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Problem statement: Oil and gas are global fuels obtained primarily from drilling wells in underground terrestrial reservoirs. Vertical drilling is preferred because of its simplicity and therefore low cost, but subsurface targets can often be procured only by directing the wellbore along predefined non-vertical trajectories. For instance, directional drilling must be employed to reach locations inaccessible to the drilling rig, to side track an existing well (multilateral drilling), or to drill multiple wells from the same offshore platform (horizontal drilling). Approach: A complete knowledge of the wellbore direction and orientation during the drilling process is essential to guarantee proper directional drilling procedure. Results: Thus, besides the conventional drilling assembly, directional drilling operations require sensors to provide azimuth, inclination and toolface angles of the drill. These sensors are part of the Measurement-While-Drilling (MWD) tool, which in current technology is installed several feet behind the drill bit. In such systems, values for inclination and toolface angles are determined from accelerometer measurements at predetermined stationary surveying stations; these values are then incorporated with magnetometer measurements to deliver the azimuth angle. Values for inclination and azimuth angles at the current surveying station are combined with those from the previous station to compute the position of the probe. However, there is no accurate information about the wellbore trajectory between survey stations. Additionally, the magnetic field of the magnetometers has deleterious effect on the overall accuracy of surveying measurements. Conclusion: A method to provide continuous information about the wellbore trajectory has been developed in this study. The module developed integrates a Rotary Steerable System (RSS) and MWD tool into one drilling probe utilizing Inertial Navigation System (INS) technology. This

Problem statement: An accurate knowledge of geographic positions of sonobuoys is critical for the conduct of antisubmarine warfare operations and detected target localization. Deployed from an airborne platform or a surface vessel, arrays of sonobuoys could be used to efficiently track and localize submarines. Lastly, some sonobuoys were being equipped with GPS for improving system accuracy and potentially allowing networked Sonobuoy positioning. However, the computation of the range using the propagation loss profile and the data of one sonobuoy usually leads to inaccurate target localization due to several effects and uncertainties. It was, alternatively, reported that if the target is within the detection rage of two or more sonobuoys, greatly improved target localization can be achieved. Approach: Aim of this research was to investigate the feasibility of fusing data from a distributed field of GPS sonobuoys to create an Artificial Intelligence (AI) based model for the error of the range computation in case of the target being detected by only one sonobuoy. Proposed module was designed utilizing Adaptive Neuron-Fuzzy Inference Systems (ANFIS) to estimate the range error associated with the computation using the propagation loss profile when the target is within the detection range of only one sonobuoy. The architecture of the proposed ANFIS system had two unique features. First was the real-time cross-validation applied during the update (training) procedure of the ANFIS-based module while the target was detected by two sonobuoys and the range was computed. Second feature was the use of non-overlapping and moving window for the real-time implementation of the ANFIS-based data fusion module. Results: Performance of the proposed system was examined with simulation data considering different scenarios for both the array of GPS sonobuoys and the target. Results showed that the corrected positioning by one sonobuoy is completely following the pos

Problem statement: An accurate knowledge of geographic positions of sonobuoys is critical for the conduct of antisubmarine warfare operations and detected target localization. Deployed from an airborne platform or a surface vessel, arrays of sonobuoys could be used to efficiently track and localize submarines. Lastly, some sonobuoys were being equipped with GPS for improving system accuracy and potentially allowing networked Sonobuoy positioning. However, the computation of the range using the propagation loss profile and the data of one sonobuoy usually leads to inaccurate target localization due to several effects and uncertainties. It was, alternatively, reported that if the target is within the detection rage of two or more sonobuoys, greatly improved target localization can be achieved. Approach: Aim of this research was to investigate the feasibility of fusing data from a distributed field of GPS sonobuoys to create an Artificial Intelligence (AI) based model for the error of the range computation in case of the target being detected by only one sonobuoy. Proposed module was designed utilizing Adaptive Neuron-Fuzzy Inference Systems (ANFIS) to estimate the range error associated with the computation using the propagation loss profile when the target is within the detection range of only one sonobuoy. The architecture of the proposed ANFIS system had two unique features. First was the real-time cross-validation applied during the update (training) procedure of the ANFIS-based module while the target was detected by two sonobuoys and the range was computed. Second feature was the use of non-overlapping and moving window for the real-time implementation of the ANFIS-based data fusion module. Results: Performance of the proposed system was examined with simulation data considering different scenarios for both the array of GPS sonobuoys and the target. Results showed that the corrected positioning by one sonobuoy is completely following the pos