EN 
BR This article introduces novel algorithms for fractures and vugs recognition in computed tomography (CT) rock images. The proposed algorithms can be used in both two-dimensional (2D) and three-dimensional (3D) approaches to accurately identify fractures and vugs within rock samples. A detailed explanation of the implemented method is provided, elucidating the underlying principles of the algorithms. Furthermore, the method’s applicability is investigated through testing with various models of pre-trained neural networks.The study contributes to rock image analysis by introducing effective techniques for identifying fractures and vugs in CT scans that can be applied for geological and engineering purposes. In a demonstrative application, this methodology was implemented to generate a two-dimensional finite element mesh, thereby facilitating the simulation of the Darcy equation using the Finite Elements Method.
Author: João Lucas Braga Da Silva
Comparing Multi-Objective Surrogate Models For Binary Petroleum Well Placement
Optimization problems often involve expensive computational calculations. For example, in petroleum well place-ment problems, optimization techniques are applied to reservoir simulation models to identify the optimal number and positions of exploration wells. Each evaluation of a well placement plan requires extensive calculations of differential equations to forecast fluid behavior. To enhance the quality of the optimization techniques without increasing the computational burden, re-searchers have explored combining optimization methods with Surrogate Models, approximation techniques used in regression problems to replace expensive objective functions with more computationally efficient evaluations. However, since most of the research on surrogate modeling has been performed for single-objective problems, surrogate modeling for multi-objective problems remains a challenging task. For this reason, this paper provides a comparative analysis of three supervised learning techniques, Random Forest (RF), Multi-Layer Perceptron (MLP), and k-Nearest Neighbors (k-NN) when applied as multi-objective surrogate models to approximate the real objective function val-ues of five datasets of a real-world expensive discrete petroleum well placement problem. We also investigate if an ensemble of these three methods surpasses their individual performances. The results demonstrate that the ensemble prediction outperforms the individual methods in 4 out of 5 datasets in terms of Root Mean Square Error, while k-NN provides the best Mean Absolute Error in 4 out of 5 datasets. Nonetheless, the Friedman and Bonferroni-Dunn tests indicate that there are only statistically significant differences between MLP and the remaining methods. Therefore, the choice between k-NN, RF, or the ensemble is indifferent.
Incorporation of Conceptual Geological Model for Fracture Distribution in 3D Reservoir Modeling: An Example of Brazilian Pre-Salt Carbonate Reservoir
Conceptual Geological Models (CGM) serve as a robust tool for 3D reservoir model building, since they allow the representation of geological knowledge in the subsurface, guiding the depiction of fault and fracture distribution, providing insights into their local occurrences, densities, orientations, and aperture. The Brazilian pre-salt carbonate reservoirs are characterized by complex fault systems and natural fractures, with variations related to structural geology, paleotopography, and stratigraphy. This study aims to integrate a CGM into 3D reservoir modeling, focusing on faults and fractures below seismic resolution foran area within an oilfield in the Santos Basin (Santos Outer High), centered on the Barra Velha Formation (BVE), where a PSDM seismic volume, wells (with conventional and special logs), and core samples and thin sections were available. Data analysis resulted in the interpretation of the main horizons in the area and the preferential distribution of fracture families (P10, P20, and P21). Findings from a CGM of fracture distribution were incorporated into reservoir modeling through the Discrete Fracture Network (DFN) methodology, in particular, that the fractures in the BVE are generally correlated with silica-rich zones of the formation. From this, maps of silica distribution were elaborated for the different stratigraphic units of the BVE and used as a constraint for the creation of the fracture networks and the DFN model. Preliminary results show that the use of a CGM proved to be advantageous in the DFN model creation process.
Investigating Fail in Downhole Chemical Injection Valves (CIVs) – A Teardown Analysis
Chemicals have been injected into the downhole of the oil wells in an attempt to ensure efficient production.
This measure is a usual oilfield strategy to improve the characteristics of crudes or deal with some flow
assurance problems, such as emulsions, scales, paraffin or asphaltenes deposition, etc. To overcome these
issues, downhole chemical injection systems (DHCI) have been installed in production facilities, in which the
injection of chemicals is controlled by chemical injection valves (CIVs). In this work, it was investigated the
possible causes of the failure of four commercial CIVs from demulsifier injection lines installed in heavy oil
production systems. The analysis consisted of disassembling the CIVs and analyzing their internal elements,
seeking the cause of the failure. A solid material (clogging) was found in some specific parts of the CIVs, which
could be the main cause of the CIVs’ failure. Solubility tests indicated a polar or apolar characteristic,
depending on the CIV. After the analysis, the CIVs were cleaned and reassembled, and tests in a high-pressure
line indicated that all of them got back to work properly. These findings have significant implications for
diagnosing the root causes of CIV failures in demulsifier injection lines, presenting a procedure to recover
obstructed CIVs, and offering preventive measures against future clogging issues.
Influence of Fluid Viscosity on the Flow Behavior within the Impeller of an Electrical Submersible Pump (ESP)
The electrical submersible pump (ESP) plays a crucial role in artificial lift operations in the oil and gas industry.
The viscosity of the pumped fluid significantly influences the flow dynamics within the ESP, thereby impacting
the performance of the machine. In this context, flow visualization techniques can unveil intricate details of the
flow in ESP impellers, thus providing a deeper understanding of the relationship between flow behavior and
pump performance. This is the main idea of the present document, which utilizes the particle image velocimetry
(PIV) technique to experimentally investigate a mineral oil flow, 𝜇 = 14 𝑐𝑃, in a transparent prototype of a real
impeller, P23 model. The paper reports insights into the flow in the pump’s rotating component at different flow
rates that correspond to percentages of the best efficiency point (BEP). Average velocity fields and turbulent
kinetic energy plots indicate that flow dynamics are highly dependent on the operating conditions of the ESP.
A comparison between results for oil and water completes the analysis, as it highlights the effects of viscosity
on the flow characteristics. This type of study is useful to validate numerical simulations, support mathematical
models, and develop improved impeller designs.
Cross-validation in an Iterative Ensemble Smoother: Stopping Earlier for Better
Iterative ensemble smoothers (IES) are among the popular history matching (UM) algorithms for reservoir characterization. The actual deployment of an IES algorithm requires implementing certain stopping criteria, normally adopted for runtime control (e.g., by stopping the IES when it reaches the maximum number of iterations) and/or safeguarding the HM performance (e.g., by preventing the simulated data from overfitting the actual observations). In practice, for various reasons, it is often challenging for existing stopping criteria to simultaneously achieve both purposes. One noticeable issue, as illustrated in this work, is that in many situations, the qualities of the estimated reservoir models may already start to deteriorate before a conventional stopping criterion activates to terminate the iteration process. Following this observation, one practically important question arises: Is it possible to further improve the efficacy of the IES algorithm by designing a different stopping criterion so that the IES can stop earlier, saving computation costs while achieving better HM performance?
As one of the rare attempts in the community, this work aims to investigate the use of a new IES stopping criterion that has the potential to provide an affirmative answer to the above question. In this regard, our main idea is based on the concept of cross-validation (CV), routinely adopted in supervised machine learning (SML) problems for early stopping to prevent SML models from overfitting the training data. Despite noticed similarities between HM and SML problems, some fundamental differences exist, making it fail to work well if one directly extends a vanilla CV procedure from SML to HM. To tackle this identified challenge, we design an efficient CV procedure tailored for HM problems, and inspect the performance of an IES algorithm equipped with this CV procedure (IES-CV) in both synthetic and real field case studies. Our numerical investigations indicate that the IES-CV algorithm achieves promising HM performance in all case studies, confirming the possibility that with the aid of a proper stopping criterion, an IES algorithm can terminate at an appropriate iteration step with near-optimal HM performance. Beyond these numerical findings, it is also our hope that the current work may help improve the best practices of applying IES to HM problems, taking advantage of the effective, CV-based stopping criterion.
A survey on multi-objective algorithms for model-based oil and gas production optimization: current status and future directions
In the area of reservoir engineering, the optimization of oil and gas production is a complex task involving a myriad of interconnected decision variables shaping the production system’s infrastructure. Traditionally, this optimization process was centered on a single objective, such as net present value, return on investment, cumulative oil production, or cumulative water production. However, the inherent complexity of reservoir exploration necessitates a departure from this single-objective approach. Multiple conflicting production and economic indicators must now be considered to enable more precise and robust decision-making. In response to this challenge, researchers have embarked on a journey to explore field development optimization of multiple conflicting criteria, employing the formidable tools of multi-objective optimization algorithms. These algorithms delve into the intricate terrain of production strategy design, seeking to strike a delicate balance between the often-contrasting objectives. Over the years, a plethora of these algorithms have emerged, ranging from a priori methods to a posteriori approach, each offering unique insights and capabilities. This survey endeavors to encapsulate, categorize, and scrutinize these invaluable contributions to field development optimization, which grapple with the complexities of multiple conflicting objective functions. Beyond the overview of existing methodologies, we delve into the persisting challenges faced by researchers and practitioners alike. Notably, the application of multi-objective optimization techniques to production optimization is hindered by the resource-intensive nature of reservoir simulation, especially when confronted with inherent uncertainties. As a result of this survey, emerging opportunities have been identified that will serve as catalysts for pivotal research endeavors in the future. As intelligent and more efficient algorithms continue to evolve, the potential for addressing hitherto insurmountable field development optimization obstacles becomes increasingly viable. This discussion on future prospects aims to inspire critical research, guiding the way toward innovative solutions in the ever-evolving landscape of oil and gas production optimization.
Balancing Conflicting Objectives in Oilfield Development: A Robust Multi-objective Optimization Framework
Optimizing production strategies for gas and oil fields is a critical challenge in petroleum engineering as it involves balancing multiple and often conflicting objectives, for instance, enhancing production rates, reducing operational costs, and mitigating the environmental effects of cumulative water or gas production. This study aims to develop and apply a robust multi-objective optimization framework to the UNISIM-II-D reservoir, which represents Brazilian pre-salt fields on nine representative models (RMs) to address geological uncertainties while considering three economic scenarios. The study focuses on maximizing expected monetary value (EMV) and the net present value of RM4 considering economic uncertainty (NPVeco of RM4), of the most pessimistic scenario among the RMs. The optimization variables are location, type (injection or production), and number of wells, while the non-dominated sorting genetic algorithm II (NSGA-II) is employed for multi-objective optimization. The study indicates that prioritizing EMV, the primary objective function, does not inevitably result in the NPVeco of RM4 achieving its optimal or near-optimal value. However, by employing the proposed framework, a 3 % improvement in EMV and a 28 % enhancement in the NPVeco of RM4 is achieved compared to the single objective optimization of EMV, which highlights the strength and robustness of the framework.
Fundamental comparison between the pseudopotential and the free energy lattice Boltzmann methods
The pseudopotential and free energy models are two popular extensions of the lattice Boltzmann method for multiphase flows. Until now, they have been developed apart from each other in the literature. However, important questions about whether each method performs better needs to be solved. In this work, we perform a fundamental comparison between both methods through basic numerical tests. This comparison is only possible because we developed a novel approach for controlling the interface thickness in the pseudopotential method independently on the equation of state. In this way, it is possible to compare both methods maintaining the same equilibrium densities, interface thickness, surface tension and equation of state parameters. The well-balanced approach was selected to represent the free energy. We found that the free energy one is more practical to use, as it is not necessary to carry out previous simulations to determine simulation parameters (interface thickness, surface tension, etc.). In addition, the tests proofed that the free energy model is more accurate than the pseudopotential model. Furthermore, the pseudopotential method suffers from a lack of thermodynamic consistency even when applying the corrections proposed in the literature. On the other hand, for both static and dynamic tests we verified that the pseudopotential method was able to simulate lower reduced temperature than the free energy one. We hope that these results will guide authors in the use of each method.