Data-Driven Vs. Traditional Reservoir Numerical Models: A Case Study Comparison of Applicability, Practicality and Performance

Shale gas reservoirs are contributing a major role in overall hydrocarbon production, especially in the United States, and due to the intense development of such reservoirs, it is a must thing to learn the productive methods for modeling production and performance evaluation. Consequently, one of the most adopted techniques these days for the sake of production performance analysis is the utilization of artificial intelligence (AI) and machine learning (ML). Hydrocarbon exploration and production is a continuous process that brings a lot of data from sub-surface as well as from the surface facilities. Availability of such a huge data set that keeps on increasing over time enhances the computational capabilities and performance accuracy through AI and ML applications using a data-driven approach. The ML approach can be utilized through supervised and unsupervised methods in addition to artificial neural networks (ANN). Other ML approaches include random forest (RF), support vector machine (SVM), boosting technique, clustering methods, and artificial network-based architecture, etc. In this paper, a systematic literature review is presented focused on the AI and ML applications for the shale gas production performance evaluation and their modeling.

show abstract

Prediction of Well Production Event Using Machine Learning Algorithms

Alatrach

Mata

Omrani³

et al. 2020

Day 3 Wed, November 11, 2020

View full text Add to dashboard Cite

In this paper, a new approach was identified and tested to detect abnormal events in producing wells when a labeled dataset is unavailable or the number of instances are below 10% and are insufficient for conventional modelling methods. Autoencoders (AE), a type of unsupervised learning, are trained to learn normal behavior by trying to reconstruct the input data that is fed into the model. When run in prediction mode, low reconstruction errors are classified as Normal behavior whilst higher errors are classified as anomalous behavior. Different model structures were tested. An average accuracy of 94% with a precision and recall rate of 70% was achieved using a 6-Layered AE-NN model. The results of the models created show encouraging results and can help detect events and notify engineers when the well is deviates from expected behavior.

show abstract

Data-Driven Vs. Traditional Reservoir Numerical Models: A Case Study Comparison of Applicability, Practicality and Performance

Cited by 4 publications

References 3 publications

References

References

AI/ML assisted shale gas production performance evaluation

Prediction of Well Production Event Using Machine Learning Algorithms

Contact Info

Product

Resources

About