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.
Month: October 2024
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.