Invention Application
- Patent Title: PREDICTING WELL PERFORMANCE FROM UNCONVENTIONAL RESERVOIRS WITH THE IMPROVED MACHINE LEARNING METHOD FOR A SMALL TRAINING DATA SET BY INCORPORATING A SIMPLE PHYSICS CONSTRAIN
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Application No.: US18325777Application Date: 2023-05-30
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Publication No.: US20240403775A1Publication Date: 2024-12-05
- Inventor: Jilin Zhang , Hui-Hai Liu , Feng Liang , Moemen Abdelrahman
- Applicant: ARAMCO SERVICES COMPANY
- Applicant Address: US TX Houston
- Assignee: ARAMCO SERVICES COMPANY
- Current Assignee: ARAMCO SERVICES COMPANY
- Current Assignee Address: US TX Houston
- Main IPC: G06Q10/0637
- IPC: G06Q10/0637 ; G06Q50/02

Abstract:
A method and a system for predicting well production of a reservoir using machine learning models and algorithms is disclosed. The method includes obtaining a training data set for training a machine learning (ML) model and selecting an artificial neural network model structure, the model structure including a number of layers and a number of nodes of each layer. Further, the method includes generating a plurality of individually trained ML models and calculating a model performance of each trained model by evaluating a difference between a model prediction and a well performance data. The plurality of top-ranked individually trained ML models is constrained using one or multiple known physical rules. A plurality of individual predicted well production data is generated using the geological, the completion, and the petrophysical data of interest and a final predicted well production data is generating based on the plurality of individual predicted well production data.
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