EN 
BR Electrical submersible pumps (ESPs) are prevalently utilized as artificial lift systems in the oil and gas industry. These pumps frequently encounter multiphase flows comprising a complex mixture of hydrocarbons, water, and sediments. Such mixtures lead to the formation of emulsions, characterized by an effective viscosity distinct from that of the individual phases. Traditional multiphase flow meters, employed to assess these conditions, are burdened by high operational costs and susceptibility to degradation. To this end, this study introduces a physics-informed neural network (PINN) model designed to indirectly estimate the fluid properties, dynamic states, and crucial parameters of an ESP system. A comprehensive structural and practical identifiability analysis was performed to delineate the subset of parameters that can be reliably estimated through the use of intake and discharge pressure measurements from the pump. The efficacy of the PINN model was validated by estimating the unknown states and parameters using these pressure measurements as input data. Furthermore, the performance of the PINN model was benchmarked against the particle filter method utilizing both simulated and experimental data across varying water content scenarios. The comparative analysis suggests that the PINN model holds significant potential as a viable alternative to conventional multiphase flow meters, offering a promising avenue for enhancing operational efficiency and reducing costs in ESP applications.
Author: João Lucas Braga Da Silva
Investigação da Compatibilidade entre Fluidos da Linha de Injeção Química de Desemulsificante em Poço de Petróleo
The chemical injection (CI) lines in oil-producing wells have been plagued by issues related to clogging, hindering the injection of demulsifiers into the oil reservoir, which directly impacts oil productivity. This problem might require stoppages in the oil production for cleaning and/or pigging the line, and even replace downhole equipment (chemical injection valves). Given this scenario, the present study aims to investigate the compatibility of fluids present in the chemical injection line. If these chemicals are not compatible and result in solid formation, it could lead to pipe blockages. To assess this, monoethylene glycol (MEG), commonly used as a flushing fluid and hydrate inhibitor, and an ethoxylated polymeric surfactant, used as a demulsifier, were mixed to observe any physical change in the solution. The tests were conducted at room temperature (30 °C) and 60 °C, for up to 72 h, with visual monitoring during this period. In addition, rheological tests were carried out on pure fluids and their mixtures to evaluate if there were viscoelastic changes. These studies made it possible to detect a whitish gel at the bottom of the test tube formed through contact between the MEG and the demulsifier (phase separation) at both temperatures. This allows us to conclude that physical changes occurred in the mixture (MEG + demulsifier), forming a higher viscosity gel. Importantly, preventing this gel formation could possibly prevent clogging in the CI lines, as the gel could adhere to solid contaminants and contribute to blockages.
Better stopping through cross validation in an iterative ensemble smoother: A perspective from supervised machine learning
Iterative ensemble smoothers (IES) are among the popular reservoir data assimilation (RDA) 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 RDA 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 computational costs while achieving better RDA 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 the noticed similarities between RDA and SML problems, some fundamental differences exist, making it fail to work well if one directly extends a vanilla CV procedure from SML to RDA. To tackle this identified challenge, we design an efficient CV procedure tailored for RDA 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 investigation indicates that the IES-CV algorithm achieves promising RDA 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 RDA performance. Beyond these numerical findings, it is also our hope that the current work may help improve the best practices of applying IES to RDA problems, taking advantage of the effective, CV-based stopping criterion.
Selection of well opening schedules in pre-salt reservoirs using WAG-CO2
In field development projects involving many wells, the limitation of available rigs can increase the need for investments. In our literature research about the definition of well opening schedules, we found some proposals but could not find studies that deep dive into this topic, nor any that propose an approach that could be applied to the Brazilian pre-salt fields. Therefore, this research focuses on evaluating the influence in the NPV of well opening schedule of a project using WAG-CO2, inspired in pre-salt fields, and we propose a simple procedure to specify the well opening schedule without having to perform many simulations or complex analysis. We used the UNISIM-II-D-BO benchmark (synthetic simulation model with characteristics of a Brazilian pre-salt field) and a well configuration proposed in a previous study, with 11 producers and 10 injectors. First, we calculated the distance between the producer and injector wells and evaluated the well economic indicator (WEI) of each well by opening all at the same time at the beginning of the production schedule. We then evaluated 252 combinations of those injector wells, where five of them were injecting water and the other five were injecting gas, from where we defined three reference alternatives to check the repeatability of the results. With all these data, we tested predefined schedules: (1) with the 1st injector well injecting only gas or participating in the WAG cycle; (2) the producers’ opening sequence based on their WEI; (3) the injectors’ opening based on their WEI or distance to producers; and (4) testing the relationship between producers and injectors (number of producers to open before the injectors), making sure that all the gas could be reinjected. For comparison purposes, we then run an optimization algorithm to select the schedule, using 2,660 simulations. At the end, we verified that the difference between the optimization with the algorithm (demanding thousands of simulations runs) and the proposed schedules (demanding few simulations) in the NPV was about 2%. Thus, to speed-up decision making, it is possible to have an opening schedule with good performance considering a simple procedure, as follows: 1st injector well collaborating with the WAG; producer wells opening from the highest to the lowest WEI; injector wells opening from the closest to the furthest distance to the producers; and the relationship between producers and injectors has to be investigated, not only aiming to find the best economic return, but also because of the environmental restriction (the need to reinject all the produced gas). This procedure reduces the number of simulations that must be performed if an engineer decides to use an optimization technique.
IMMISCIBLE VISCOUS FINGERING MODELING OF TERTIARY POLYMER FLOODING BASED ON REAL CASE OF HEAVY OIL RESERVOIR MODEL
In immiscible displacements, lower viscosity injected fluids with higher mobility than crude oil can create viscous fingers, affecting displacement efficiency. The Buckley–Leverett approach for relative permeabilities (kr) may not represent accurately 2D features like increased water saturation in viscous fingering. Based on the physics issue, this work applies Sorbie’s 4-Steps methodology to a 3D simulation of an offshore heavy oil reservoir focusing on waterflooding and tertiary polymer flooding, assessing their impact on oil production forecasts. It also explores the application of this methodology to coarse grid simulation models, employing pseudo kr functions by data assimilation. During tertiary polymer injection, two processes were identified in oil displacement: viscous crossflow mechanism and oil bank mobilization by a second finger. This combination resulted in earlier and increased oil production. For both strategies, refining the grid increased simulation runtime from minutes to days compared to coarse grids, making it impractical for intensive processes. From data assimilation, the best solution with matched field indicators reduced runtime from days to minutes. This study expanded the 4-Steps methodology for 3D reservoir simulation, proposing kr as uncertainties. Data assimilation enhances the methodology, generating pseudo kr for coarser grid simulations, reducing computational costs, and capturing small-scale phenomena.
Integration between experimental investigation and numerical simulation of alkaline surfactant foam flooding in carbonate reservoirs
In Brazil, pre-salt carbonate reservoirs are largely responsible for the current increase in oil production. However, due to its peculiar characteristics, increasing oil recovery by water injection is not enough. Therefore, we seek to evaluate the recovery potential using chemical methods (cEOR). Among these, the Alkali Surfactant Foam (ASF) method appears with high potential, a variant of Alkali Surfactant Polymers (ASP) without the problems presented by it. Therefore, this work presents an innovative methodology, which seeks to evaluate the potential for recovery with ASF in carbonate reservoirs by integrating experimental characterization and recovery prediction using reservoir simulation. For this, phase behavior and adsorption analyses were carried out. The experimental results provided key parameters for the simulation, such as optimal salinity, surfactant adsorption, foam mobility reduction factors. The results are from two case studies of AS and ASF flooding, using a section of UNISIM-II benchmark, using a one-quarter of five-spot model. Having the modelling for these cEOR methods defined, an optimization process for each method was applied, allowing a reliable comparison among the methods and over a base case of water injection, seeking the maximization of the net present value (NPV). As a result, in the experimental part, a low interfacial tension (IFT) value of 0.003 mN/m was achieved with a surfactant adsorption reduction of 17.9% for an optimal setting among brine (NaCl), alkali (NaBO2.4H2O), and surfactant (BIO-TERGE AS 40). In the reservoir simulation part, using a fast genetic algorithm in the optimization process, a NPV of US$ 14.43 million higher than the base case (water injection) and a 4.5% increase in cumulative oil production for the ASF injection case were obtained. Considering the analyses of production curves (cumulative oil production and oil rate) and oil saturation maps, a considerable oil production anticipation was observed, which was the main reason for NPV improvement, proving the high potential for application of the ASF method in carbonate reservoirs.
Model-Based Petroleum Field Management in Three Stages: Life-Cycle, Short-Term, and Real-Time
The objective of this work is to present a new practical methodology to manage petroleum fields considering three stages (life-cycle, short-term, and real-time) that can run alongside different model fidelities and characteristics. The model-based field management process follows the general methodology proposed by Schiozer et al. (2019) with four activities: (1) fit-for-purpose models construction, (2) data assimilation for uncertainty reduction, (3) life-cycle production optimization and (4) short-term optimization for real-time implementation. The selection of the production strategy for field management comprehends the last two activities. Life-cycle optimization is the first stage of the process and generates control setpoints for short-term analysis. Short-term optimization is then used to improve the quality of the solutions considering the control parameters of the next cycle (considering a closed-loop procedure). Real-time solution is then implemented considering operational disturbances from real operations. The methodology was applied to a benchmark case (UNISIM-IV-2026) which is a case based on a typical carbonate field from the Brazilian Pre-salt, with light oil and submitted to Water-Alternate-Gas injection with CO2 (WAG-CO2). The results show that the methodology is applicable to real and complex fields. As the three stages can run simultaneously, one can (1) use different model fidelities to improve the quality of the solutions and (2) use model-based solutions for real-time implementation. Life-cycle optimization using complex simulation models and long-term objectives can run in the background to generate control setpoints for short-term analysis in which lower fidelity models and simplified solutions can be used for the control and field revitalization parameters of each closed-loop cycle. Real-time solutions can be implemented considering operational problems and disturbances. This work presents a novel procedure to integrate three stages for production optimization that can run in parallel, allowing the integration of life-cycle and real-time solutions. The methodology (1) allows the use of complex reservoir simulation models from the life-cycle production strategy optimization, (2) focuses short-term control parameters that improve the quality of the short-term solution, and (3) guides real-time implementation, so it can be the basis to a digital field management.
Numerical Study on the Impact of Advanced Phenomena in a Fractured Carbonate Reservoir Subjected to WAG-CO2 Injection
Advanced phenomena related to water-alternating-gas (WAG) injection are usually neglected in numerical simulations. This work evaluates the impact of different physical phenomena on field indicators, considering a typical pre-salt carbonate reservoir (UNISIM-II-D-CO, a dual-por dual-perm compositional case) subjected to WAG-CO2 injection. Additionally, the computational cost incurred by each of these phenomena is evaluated, since it represents a great challenge in optimization and probabilistic studies. The following phenomena are evaluated considering a nominal base case: (i) matrix-fracture transfer calculation, (ii) relative permeability hysteresis, (iii) CO2 and CH4 solubilities in aqueous phase, (iv) diffusion, (v) numerical dispersion control models, and (vi) velocity-dependent dispersion. CO2 and CH4 solubilities in the aqueous phase, as well as molecular diffusion, did not have a significant impact on field indicators, but they increased simulation runtime more than two times. Matrix-fracture transfer modeling was the most impactful factor, followed by hysteresis and velocity-dependent dispersion. Therefore, the impact of these phenomena was also investigated in a probabilistic approach, considering an ensemble of 197 geostatistical scenarios under uncertainty. Risk curves revealed that the advanced matrix-fracture transfer models improve sweep efficiency. This effect is mainly due to gravity force which acts as a driving mechanism for the oil moving from the matrix to fractures. The capillary effect, in turn, was small compared to gravity. The impact of dispersion and hysteresis on risk curves were smaller than the effect of matrix-fracture transfer modelling. However, these phenomena are particularly interesting in UNISIM-II-D-CO due to the presence of Super-K facies. Hysteresis, when applied to low and high permeability layers, reduced gas mobility and, consequently, the gas produced, contributing to the NPV for most models under uncertainty. On the other hand, the velocity-dependent dispersion mainly affected fluid flows in the regions adjacent to Super-K layers, promoting better oil recovery. The inclusion of advanced phenomena related to WAG-CO2 injection can hold importance when modeling fractured carbonate fields, like those found in the Pre-Salt in Brazil. Nevertheless, computational costs might make their inclusion impractical in full-field simulation models employed for optimization and probabilistic studies. In such cases, it is recommended to assess low-fidelity models or alternatives to accelerate simulations, focusing mainly on the most impactful phenomena related to WAG-CO2 injection.
MODEL-BASED ANALYSIS TO EVALUATE THE EFFECT OF POLYMER PROPERTIES AND PHYSICAL PHENOMENA ON POLYMER FLOODING OPERATIONS
Polymer flooding, known for enhancing heavy oil displacement, may encounter efficiency-limiting physical phenomena. This work assesses the impact of key factors like viscosity, shear rate, and adsorption in field applications using a model-based approach on the EPIC001 case, a real offshore Brazilian heavy oil reservoir. In simulation results, increasing the displacing fluid viscosity using apparent or zero-shear functions, oil production and economic returns show improvements over waterflooding. The shear rate effect slightly increases oil production and enhances injectivity loss due to shear-thinning in polymer flow. However, modeling it increases computation costs, as it extends simulation run time from minutes to days, making it impractical for intensive processes like production optimization. An analysis of the method’s effectiveness shows that it varies based on the adsorption level considered. At its highest value, even with a higher oil recovery factor, the economic return was lower than using waterflooding. Combining shear rate and adsorption has a minimal impact on field indicators when compared to adsorption alone. This work enhances the comprehension of physical phenomena and non-Newtonian behavior in tertiary polymer flooding on heavy oil reservoirs and its impact on the production forecast. It also highlights important considerations for modeling-based procedures.