This formation is proposed by the brand Alliance4Training
Training - Introduction to Data Analytics and Machine Learning Techniques for Geosciences and Reservoir Engineering
This course provides an extensive and practical knowledge for applying data analytics in reservoir modeling and predicting the well performance; the data driven approach is used to understand the main factors affecting the reservoir performance, using the fuzzy logic to rank these parameters and build a predictive model to optimize the reservoir response. Emphasis will be put on the use of supervised and unsupervised neural networks algorithms
Training - Introduction to Data Analytics and Machine Learning Techniques for Geosciences and Reservoir Engineering
For whom ?
Ingénieurs et techniciens du secteur pétrolier (forage, complétion, production) et parapétrolier dont les activités sont en liaison avec l'abandon et bouchage des puits (P&A)
Aucun prérequis n'est nécessaire pour suivre cette formation
Objectives
- Distinguer les différents types de bouchage des puits (temporaire, permanent)
- Reconnaitre les procédures de design et des opérations de bouchage des puits
- Identifier les éléments de barrières et leur validation
- Apprécier le rôle des personnels chargés des opération P&A
Les apprenants seront capables de mettre en œuvre les compétences suivantes :
Program
Pedagogy
Assessment of achievements
More information
Expected skills
- Carry out technical/economic studies
- Master methods
- Master techniques and tools
Training - Introduction to Data Analytics and Machine Learning Techniques for Geosciences and Reservoir Engineering
This course provides an extensive and practical knowledge for applying data analytics in reservoir modeling and predicting the well performance; the data driven approach is used to understand the main factors affecting the reservoir performance, using the fuzzy logic to rank these parameters and build a predictive model to optimize the reservoir response. Emphasis will be put on the use of supervised and unsupervised neural networks algorithmsThis course provides an extensive and practical knowledge for applying data analytics in reservoir modeling and predicting the well performance; the data driven approach is used to understand the main factors affecting the reservoir performance, using the fuzzy logic to rank these parameters and build a predictive model to optimize the reservoir response. Emphasis will be put on the use of supervised and unsupervised neural networks algorithms
| Language: English |
| Modality: Face-to-face only |
To French entities: IFP Training is referenced to DataDock ; you may contact your OPCO about potential funding.
Please contact our disabled persons referent to check the accessibility of this training program: [email protected]