Statistical Machine Learning for Intelligent Operations
- Mohammed AlGhazal (Saudi Aramco) | Viranchi Vedpathak (Stratagraph)
- Document ID
- Society of Petroleum Engineers
- SPE Middle East Oil and Gas Show and Conference, 18-21 March, Manama, Bahrain
- Publication Date
- Document Type
- Conference Paper
- 2019. Society of Petroleum Engineers
- 6 Health, Safety, Security, Environment and Social Responsibility, 6.1.5 Human Resources, Competence and Training, 7.6.4 Data Mining, 7.6 Information Management and Systems, 6.1 HSSE & Social Responsibility Management, 7.6.6 Artificial Intelligence, 6.3 Safety, 7 Management and Information
- operations, Machine learning
- 53 in the last 30 days
- 56 since 2007
- Show more detail
- View rights & permissions
|SPE Member Price:||USD 8.50|
|SPE Non-Member Price:||USD 25.00|
Digitalization in its various shapes of artificial intelligence, machine learning or big data analytics is slated to add unprecedented efficiencies that will transform the oil and gas landscape. The role of data-based machine learning and artificial intelligence techniques continues to grow to streamline error-prone, labor-intensive, repetitive tasks.
The data-based machine learning and artificial intellence models are only as good as the training data used to train and validate these models. Accordinly, data aquistion and readiness for analysis is a critical part of the digitalization journey. This paper discusses the application of the statistical method of principle component analysis (PCA) to systematically assure data quality and representation to boost the design and function of machine learning and artificial intelligence models to generate the levels of operational intelligence required.
The analysis work performed shows that PCA is a useful quantative technique to controllably extract representative data that feeds machine learning models and systems that drive automation. Additionally, the paper highights the application of PCA to condense data in favor of the data intreperter or big data machine learning and aritifical intelligence engineer; it is evident that PCA can increase the correlation with the variables to unveil hidden trends.
Moreover, while digital intiatives, such as automation, augment safety practices, unintended different classes of safety breaches may be encountered. Accordingly, the paper highlights the importance of digitalizing safely using embedded protection controls.
This work shows the success of the combination of legacy-based data and intelligence extracted using digital analytics to transcend boundaries towards intelligent decision-making framework.
|File Size||1 MB||Number of Pages||9|
Aljubran, M., Al-Ghazal, M., & Vedpathak, V. "Integrated Cybersecurity for Modern Information Control Models in Oil and Gas Operations," SPE 190582-MS presented at the SPE International Conference on Health, Safety, Security, Environment, and Social Responsibility, 16-18 April 2018, Abu Dhabi, United Arab Emirates.
Salehi, S. September 2018. Cognitive Study of Human Factors for Safe Drilling Operations: Eye-Tracking Technology. Society of Petroleum Engineers. www.spe.org.
Wallace, S. P., Hegde, C. M., & Gray, K. E. "A System for Real-Time Drilling Performance Optimization and Automation Based on Statistical Learning Methods," SPE 176804-MS presented at the SPE Middle East Intelligent Oil & Gas Conference and Exhibition, 15-16 September 2015, Abu Dhabi, United Arab Emirates.