Real-Time Liquid Rate and Water Cut Prediction From the Electrical Submersible Pump Sensors Data Using Machine-Learning Algorithms.

This study presents a novel data-driven approach for calculating multiphase flow rates in electrical submersible pumped wells. Traditional methods for estimating flow rates at test separators fail to identify production trends and require additional costs for maintenance. In response, virtual flow metering (VFM) has emerged as an attractive research area in the oil and gas industry. This study introduces a robust workflow utilizing symbolic regression, extreme gradient boosted trees, and a deep learning model that includes a pipeline of convolutional neural network (CNN) layers and long short-term memory algorithm (LSTM) layers to predict liquid rate and water cut in real time based on pump sensors' data. The novelty of this approach lies in offering a cost-effective and accurate alternative to the usage of multiphase physical flow meters and production testing. Additionally, the study provides insights into the potential of data-driven methods for VFM in electrical submersible pumped wells, highlighting the effectiveness of the proposed approach. Overall, this study contributes to the field by introducing a new, data-driven method for accurately predicting multiphase flow rates in real time, thereby providing a valuable tool for monitoring and optimizing production processes in the oil and gas industry.

Machine Learning Approach for Predictive Maintenance of the Electrical Submersible Pumps (ESPs).

Electrical submersible pumps (ESPs) are considered the second-most widely used artificial lift method in the petroleum industry. As with any pumping artificial lift method, ESPs exhibit failures. The maintenance of ESPs expends a lot of resources, and manpower and is usually triggered and accompanied by the reactive process monitoring of multivariate sensor data. This paper presents a methodology to deploy the principal component analysis and extreme gradient boosting trees (XGBoosting) in predictive maintenance in order to analyze real-time sensor data to predict failures in ESPs. The system contributes to an efficiency increase by reducing the time required to dismantle the pumping system, inspect it, and perform failure analysis. This objective is achieved by applying the principal component analysis as an unsupervised technique; then, its output is pipelined with an XGBoosting model for further prediction of the system status. In comparison to traditional approaches that have been utilized for the diagnosis of ESPs, the proposed model is able to identify deeper functional relationships and longer-term trends inferred from historical data. The novel workflow with the predictive model can provide signals 7 days before the actual failure event, with an F1-score more than 0.71 on the test set. Increasing production efficiencies through the proactive identification of failure events and the avoidance of deferment losses can be accomplished by means of the real-time alarming system presented in this work.

Coupling Microfluidics Data with Core Flooding Experiments to Understand Sulfonated/Polymer Water Injection.

The injection of sulfonated-modified water could be an attractive application as it results in the formation of a mechanically rigid oil-water interface, and hence, possible higher oil recovery in combination with polymer. Therefore, detailed experimental investigation and fluid-flow analysis into porous media are required to understand the possible recovery mechanisms taking place. This paper evaluates the potential influence of low-salt/sulfate-modified water injection in oil recovery using a cross-analyzed approach of coupled microfluidics data and core flooding experiments. Fluid characterization was achieved by detailed rheological characterization focusing on steady shear and in-situ viscosity. Moreover, single and two-phase micromodels and core floods experiments helped to define the behavior of different fluids. Overall, coupling microfluidics, with core flooding experiments, confirmed that fluid-fluid interfacial interaction and wettability alteration are both the key recovery mechanisms for modified-water/low-salt. Finally, a combination of sulfate-modified/low-salinity water, with polymer flood can lead to ~6% extra oil, compared to the combination of polymer flood with synthetic seawater (SSW). The results present an excellent way to make use of micromodels and core experiments as a supporting tool for EOR processes evaluations, assessing fluid-fluid and rock-fluid interactions.

Assessment of Supercritical CO2 Extraction as a Method for Plastic Waste Decontamination.

Due to the lack of advanced methods to clean plastic waste from organic contaminants, this study aimed at evaluating supercritical extraction as a decontamination method. Oil-adhesive high-density polyethylene (HD-PE) oil containers were subjected to supercritical extraction using supercritical carbon dioxide. The extraction was conducted at 300 bar, applying various temperatures (i.e., 70, 80 and 90 °C). The study assessed the impact of temperature on the decontamination efficiency. The variation in the samples' quality was first analyzed using near infrared (NIR) spectroscopy. An analysis of the content of polycyclic aromatic hydrocarbons (PAHs) was followed. Samples treated at 70 and 80 °C showed higher extraction efficiencies, in spite of the lower extraction temperatures. The NIR analysis showed that the plastic specimens did not experience degradation by the supercritical decontamination method. Moreover, the NIR spectra of the extracted oil showed the presence of a wide range of compounds, some of which are hazardous. This has been confirmed by a GC-MS analysis of the extracted oil. Based on the provided assessment, the quality of the decontaminated HD-PE plastic samples-from a contamination point of view-is enhanced in comparison to untreated samples. The level of PAHs contamination decreased to be within the allowed limits defined by the REACH regulation, and also met the specifications of the German Product Safety Committee. This study proved the effectiveness of the supercritical extraction using CO2 in extracting organic contaminants from plastics, while maintaining their quality.

Significant solubility of carbon dioxide in Soluplus® facilitates impregnation of ibuprofen using supercritical fluid technology.

Treatment of Soluplus® with supercritical carbon dioxide allows promising applications in preparing dispersions of amorphous solids. Several characterization techniques were employed to reveal this effect, including CO2 gas sorption under high pressure and physicochemical characterizations techniques. A gravimetric method was used to determine the solubility of carbon dioxide in the polymer at elevated pressure. The following physicochemical characterizations were used: thermal analysis, X-ray diffraction, Fourier transform, infrared spectroscopy and scanning electron microscopy. Drug loading of the polymer with ibuprofen as a model drug was also investigated. The proposed treatment with supercritical carbon dioxide allows to prepare solid solutions of Soluplus® in less than two hours at temperatures that do not exceed 45 °C, which is a great advantage to be used for thermolabile drugs. The advantages of using this technology for Soluplus® formulations lies behind the high sorption capability of carbon dioxide inside the polymer. This will ensure rapid diffusion of the dissolved/dispersed drug inside the polymer under process conditions and rapid precipitation of the drug in the amorphous form during depressurization accompanied by foaming of the polymer.