Looking Ahead of the Bit Using Surface Drilling and Petrophysical Data: Machine-Learning-Based Real-Time Geosteering in Volve Field
- Ishank Gupta (University of Oklahoma) | Ngoc Tran (University of Oklahoma) | Deepak Devegowda (University of Oklahoma) | Vikram Jayaram (Pioneer Natural Resources) | Chandra Rai (University of Oklahoma) | Carl Sondergeld (University of Oklahoma) | Hamidreza Karami (University of Oklahoma)
- Document ID
- Society of Petroleum Engineers
- SPE Journal
- Publication Date
- April 2020
- Document Type
- Journal Paper
- 990 - 1,006
- 2020.Society of Petroleum Engineers
- random forest, gradient boosting, neural networks, machine learning on drilling data, real-time geosteering
- 30 in the last 30 days
- 262 since 2007
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Petroleum reservoirs are often associated with multiple target zones or a single zone adjacent to nonproductive intervals. Real-time geosteering therefore becomes important to remain in zone or to dynamically steer toward a target. This requires knowledge of the petrophysical/rock mechanical properties of the rock surrounding the bit. Although logging while drilling can provide this information, a cost-effective and almost-real-time solution is lacking. In general, there is a depth lag, and therefore, a time delay, between what the logging-while-drilling sub relays to the surface and the bit performance. This study focuses on relating drill-bit- and drillstring-performance data in a machine-learning (ML) workflow to predict the lithology at the bit while drilling. The method we are proposing offers several advantages in terms of cost and time savings for real-time geosteering applications, where going out of zone requires costly intervention.
In this study, we have used a public data set from Volve Field on the Norwegian continental shelf. Within our proposed workflow, as a first step, logs sensitive to lithology [such as density, gamma ray (GR), and sonic] are grouped into three electrofacies. We also had access to core data, which helped us interpret the electrofacies in terms of mineralogy. The three electrofacies corresponded to quartz-rich (sandstone/siltstone), clay-rich (shale), and carbonate-rich (limestone) lithologies.
The next step is to predict the electrofacies using various measurement-while-drilling (MWD) variables, such as rate of penetration (ROP), weight on bit (WOB), and several others that are monitored in real time. Supervised classification algorithms were used to relate real-time surface measurements to lithology. The algorithms were able to predict lithology in test wells with more than 80% accuracy. These results, although encouraging, constitute a small step toward drilling-automation/advisory systems. The development of such systems can prevent costly out-of-zone drilling and minimize rig time and equipment use, thereby potentially reducing capital expenditures. This study was specifically performed in Volve Field in the North Sea using petrophysical and surface drilling data from vertical wells. However, the workflow has a potential to be extended to other formations in other fields in different well types.
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