When engineers and data scientists talk about analytics, deep learning often dominates the conversation. From seismic interpretation to real-time production optimisation, neural networks are seen as a cutting-edge solution which management is pushing for. But do we really need it for every problem? Or can tried-and-tested statistical methods still deliver the insights we need?
The Hype Around Deep Learning
Deep learning is excellent at handling massive, complex datasets, which includes:
- Seismic image analysis for reservoir characterization
- Real-time drilling data streams for anomaly detection
- Production optimization from high-frequency sensors
These models can capture nonlinear relationships that traditional tools might miss. But they come with limitations:
- They need large, clean datasets, which are not always available.
- They require computationally heavy infrastructure that may not be feasible on-site.
- They act as black boxes, making it difficult to explain predictions to engineers or management.
Where Statistics Still Provides More Value
Not every petroleum engineering challenge needs a neural network. In fact, statistics can offer more practical, interpretable solutions:
- Sand control selection
Logistic regression and survival analysis can estimate failure probabilities of gravel packs, screens, and frac packs. This helps engineers pick the most reliable completion design. - Well performance evaluation
Statistical regression on flow rate, pressure, and drawdown still offers clear, quick insights, especially for early-life wells where data is sparse.
Hybrid Approaches: Best of Both Worlds
The most effective strategies often blend the two:
- Use statistics to clean, explore, and interpret the data.
- Apply deep learning when patterns are too complex for simpler models.
- Combine them: for example, use survival analysis to explain sand control reliability, and add machine learning to refine predictions from larger datasets.