Proactive geosteering workflows include a number of steps that are repeated as the drilling operation progresses. First, various measurements such as gamma ray, gravity or electromagnetic (EM) data are collected while drilling. Second, the recorded data samples are processed and used to update the geomodel, including parameters around the well relevant to the steering (e.g., reservoir boundaries, faults, geophysical properties, fluid contacts, etc.). Finally, geosteering decisions are made based on the updated geomodel, other available knowledge and operational constraints while drilling. In many situations, the inversion or interpretation procedure used to update the existing geomodel provides only a single admissible solution, while the uncertainty is not quantified. As a consequence, when put together with other constraints that control the placement of the well, the decision making process might be biased, increasing the risk of taking poor decisions.An alternative to the conventional deterministic inversion methods is the ensemble-based inversion algorithms (for instance, the ensemble Kalman filter), which have been widely applied in various disciplines such as meteorology, oceanography, hydrology and reservoir engineering in the last decade, and are praised for their satisfactory performance and ability to quantify the uncertainty. In this work, we propose an ensemble-based framework that uses available logging while drilling measurements for continuously updating the geomodel and optimizing the placement of the remaining well path under uncertainty. Deep EM measurements are chosen as observed data for this study because they combine good range and reliability for the look around and are readily available in many drilling operations. Furthermore, a 3D finite difference EM modelling tool, capable of taking into account complex reservoir geometries, is used to solve the forward problem. The proposed framework is tested on both simple and more realistic synthetic cases. The obtained results suggest that the ensemble-based methodology can match the synthetic truth in a probabilistic sense. The subsequent well placement is optimized in a robust way based on these estimations, and achieves good coverages of the reservoir zones.
展开▼
