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Global pose refinement is a significant challenge within Simultaneous Localization and Mapping (SLAM) frameworks. For LIDAR-based SLAM systems, pose refinement is integral to correcting drift caused by the successive registration of 3D point clouds collected by the sensor. A divergence between the actual and calculated platform paths characterizes this error. In response to this challenge, we propose a linear, parameter-free model that uses a closed circuit for global trajectory corrections. Our model maps rotations to quaternions and uses Spherical Linear Interpolation (SLERP) for transitions between them. The intervals are established by the constraint set by the Least Squares (LS) method on rotation closure and are proportional to the circuit's size. Translations are globally adjusted in a distinct linear phase. Additionally, we suggest a coarse-to-fine pairwise registration method, integrating Fast Global Registration and Generalized ICP with multiscale sampling and filtering. The proposed approach is tested on three varied datasets of point clouds, including Mobile Laser Scanners and Terrestrial Laser Scanners. These diverse datasets affirm the model's effectiveness in 3D pose estimation, with substantial pose differences and efficient pose optimization in larger circuits. [ABSTRACT FROM AUTHOR]

Enhancing oil recovery in reservoirs with light oil and high gas content relies on optimizing the miscible water alternating gas (WAG) injection profile. However, this can be costly and time-consuming due to computationally demanding compositional simulation models and numerous other well control variables. This study introduces WAGeq, a novel approach that expedites the convergence of the optimization algorithm for miscible water alternating gas (WAG) injection in carbonate reservoirs. The WAGeq leverages production data to create flexible solutions that maximize the net present value (NPV) of the field, while providing practical implementation of individual WAG profiles for each injector. The WAGeq utilizes an injection priority index to rank the wells and determine which should inject water or gas at each time interval. The index is built using a parametric equation that considers factors such as producer and injector relationship, water cut (WCUT), gas–oil ratio (GOR), and wells cumulative gas production, to induce desirable effects on production and WAG profile. To evaluate WAGeq's effectiveness, two other approaches were compared: a benchmark solution named WAGbm, in which the injected fluid is optimized for each well over time, and a traditional baseline strategy with fixed 6-month WAG cycles. The procedures were applied to a synthetic simulation case (SEC1_2022) with characteristics of a Brazilian pre-salt carbonate field with karstic formations and high CO2 content. The WAGeq outperformed the baseline procedure, improving the NPV by 6.7% or 511 USD million. Moreover, WAGeq required fewer simulations (less than 350) than WAGbm (up to 2000), while delivering a slightly higher NPV. The terms of the equation were also found to be essential for producing a WAG profile with regular patterns on each injector, resulting in a more practical solution. In conclusion, WAGeq significantly reduces computational requirements while creating consistent patterns across injectors, which are crucial factors to consider when planning a practical WAG strategy. [ABSTRACT FROM AUTHOR]

A key decision in field management is whether or not to acquire information to either improve project economics or reduce uncertainties. A widely spread technique to quantify the gain of information acquisition is Value of Information (VoI). However, estimating the possible outcomes of future information without the data is a complex task. While traditional VoI estimates are based on a single average value, the Chance of Success (CoS) methodology works as a diagnostic tool, estimating a range of possible outcomes that vary because of reservoir uncertainties. The objective of this work is to estimate the CoS of a 4D seismic before having the data, applied to a complex real case (Norne field). The objective is to assist the decision of whether, or not, to acquire further data. The methodology comprises the following steps: uncertainty quantification, selection of Representative Models (RMs), estimation of the acquisition period, production strategy optimization and, finally, quantification of the CoS. The estimates use numerical reservoir simulation, economic analysis, and uncertainty evaluation. We performed analyses considering perfect and imperfect information. We aim to verify the increment in economic return when the 4D data identifies the closest-to-reality reservoir model. While the traditional expected VoI calculation provides only an average value, this methodology has the advantage of considering the increase in the economic return due to reservoir uncertainties, characterized by different RMs. Our results showed that decreased reliability of information affected the decision of which production strategy to select. In our case, information reliability less than 70% is insufficient to change the perception of the uncertain reservoir and consequently decisions. Furthermore, when the reliability reached around 50%, the information lost value, as the economic return became similar to that of the case without information acquisition.

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