Managing Data Lineage of O&G Machine Learning Models: The Sweet Spot for Shale Use Case

References

1. Bishop, C.M.
   [2006] Pattern recognition and machine learning. springer.
   [Google Scholar]
2. Guevara, J., Zadrozny, B., Buoro, A., Lu, L., Tolle, J., Limbeck, J.W., Hohl, D.
   et al. [2019] A Machine-Learning Methodology Using Domain-Knowledge Constraints for Well-Data Integration and Well-Production Prediction. SPE Reservoir Evaluation & Engineering.
   [Google Scholar]
3. Herschel, M., Diestelkämper, R. and Ben Lahmar, H.
   [2017] A survey on provenance: What for? What form? What from?The VLDB Journal – The International Journal on Very Large Data Bases, 26(6), 881–906.
   [Google Scholar]
4. Moreau, L., Missier, P., Belhajjame, K., B’Far, R., Cheney, J., Coppens, S., Cresswell, S., Gil, Y., Groth, P., Klyne, G.
   et al. [2013] Prov-dm: The prov data model. Retrieved July, 30(2013), W3C.
   [Google Scholar]
5. Moreno, M.F., BrandÃčo, R. and Cerqueira, R.
   [2016] Extending Hypermedia Conceptual Models to Support Hyperknowledge Specifications. In: 2016 IEEE International Symposium on Multimedia (ISM). 133–138.
   [Google Scholar]
6. Newman, S.
   [2015] Building microservices: designing fine-grained systems. “O’Reilly Media, Inc.”.
   [Google Scholar]
7. Souza, R., Azevedo, L., Lourenço, V., Soares, E., Thiago, R., Brandão, R., Civitarese, D., Brazil, E., Moreno, M., Valduriez, P.
   et al. [2019a] Provenance Data in the Machine Learning Lifecycle in Computational Science and Engineering. In: 2019 IEEE/ACM Workflows in Support of Large-Scale Science (WORKS). IEEE, 1–10.
   [Google Scholar]
8. Souza, R., Azevedo, L., Thiago, R., Soares, E., Nery, M., Netto, M., Brazil, E.V., Cerqueira, R., Valduriez, P. and Mattoso, M.
   [2019b] Efficient runtime capture of multiworkflow data using provenance.
   [Google Scholar]