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Interests in the use of chemometric and data science methods for laboratory techniques have grown rapidly over the last 10 years, for the reason that they are cheaper and faster than traditional analytical methods of material testing.This study uses 888 rock samples collected from the exploration and production (E&P) sector of the oil industry. Based on the Fourier-transform infrared (FT-IR) spectra of these rock samples their solubility predictions have been developed and investigated with nine methods including both linear and non-linear ones. Two of these methods such as Partial Least Squares Regression (PLSR) and Support Vector Regression (SVR) are available in a commercial software package and the other seven methods, Extreme Gradient Boosting (XGBoost), Ridge Regression (RR), k-nearest neighbours (k-NN), Decision Tree (DT), Multilayer Perceptron (MLP), Support Vector Regression (SVR), Artificial Neural Network (ANN) with TensorFlow (TF), were coded by the authors based either on commercial applications or open source libraries. The investigation starts with spectral data pre-processing carried out by standard normal variate (SNV), baseline correction and feature selection methods creating the feature set for all machine learning (ML) applications.The accuracy of predictions has been evaluated with mean squared error as a performance metric for each investigated method. The comparisons of predicted values to real data of test samples have shown that mineral solubility in acids can be well predicted in the range of the uncertainties of real laboratory measurements, therefore it can be used to improve the response time of these investigations and reduce the risk in industrial applications. In those cases, where the unknown samples have got some out of the range features, the limitations in the accuracy of predictions have become clear. We have also identified the limitations in the methodology and planned steps to further improve the prediction capabilities. The identified constraint of samples' multitude further emphasizes the need for database building efforts, so that the real potential in big data and machine learning can be realized. KEYWORDSacid solubility, artificial neural network (ANN) TensorFlow, Extreme Gradient Boosting (XGBoost), mid-infrared spectra, multivariate data analysis (MVDA)

The present research presents a framework that supports the development and operation of machine-learning (ML) algorithms to develop, maintain and manage the whole lifecycle of modeling software sensors related to complex chemical processes. Our motivation is to take advantage of ML and edge computing and offer innovative solutions to the chemical industry for difficult-to-measure laboratory variables. The purpose of software sensor models is to continuously forecast the quality of products to achieve effective quality control, maintain the stable production condition of plants, and support efficient, environmentally friendly, and harmless laboratory work. As a result of the literature review, quite a few ML models have been developed in recent years that support the quality assurance of different types of materials. However, the problems of continuous operation, maintenance and version control of these models have not yet been solved. The method uses ML algorithms and takes advantage of cloud services in an enterprise environment. Industrial 4.0 devices such as the Internet of Things (IoT), edge computing, cloud computing, ML, and artificial intelligence (AI) are core techniques. The article outlines an information system structure and the related methodology based on data from a quality-assurance laboratory. During the development, we encountered several challenges resulting from the continuous development of ML models and the tuning of their parameters. The article discusses the development, version control, validation, lifecycle, and maintenance of ML models and a case study. The developed framework can continuously monitor the performance of the models and increase the amount of data that make up the models. As a result, the most accurate, data-driven and up-to-date models are always available to quality-assurance engineers with this solution.

Central element of MOL Hungarian Oil and Gas Plc. (MOL) US strategy is to increase the hydrocarbon production at Hungarian oil and gas fields using technologies that are more efficient. The main goals of this activity are to increase the recovery factor in fields depleted with extensive water flooding, improving efficiency of recovery technologies. For this purpose, new materials and technologies should be developed and applied at both Hungarian and foreign matured oil fields. That is the biggest challenge of the research and development (R&D) activity of the MOL Upstream. The R&D project began more than ten years ago to meet these challenges and increase the oil recovery factor of the Algyo field, which is the largest Hungarian oil field. This paper describes how a countless number of surfactants, co-surfactants and their mixtures were synthetized, developed and tested in the laboratory to achieve the objective, developing a combined surfactant-polymer (SP) flooding technology. The most important properties of these complex fluids were the thermal stability at reservoir conditions (98°C and 170 bar), the colloid chemical stability in electrolyte medium (formation water) and the compatibility with reservoir rock and pore filling fluids. The primary findings of this job show that several surfactants were effective at high temperature; low salinity reservoir conditions and have good solubilisation and displacement effect and low interfacial tension and low reversible adsorption on reservoir rock. Synergetic effect was observed between surfactants and polymers therefore surfactant-polymer mixtures were produced and tested in core flooding tests. Based on numerous displacement tests on reservoir core plugs it can be stated that the calculated recovery factor was 20-25% using the developed SP mixtures. The successful laboratory displacement tests were also reproduced by numerical simulation on numerical core samples as well as the injectivity test on the new 3D reservoir model that was carried out to see the effect of developed SP mixture under real reservoir conditions. This paper will present the results of several years of research and development work for SP formulation targeting SP flooding in high pressure and high temperature reservoir. The field implementation through an injectivity test will also be presented demonstrating that injection of 2,000 m3 SP solution has huge effect on oil production even 3 years later. Based on the outstanding field results a SP flooding pilot was started in 2016.