Optimization of Well Trajectory Under Uncertainty for Proactive Geosteering

Abstract

Various logging-while-drilling (LWD) and seismic-while-drilling (SWD) tools offer opportunities to obtain geological information near the bottomhole assembly during the drilling process. These real-time in-situ data provide relatively high-resolution information around and possibly ahead of the drilling path compared with the data from a surface seismic survey. The use of these in-situ data offers substantial potential for improved recovery through continuous optimization of the remaining well path while drilling.We show an automated workflow for proactive geosteering through continuous updating of the estimates of the Earth model and robust optimization of the remaining well path under uncertainty. A synthetic example is shown to illustrate the proposed workflow. The estimates of the depths of the reservoir surfaces and the depth of the oil/water contact and their associated uncertainty are obtained through the ensemble Kalman filter by use of directional-resistivity measurements. A robust optimization is used to compute the well position that minimizes the average cost function evaluated on the ensemble of geological models estimated from the ensemble Kalman filter (EnKF). The effect of modeling errors and the effect of joint estimation of the depths of the boundaries and gridblock resistivity are also investigated.

A decision support system for multi-target geosteering

“…Therefore, we use a simple integral model for electromagnetic (EM) measurements following Chen et al (2015). The tool set-up in that paper has a lookaround capability of about 5 meters, and it is sensitive to resistivity in the up, down and side directions, see figure 2.…”

“…The optimization algorithm presented in this section extends the classical robust optimization (Chen et al, 2015;Lorentzen et al, 2006;Chen et al, 2009) to include the up-to-date knowledge when optimizing the full trajectory ahead of the bit. Due to the possibility of future learning, it is essential that only the first point is chosen by the robust optimization while the rest of the 4 Note that the evaluations for all the trajectories for the individual realizations have already been performed and cached on step one of the algorithm by applying equations (3).…”

“…The decision points are equally spaced in the horizontal direction with approximately one stand between them (28.6 meters apart). The earth model is updated using unprocessed synthetic EM measurements (taken at the decision points) that are modelled by a simple integral model as in Chen et al (2015). The depth of investigation (DOI) of the synthetic tool is about 5 meters, see figure 2.…”

Modeling extra-deep electromagnetic logs using a deep neural network

“…To reach high-quality geosteering decisions, it is convenient to use statistical (Bayesian) interpretation, which provides uncertainties (Kullawan et al, 2014). Bayesian workflows for updating geomodels while drilling provide promising results in synthetic testing (Chen et al, 2015;Luo et al, 2015;Alyaev et al, 2019). At the same time, adequate uncertainty representation may require up to 100,000 forward simulations per logging position (Dupuis and Manuscript received by the Editor 5 June 2020; revised manuscript received 19 January 2021; published ahead of production 10 March 2021; published online 13 May 2021.…”

Man vs machine: an experimental study of geosteering decision skills

“…[3] developed a Bayesian estimator of stratigraphy that can be further extended with forward well planing. [4,5] considered an ensemble-based method for optimization of reactive steering under uncertainty. [6] demonstrated application of dynamic programming for finding optimal long-term decision strategies for a certain set of geosteering problems.…”