Geosteering is a sequential decision process under uncertainty. The goal of geosteering is to maximize the expected value of the well, which should be defined by an objective value-function for each operation.In this paper we present a real-time decision support system (DSS) for geosteering that aims to approximate the uncertainty in the geological interpretation with an ensemble of geomodel realizations. As the drilling operation progresses, the ensemble Kalman filter is used to sequentially update the realizations using the measurements from real-time logging while drilling. At every decision point a discrete dynamic programming algorithm computes all potential well trajectories for the entire drilling operation and the corresponding value of the well for each realization. Then, the DSS considers all immediate alternatives (continue/steer/stop) and chooses the one that gives the best predicted value across the realizations. This approach works for a variety of objectives and constraints and suggests reproducible decisions under uncertainty. Moreover, it has real-time performance.The system is tested on synthetic cases in a layer-cake geological environment where the target layer should be selected dynamically based on the prior (predrill) model and the electromagnetic observations received while drilling. The numerical closed-loop simulation experiments demonstrate the ability of the DSS to perform successful geosteering and landing of a well for different geological configurations of drilling targets. Furthermore, the DSS allows to adjust and reweight the objectives, making the DSS useful before fully-automated geosteering becomes reality.

A systematic decision analytic methodology for providing insight into horizontal well placement decisions is presented. These decisions require access to real-time data and to input from subject-experts on the team and from the decision-maker. The available information and outcomes of alternatives are associated with uncertainty. Furthermore, the decision-maker may have multiple objectives, such as future productions, wellbore configuration, and drilling costs. The methodology uses Bayesian decision networks, also known as influence diagrams, to frame, analyze, and support operational decisions.Influence diagrams are compact graphical representations of decisions and are based on decision and probability theories. They rigorously illustrate the interrelationships among decisions, key uncertainties, and value functions. Furthermore, they allow incorporating sensor readings, sub-surface model outputs, and experts' knowledge, which enables probabilistic inference and decision support in real-time contexts. Finally, the influence diagrams models may be used to calculate value of information and of control.In this paper, we focus on supporting well placement decisions involving multiple objectives. We explore the feasibility and suitability of using two potential extensions of the influence diagrams: (1) multiple-attribute utility influence diagram and (2) multiple-objective influence diagram. Using the methodology, the example shows that both extensions have the potential to improve the decision-making process within the multi-disciplinary team. They can provide further insight into decisions by ranking the value of information for key uncertainties from the perspective of different sub-groups of the drilling team.Following a systematic decision analytic methodology, in particular, decision analysis cycle, and applying influence diagrams in drilling operations would create value by reducing losses (materials and drilling/completion processes' downtime) and increasing future production through optimized well placement while drilling.