A Decision Support System for Multi-target Geosteering

Modern geosteering is heavily dependent on real-time interpretation of deep electromagnetic (EM) measurements. We have developed a methodology to construct a deep neural network (DNN) model...

https://library.seg.org/doi/pdf/10.1190/geo2020-0389.1

Modeling extra-deep electromagnetic logs using a deep neural network

A key objective in any reservoir development plan is to achieve maximum reservoir exploitation which is usually quantified using an economic objective such as net present value (NPV). A key element of such an optimized development plan is an optimized well planning scheme (number, placement and trajectories of the wells). In the well planning phase, it is important to quantify the geological uncertainty. In this study, a new approach is presented in which the targets and thereby the trajectories of the wells are optimized while the geological uncertainties are taken into account. The latter is achieved by using an ensemble of updated reservoir models resulting from assisted history matching (AHM) as the input for the optimization of the field development plan. For the case presented in this study, the reservoir structure, more specific the top and bottom of the reservoir, is assumed to be the main source of uncertainty. To optimize the well targets and trajectories, the Stochastic Simplex Approximate Gradients (StoSAG) methodology is used. A parameterization of the well path is proposed, in which the angles, azimuths and measured depths of the targets are used as controls to optimize the trajectories of the horizontal wells. With this parameterization, the horizontal section is not always straight, in contrast to the approaches presented in many previous publications. The proposed workflow has been applied successfully on a realistic synthetic case inspired from a real field case. The results show that significant increases in objective function can be achieved when well trajectories are optimized constrained to uncertainties in the structural model

Well trajectory optimization constrained to structural uncertainties

Analysis on the nonlinear lateral vibration of drillstring in curved wells with beam finite element

pdf/verification-of-a-real-time-ensemble-based-method-for-1bfg3mz7.pdf

Verification of a real-time ensemble-based method for updating earth model based on GAN

Various logging-while-drilling (LWD) and seismic-while-drilling (SWD) tools offer opportunities to obtain geological information near the bottom-hole-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 to the data from a surface seismic survey. The use of this 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 estimate of the reservoir surfaces, reservoir thickness, and the depth of the initial oil-water contact and their associated uncertainty are obtained through the ensemble Kalman filter using 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 EnKF.

Optimization of Well Trajectory Under Uncertainty for Proactive Geosteering

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Optimization of Well Trajectory Under Uncertainty for Proactive Geosteering