Autor: João Lucas Braga Da Silva

Enhancing Asset Profitability with Flexibility for Life Cycle Field Development – A Comparative Study for Well Placement Allocation and Platform Capacity

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In the context of rising global energy demands that are aligned with sustainable energy supply, making informed decisions regarding investments has become increasingly complex. This complexity is particularly challenging in oil and gas management, where devising a production strategy and commencing field development pose challenges given the multitude of uncertain variables and extended timelines involved. Flexibility is key to address these uncertainties. Hence, the objective of this article is to evaluate the importance and advantages of considering the expected value of flexibility in the decision-making process to create a strategy able to deal with the risks imposed in the petroleum industry. Doing so, this article provides an examination of different approaches employed for the implementation of flexibility, considering the well placement allocations, final strategy selection, and platform capacity, thereby offering an informed perspective on this crucial aspect of reservoir strategic management.

The methodology for the construction of a flexible strategy employs theories in decision analysis combined with reservoir simulation models and optimization methods in a Bayesian probabilistic approach to access the expected value of the flexibility (EVoF). We present a structured technique to assess and select optimal strategies, specifically focusing on managing uncertainty in the initial stages of field development to identify potential platform capacities and drilling location strategies in the face of uncertainties related to reservoir characteristics, facility operations, and market conditions. To illustrate the results, we conduct a case study on an offshore benchmark field with Brazilian pre-salt features under WAG-CO2 recovery method, involving the complete reinjection of produced CO2 to mitigate greenhouse gas effects.

The results reveal that the initial strategy can highly impact the final net present value outcome and risk curves due to the first wells drilled. The results also indicate that increasing flexibility in the early stage of development could extract the best results related to financial return. Our study underscores the immense potential of integrating flexibility valuation and uncertainty quantification into the energy planning and policy-making process. It also highlights that the holistic integration between flexibility and reservoir simulation facilitates the identification of innovative investment strategies and enhances the decision-making process with the tools to navigate the complexities of uncertainty with greater confidence and adaptability.

This innovative approach offers a structured technique that not only addresses uncertainties in the subsurface reservoir and economic scenarios but also contributes to the identification of methodologies for investment management, enhancing the adaptability in the dynamic landscape of reservoir engineering.

Investigating Fail in Downhole Chemical Injection Valves (CIVs) – A Teardown Analysis

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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)

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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.

Incorporation of Conceptual Geological Model for Fracture Distribution in 3D Reservoir Modeling

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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 for an 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.

Estimating Rock Typing in Uncored Wells Using Machine Learning Techniques for Brazilian Pre-Salt Carbonate Reservoir

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Accurate rock typing in uncored wells is essential for enhancing reservoir models, particularly in complex geological formations like the Brazilian pre-salt carbonate reservoirs. This study explores the application of machine learning (ML) techniques to estimate rock types in uncored wells. The research leveraged core data from 11 cored wells to calculate the Rock Quality Index (RQI) and Flow Zone Indicator (FZI), identifying 14 distinct rock types through the Discrete Rock Typing (DRT) method, along with well log data such as gamma ray, density, neutron, permeability, and porosity. Various machine learning models, including Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Decision Tree, Gradient Boosting, Naive Bayes, XGBoost, and Random Forest were tested, where XGBoost achieved the highest accuracy of 73.3%. Applying XGBoost to all wells resulted in accuracy ranging from 0.6 to 0.91 and the model was subsequently used to estimate rock types in over 20 uncored wells to generate reservoir simulation models. This study highlights the efficacy of machine learning (particularly XGBoost) in addressing reservoir complexities and offering significant improvements in the understanding and development of carbonate reservoirs.

A numerical procedure to identify vugs and fractures in rock samples based on CT scan data

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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.