Performance evaluation of state-of-the-art multimodal remote sensing image matching methods in the presence of noise

Analysis of different objective functions in petroleum field development optimization


 This paper presents an advanced version of the previous IDLHC-ML approach, designed to enhance life-cycle well control optimization by reducing simulations. Unlike its predecessor, this updated method, called IDLHC-MLR, uses representative models (RMs) to address the effect of geological uncertainties on production strategies. Despite presenting additional computational challenges, considering uncertainties in determining effective strategies is crucial, making the new IDLHC-MLR approach a valuable solution.
 The IDLHC-MLR combines the iterative discrete Latin hypercube optimization algorithm (IDLHC) with machine learning (ML) to robustly optimize the well's bottom-hole pressure (BHP) throughout the field management period. The method is applied to the UNISIM-I-M benchmark of Namorado Field, located in the Campos Basin, Brazil. The IDLHC-MLR method trains the initial ML model with well BHP strategies robustly applied to all RMs in the first iteration of IDLHC. In subsequent iterations, the trained ML model is used to predict the expected monetary value of the RMs, and only a subset of new strategies with the highest expected outcome is selected for simulation. In addition, the ML algorithms are retrained with newly generated strategies over the iterations to improve the model's accuracy.
 The IDLHC-MLR incorporates stacked ensemble learning, which leverages predictions from various base machine learning models to train a secondary algorithm. In this approach, the IDLHC-MLR employs multiple base learners such as Lasso, Gradient Boosting, and Random Forest to make predictions, which are then inputted into a multi-layer perceptron for training purposes. This integration of multiple base models results in a more robust and accurate prediction and provides a 45% reduction in the number of simulations required compared to the traditional IDLHC model while maintaining similar expected monetary value. To conclude, utilizing inexpensive ML models effectively reduces computational time by substituting costly full-physics reservoir simulations.
 The significant computational time required for full-physics simulations, particularly when considering multiple scenarios to account for uncertainties, can pose a challenge to meeting project deadlines. The IDLHC-MLR methodology, incorporating low-cost ML models, offers a practical solution to reduce computational time, increasing the likelihood of successful project implementation within the given timeline.

Optimizing Well Control Strategies with IDLHC-MLR: A Machine Learning Approach to Address Geological Uncertainties and Reduce Simulations

The rendering of virtual three-dimensional (3D) structures represented by Point Cloud (PC) allows the representation of internal and/ or external environments to buildings. However, the compilation of 3D geometric models is influenced by the intrinsic characteristics of PCs, which can be mitigated by the application of an PC simplification operator. According to the mathematical norms of fractal geometry, it was assumed that a PC is characterized by self-similarity. Two experimental datasets acquired with an SLT in static mode indoors were used. Four tasks were accomplished: sampling and structuring of a PC to solve the problem of random distribution, from an octree structure; estimation of the curvature of the points and the roughness of a neighbourhood for the extraction of edge points by the analysis of self-similarity and application of the Statistical Outliers Remove (SOR) algorithm, for the elimination of outliers points; uniform voxelization, to simplify the intermediate points; application of the Iterative Closest Point (ICP) algorithm to register the sets generated in the same local coordinate system. The use of voxelization was satisfactory, but once the voxel size is manually defined, the PC can be oversimplified and lose essential characteristics. This can be minimized by the primary analysis of the edge points, generating a set that is uniform, less noisy, and self-similar to the original set. To achieve a minimum density of points to model an environment three-dimensionally, one must analyse the geometric self-similarity characteristics of the PC to produce a simplified set self-similar to the original, considering the premises of fractal geometry. It is recommended to create an automatic simplification process to minimize the subjectivity coming from the analyst.

Anuário do Instituto de Geociências

Abstract 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 WAG eq , a novel approach that expedites the convergence of the optimization algorithm for miscible water alternating gas (WAG) injection in carbonate reservoirs. The WAG eq 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 WAG eq 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 ( W CUT ), gas–oil ratio (GOR), and wells cumulative gas production, to induce desirable effects on production and WAG profile. To evaluate WAG eq ’s effectiveness, two other approaches were compared: a benchmark solution named WAG bm , 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 CO 2 content. The WAG eq outperformed the baseline procedure, improving the NPV by 6.7% or 511 USD million. Moreover, WAG eq required fewer simulations (less than 350) than WAG bm (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, WAG eq significantly reduces computational requirements while creating consistent patterns across injectors, which are crucial factors to consider when planning a practical WAG strategy. 

/pdf/accelerated-optimization-of-co2-miscible-water-alternating-28s4rfia.pdf

Accelerated optimization of CO2-miscible water-alternating-gas injection in carbonate reservoirs using production data-based parameterization

The rendering of virtual three-dimensional (3D) structures represented by Point Cloud (PC) allows the representation of internal and/or external environments to buildings. However, the compilation of 3D geometric models is influenced by the intrinsic characteristics of PCs, which can be mitigated by the application of an PC simplification operator. According to the mathematical norms of fractal geometry, it was assumed that a PC is characterized by self-similarity. Two experimental datasets acquired with an SLT in static mode indoors were used. Four tasks were accomplished: sampling and structuring of a PC to solve the problem of random distribution, from an octree structure; estimation of the curvature of the points and the roughness of a neighbourhood for the extraction of edge points by the analysis of self-similarity and application of the Statistical Outliers Remove (SOR) algorithm, for the elimination of outliers points; uniform voxelization, to simplify the intermediate points; application of the Iterative Closest Point (ICP) algorithm to register the sets generated in the same local coordinate system. The use of voxelization was satisfactory, but once the voxel size is manually defined, the PC can be oversimplified and lose essential characteristics. This can be minimized by the primary analysis of the edge points, generating a set that is uniform, less noisy, and self- similar to the original set. To achieve a minimum density of points to model an environment three-dimensionally, one must analyse the geometric self- similarity characteristics of the PC to produce a simplified set self-similar to the original, considering the premises of fractal geometry. It is recommended to create an automatic simplification process to minimize the subjectivity coming from the analyst.

/pdf/3d-cartographic-generalization-of-lidar-point-clouds-based-3ui0ec0f.pdf

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

Closed-form solution to point- and plane-based co-registration of terrestrial LiDAR point clouds

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Accelerated optimization of CO2-miscible water-alternating-gas injection in carbonate reservoirs using production data-based parameterization

3D Cartographic Generalization of LiDAR Point Clouds Based on the Principle of Self-Similarity of a Deterministic Fractal Structure

Closed-form solution to point- and plane-based co-registration of terrestrial LiDAR point clouds