An Automatic Well Planner for Complex Well Trajectories

Abstract

A data-driven automatic well planner procedure is implemented to develop complex well trajectories by efficiently adapting to near-well reservoir properties and geometry. The procedure draws inspiration from geosteering drilling operations, where modern logging-while-drilling tools enable the adjustment of well trajectories during drilling. Analogously, the proposed procedure develops well trajectories based on a selected geology-based fitness measure using an artificial neural network as the decision maker in a virtual sequential drilling process within a reservoir model. While neural networks have seen extensive use in other areas of reservoir management, to the best of our knowledge, this work is the first to apply neural networks on well trajectory design within reservoir models. Importantly, both the input data generation used to train the network and the actual trajectory design operations conducted by the trained network are efficient calculations, since these rely solely on geometric and initial properties of the reservoir, and thus do not require additional simulations. Therefore, the main advantage over traditional methods is the highly articulated well trajectories adapted to reservoir properties using a low-order well representation. Well trajectories generated in a realistic reservoir by the automatic well planner are qualitatively and quantitatively compared to trajectories generated by a differential evolution algorithm. Results show that the resulting trajectories improve productivity compared to straight line well trajectories, both for channelized and geometrically complex reservoirs. Moreover, the overall productivity with the resulting trajectories is comparable to well solutions obtained using differential evolution, but at a much lower computational cost.

Efficient well placement optimization under uncertainty using a virtual drilling procedure

“…Moreover, the focus is on describing how the AWP translates the well heel and toe coordinates into a refined trajectory and its tailoring to the specific properties of each realization of an ensemble. The interested reader is referred to [22] for a detailed description of the AWP methodology.…”

“…The goal of the AWP is to maximize the objective function where N(x h , x t ) = n represents the number of well segments. The outer sum runs over the training set of heel and toe coordinates in relevant reservoir models [22]. The term b t (x n ) is defined as:…”

“…Embedding the AWP within a robust optimization scheme gives the ability to adapt to different geological realization while maintaining a reduced number of well path variables, i.e., the same number of well path variables as a coarse idealized straight-line well. For single-realization well placement optimization, introducing the AWP within the optimization loop has been shown to improve cost function progression and to yield higher cost function values [22]. This work expands this approach for well placement optimization into a robust optimization scheme.…”

“…In this approach, the starting point and direction are determined by the optimization procedure, whereas the well description tailored for each realization is determined by a separate automatic procedure. By doing so, the optimization and simulation domains can have different scales with respect to well parameterizations, i.e., well descriptions can be coarser in the iterative layer that operates on optimization variables, while simulations are performed using highly-refined, realization-specific, well trajectories [22].…”