wells, wherein the trained machine learning model is configured to predict multilateral well production parameters.



[[Category:E21B41/00]]