DISTINGUISH: Decision support using neural networks to predict geological uncertainties when geosteering

The ability to fine-tune well positioning in real-time through geosteering increases the potential value of wells and minimizes risks. Geosteering is essential for petroleum and penetrates other types of drilling, including geothermal projects, civil wells and tunnels, and soon CO2 storage wells. Currently, geosteering decisions rely on fast-paced manual interpretation of real-time data, which needs to consider pre-job modeling, geological setting, and embedded uncertainties. “Given the pace of drilling, the person in charge of geosteering is hard-pressed just to interpret the incoming data, much less contemplating the uncertainty in it” (Rassenfoss, 2022, SPE JPT). Following the newest trends in the industry, the geosteering workflow of the future shall capture and update uncertainty in an ensemble of geomodels. The most advanced ensemble methods are the core of closed-loop reservoir management (EnCLRM), the new standard for optimizing the development of petroleum fields. However, EnCLRM is not fast enough for real-time operations due to the complexity involved in geomodelling. We plan to remove this complexity by creating new Generative-Network (GN) geomodels. GN-geomodels “learn geology” before the operation and have sub-second performance. They unlock next-generation data assimilation and new predictive decision-support AI. In 2024, we presented an initial version of a combined workflow for real-time data assimilation and decision-making during drilling with GN-geomodels. This workflow integrates GN-geomodels for geological parameterization, ensemble methods for model updates, and global optimization for decision support. By stepwise reducing uncertainty with real-time data, it enhances predictive models of geology ahead of drilling, leading to more accurate steering decisions. The DISTINGUISH workflow redefines geosteering by shifting focus from 'depth of detection' to 'distance of prediction,' which informs the probabilistic decision support. We plan early testing on field data and with future technology users to accelerate adoption. DISTINGUISH addresses research challenges related to well placement from an Oil-&-Gas-21 machine-learning report. According to the report, better well placement may lead to additional discoveries on the Norwegian Continental Shelf, an increased net present value from drilling, and significant CO2 emission reductions.

The ability to fine-tune well positioning in real-time through geosteering increases the potential value of wells and minimizes risks. Geosteering is essential for petroleum and penetrates other types of drilling, including geothermal projects, civil wells and tunnels, and soon CO2 storage wells. Currently, geosteering decisions rely on fast-paced manual interpretation of real-time data, which needs to consider pre-job modeling, geological setting, and embedded uncertainties. “Given the pace of drilling, the person in charge of geosteering is hard-pressed just to interpret the incoming data, much less contemplating the uncertainty in it” (Rassenfoss, 2022, SPE JPT). Following the newest trends in the industry geosteering workflow of the future shall capture and update uncertainty in an ensemble of geomodels. The most advanced ensemble methods are the core of closed-loop reservoir management (EnCLRM), the new standard for optimizing the development of petroleum fields. However, EnCLRM is not fast enough for real-time operations due to the involved geomodelling complexity. We plan to remove this complexity by creating new Generative-Network (GN) geomodels. GN-geomodels “learn geology” before the operation and have sub-second performance. They unlock next-generation data assimilation and new predictive decision-support AI. DISTINGUISH will develop these technologies and combine them into the “geosteering workflow of the future.” It proposes a new way of thinking substituting “depth of detection” with “distance of prediction” and probabilistic decision support. We plan early testing on field data and with future technology users to accelerate adoption. DISTINGUISH addresses research challenges related to well placement from an Oil-&-Gas-21 machine-learning report. According to the report, better well placement may lead to additional discoveries on the Norwegian Continental Shelf, an increased net present value from drilling, and significant CO2 emission reductions.