BR 
EN Petroleum is a complex mixture of hydrocarbons varying from saturate, resins, asphaltenes and aromatics. Wax, also known as paraffin, normally refers to the range of n-alkanes in the crude oil with carbon numbers higher than 18. The waxes present in crude oils are divided into two categories: macrocrystalline wax, tends to form large plate-like crystals and, microcrystalline, tends to form solids with a lower degree of crystallinity. It has not been possible to establish a pattern that links the tendency of type of solvent and type of paraffin, due to the complexity of the wax crystal morphology. The objective of this research is to study the thermal properties of macro and microcrystalline paraffin under solubilization of several solvents through experimental techniques of DSC (Differential Scanning Calorimetry) and CPM (Cross Polarized Microscopy). The interaction of each type of n-paraffin in different solvents causes distinct influences on the crystal morphology and, consequently, influences on their thermal behavior. This study is relevant since elucidating this behavior helps to optimize deposition models and thus, define more effective mitigation resources in the problem of wax deposition.
Autor: João Lucas Braga Da Silva
Study of the influence of commercial emulsion breaker on the water/ oil interfacial properties
The aim of this research project is to study how commercial emulsion breakers affect the interfacial properties of water/ oil systems. For that, the studies will be focused on the properties of the interface such as interfacial tension, dilational and shear viscoelastic behavior. The characteristics of the interfacial film will be analyzed along with the results from kinetic studies of phase separation with emulsion breakers. The tension and rheological studies of the interface will be performed by using a Spinning drop Tensiometer (Dataphysics SVT-20N) and an interfacial rheology accessory for a rotational rheometer (Haake, Mars III). Each rheological system aims to getting different properties of the interface. The dilational technique provides information about the response of the system when the interfacial area is changed, i.e., the kinetics in which the system acts to bring the interface back to the equilibrium condition. On the other side, in the shear technique the interfacial area is kept constant and the mechanical properties of the interface are probed. Emulsion breakers can act by affecting the viscoelastic properties of the interfacial film built up of crude oil components with surface active properties, such as asphaltenes, resins and naphtenic acids. Thus, the focus of this research proposal is to investigate how emulsion breakers acts on the formation and establishment of the interfacial film and the relationship between the film physic-chemical properties and the efficiency of the emulsion breaker, looking for a microscopic understanding of the phase separation phenomenon of water/ oil emulsions induced by emulsion breakers. (AU)
Using an electrical submersible pump mechanical vibration and a Fourier convolution neural network to estimate the water cut in two-phase liquid-liquid flows
In oil exploitation, water in the production is common and increases with the field life. For oil
production to be economically feasible, artificial lifting techniques may be required. In this context,
electrical submersible pumps have been widely used to provide energy to the fluid for decades.
Despite the recent development on ESP vibration signal usage, most studies use the vibration signal
for fault diagnosis algorithm development. On the other hand, the water cut is essential to obtain
several volumetric variables, such as the flow rate of each phase, the effective viscosity,
and the pressure drop. This study aims to obtain an artificial neural network that correlates the ESP
mechanical vibrations with the water-liquid ratio of an oil-water two-phase flow. The artificial neural
network uses a Fourier convolution neural network architecture, where the convolutions are
performed in the frequency domain rather than the time domain. The experimental data obtained
resulted in a non-uniform dataset on different spatial projection, which significantly affected the
data-driven technique performance. Then, by filtering out spaced experimental points and using only
the samples inside the sample convex hull, it was possible to successfully obtain a regression
between the ESP vibration spectrum and the water cut. The results showed that data filtering and
selection were crucial for the artificial neural network performance.
A Droplet-Based Image Velocimetry Technique for the Measurement of Liquid Velocity Fields in Two-Phase Water-Oil Dispersion Flows
The present work describes a measurement technique to estimate the continuous liquid velocity fields in twophase water-oil dispersion flows. A transparent pump prototype made of acrylic was firstly developed and installed to enable the use of flow visualization and optical measurement techniques. Then, in the experiments, water droplets were injected into the impeller channels of the centrifugal pump, where a mineral oil with a viscosity of µo = 18.0 cP was used as the continuous phase. The two-phase water-oil dispersion flow was then filmed with a high-speed camera, and the water droplets were black-dyed for a better contrast with the white background. When injecting the water drops, breakage events were frequently observed due to turbulence and shear effects, resulting in the birth of small droplets with a size in the range from 100 µm to 500 µm. The occurrence of small water droplets in combination with the viscous continuous oil phase meant that those droplets could be assumed as tracer particles from the continuous phase. Therefore, by computing the
small water droplet velocities, it is possible to estimate the velocity field of the continuous oil phase within an acceptable error margin. This is the main idea of the technique presented in this work, which does not require the addition of intrusive tracer particles, and thus can be seen as a cheap and simple alternative to PIV in two-phase dispersions with continuous viscous phases. After a series of image processing steps, the small water droplets in the range from 100 µm to 500 µm are identified, and the PTV technique computes their instantaneous velocity. In order to assess the method capabilities, the PTV ensemble-averaged liquid flow rate is compared against experimental values from a Coriolis flowmeter installed in the experimental setup. The technique is then applied to study the flow within a pump impeller, resulting in similar flow patterns found in the literature for studies using LDV and PIV studies.
Development and assessment of a particle tracking velocimetry (PTV) measurement technique for the experimental investigation of oil drops behaviour in dispersed oil–water two-phase flow within a centrifugal pump impeller
The objective of the current work is to present the development of a Particle Tracking Velocimetry (PTV) algorithm for the analysis of oil drops behaviour in two-phase oil–water dispersions within a centrifugal pump impeller. The drop tracking was realized through high-speed camera images in a transparent pump prototype, which enabled the visualization of oil drops dispersed in water in all the impeller channels. The PTV algorithm is based on deep-learning techniques for image processing. The drops are detected by a combined U-Net and Convolutional Neural Network (CNN) method, with the former generating a binary image and the latter detecting valid oil drop contours. After detection, the Labelled Object Velocimetry (LOV) is adopted to calculate the instantaneous oil drop velocity. A synthetic image generator based on a Generative Adversarial Network (GAN) is then developed to assess the results from the U-Net, CNN, and LOV models. Additional validation studies are performed using the results from Perissinotto et al. (2019a). The results reveal that the presented deep-learning PTV algorithm is robust and provides consistent and reliable data for the dispersed oil phase in two-phase oil–water flows.
Utilizing integrated artificial intelligence for characterizing mineralogy and facies in a pre-salt carbonate reservoir, Santos Basin, Brazil, using cores, wireline logs, and multi-mineral petrophysical evaluation
In complex carbonate reservoirs, it is crucial to understand the connections between reservoir compositions (minerals, facies, and properties). Conventionally, core samples have been used to measure reservoir parameters and identify minerals and facies. However, core samples are limited to certain wells. Therefore, additional techniques are necessary to overcome this limitation comprehensively. This study aims to identify key mineralogical and facies components of the Barra Velha Formation (BVR) and their relation to reservoir parameters. Dolomite, calcite, quartz, and clay minerals were commonly found using X-ray Diffraction (XRD). By employing multi-mineral (MM) petrophysical evaluations, we accurately recreated mineral quantities from XRD and petrophysical properties from core analysis to ensure reliability. Replications of inputs well logs and the mineralogical volume from spectroscopic (ECS) were used as reliability techniques for validating the MM. A total of 47 wells were analyzed using those methods. In this study, the classification of facies was accomplished through the selection of three prominent supervised artificial intelligence techniques, among which SOM, a widely employed method for facies estimation, was included. Additionally, the ensemble methods of Random Forest and XGBoost were adopted due to their recognized efficacy in handling tabular data and their track record of success in machine learning and artificial intelligence competitions. Remarkably, the performance evaluation revealed that Random Forest and XGBoost algorithms outperformed SOM, yielding the most favorable outcomes in this context. An integrated analysis of mineralogical and facies results was conducted, incorporating production data and special profiles such as nuclear magnetic resonance (NMR) and Wellbore Image (WBI) to identify vug-containing areas. The dolomitic facies exhibited favorable reservoir qualities, influenced by diagenetic processes represented by vuggy porosity, which enhanced permeability. Shrubstones, spherulites, and reworked facies showed superior petrophysical qualities and were connected with productive regions, leading to elevated dolomite concentrations, and vuggy abundance. The study highlights two major innovations: the use of mineralogical volume from multi-mineral assessments as inputs for AI-based property estimation to improve facies estimates, and the discovery of relationships between facies, minerals, and reservoir properties, compared to production data. This understanding allows for more accurate static model creation, optimal production interval selection, improved hydrocarbon recovery, and better specification of stimulation processes.
The Golem: A general data-driven model for oil & gas forecasting based on recurrent neural networks
Oil & gas forecasting is one of the most critical issues in reservoir management. Physics-based simulations are the most common models used for production forecasts in oilfields. Previous works based on Machine Learning (ML) developed models focused on the oil rate as the unique target variable, forecasting by one-day output, and just one class of reservoir (synthetic or actual). This work introduces a general data-driven model based on Recurrent Neural Networks to forecast an adaptive sequence of timestamps for the complete production rates (oil, gas, and water), and we also included the well-bore pressure as a target variable, for both classes of reservoirs as actual as synthetic. The first dataset was obtained from the synthetic benchmark UNISIM-II-H, which simulates a carbonate reservoir in the Brazilian pre-salt; the second dataset is from an actual reservoir, the Volve oilfield, a decommissioned reservoir in the Norwegian North Sea. The forecasting is calculated using an input sequence of daily values from the historical record of the production rates and the pressure; the output is also a set of the values to the next sequence of days for one selected production variable (oil, gas, water, or pressure). The size of both input and output sets is adaptive and its adjustment depends on the dataset size and the production time. We built the model and compared it between the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) implementations. We tuned the architectural parameters of the model, the input size of historical records, and the output forecasting days. We performed the training/testing procedures with several sizes for the training dataset from the target well-bore and tested with the remaining data to evaluate the model stability. We adopted the Symmetric Mean Absolute Percentage Error (SMAPE) and the coefficient of determination r-square (R2) metrics to compare our forecasting values to the production rates and the pressure, most of the results for both synthetic and actual oilfields exhibited that the model can follow an accurate trend of the production rates and the pressure, and the output values are approximated. Forecasting values from the designed model exhibited closer values when compared to the expected data from the well-bores in most of the experiments, some cases exhibited a SMAPE lower than 2 and R2 up to 0.99. The model can learn the behavior of each production variable in the training stage and the forecasting output can be adapted for a set of several timestamps.
Phase inversion identification in Electrical Submersible Pumps using mechanical vibrations
Electric Submersible Pumps (ESPs) are multistage centrifugal pumps used in the artificial lift and transport of multiphase fluid mixtures. The flow regime is a liquid–liquid flow when the fluids correspond to two non-miscible fluids. Liquid–liquid flow is a mixture with a continuous and dispersed phase. As the amount of fluid in the dispersed phase increases, the dispersed phase suddenly becomes continuous and vice-versa. This transition phenomenon is called phase inversion. The flow regimes in oil–water mixtures are oil-in-water (o/w) and water-in-oil (w/o) flow regimes. This work demonstrates a correlation between the flow regime and the flow-induced vibration (FIV) in ESP operating with an oil–water mixture. This research proposes a novelty method to flow regime identification based on the Root Mean Square (RMS) of the vibration acceleration of the Fast Fourier Transform (FFT) signal.
The experimental setup consists of an 8-stage Electrical Submersible Pump (ESP) and a vibration acquisition system with six accelerometers uniformly distributed along the ESP. The experimental procedure consists of changing the water cut (percentage of water) from the oil flow regime to the water flow regime, maintaining stable ESP rotational speed, the total flow rate, and the oil viscosity. For each water cut, mechanical vibration is collected. The operational conditions consider 30, 40, and 50 Hz rotational speeds and viscosities between 70 and 210 cP.
Frequency domain analysis involves studying FFT between 0 and 5000 Hz, considering different water cuts and frequency ranges. Statistical features – mean, variance, geometric mean harmonic mean, and RMS – were extracted from the FFT for each frequency range. Results showed a strong correlation between the RMS of FFT and the phase inversion phenomena considering the rotational speed. A logistic regression model was employed to establish a transition boundary between oil-in-water and water-in-oil using 10% of the data. The model successfully separated at least 95.67% of the remaining data in the least favorable scenario.
Paleokarst features in the Aptian carbonates of the Barra Velha Formation, Santos Basin, Brazil
Seismic data, borehole image logs, and conventional well logs were used to investigate the distribution and characteristics of paleokarst features in the Aptian carbonates of the Barra Velha Formation in a pilot area of the Santos Basin, Brazil. Multiple seismic attributes were used to enhance details on the seismic data and highlight key seismic parameters including strata deformation and geometry, continuity of seismic events, and fault patterns. The study found that karst structures are controlled by faults and fractures along structural highs, which served as a conduit for the flow of dynamic fluids that dissolved the carbonate materials. Several closed, circular depressions and bright spots identified in the northeastern portion of the study area represent possible sinkhole structures. Epigenic and hypogenic processes due to the action of meteoric water, hydrothermal activity, and intra-formation acidity along regional unconformities in the Barremian-Aptian may have been responsible for the dissolution. Limitations of this study are related to the dificulty of integrating multiple datasets with various scales. However, the higher confidence for the occurrence of the karst features is provided by borehole images at the sub-seismic scale. The findings of this study hold significant relevance for the strategic planning of energy development and carbon sequestration initiatives in the Brazilian continental margins, thereby aiding in informed decision-making.