Gas Industry Research Articles

An Intelligent Kick Detection Model for Large-Hole Ultra-Deep Wells in the Sichuan Basin

The Sichuan Basin has abundant deep and ultra-deep natural gas resources, making it a primary target for exploration and the development of China’s oil and gas industry. However, during the drilling of ultra-deep wells in the Sichuan Basin, complex geological conditions frequently lead to gas kicks, posing significant challenges to well control and safety. Compared to traditional kick detection methods, artificial intelligence technology can improve the accuracy and timeliness of kick detection. However, there are limited real-world kick data available from drilling operations, and the datasets are extremely imbalanced, making it difficult to train intelligent models with sufficient accuracy and generalization capabilities. To address this issue, this paper proposes a kick data augmentation method based on a time-series generative adversarial network (TimeGAN). This method generates synthetic kick samples from real datasets and then employs a long short-term memory (LSTM) neural network to extract multivariate time-series features of surface drilling parameters. A multilayer perceptron (MLP) network is used for data classification tasks, constructing an intelligent kick detection model. Using real drilling data from ultra-deep wells in the SY block of the Sichuan Basin, the effects of k-fold cross-validation, data dimensionality, various imbalanced data handling techniques, and the sample imbalance ratio on the model’s kick detection performance are analyzed. Ablation experiments are also conducted to assess the contribution of each module in identifying kick. The results show that TimeGAN outperforms other imbalanced data handling techniques. The accuracy, recall, precision, and F1-score of the kick identification model are highest when the sample imbalance ratio is at 1 but decrease as the imbalance ratio increases. This indicates that maintaining a balance between positive and negative samples is essential for training a reliable intelligent kick detection model. The trained model is applied during the drilling of seven ultra-deep wells in Sichuan, demonstrating its effectiveness and accuracy in real-world kick detection.

Performance Evaluation of Delonix regia Sawdust as Cement Retarder in Oil and Gas Well

Sawdust, a byproduct of wood exploitation and processing, poses environmental pollution risks if not managed appropriately. Its substantial carbon footprint can lead to pollution, habitat disruption, and fire hazards. However, repurposing sawdust as a chemical additive in cement slurry within the oil and gas industry offers an environmentally friendly solution. This practice aligns with sustainability goals, enhances cement slurry properties, promotes wellbore stability, and replaces more hazardous additives, thereby reducing environmental impact. This study is aimed at the production of a retarder (sodium lignin) from sawdust waste obtained from the Delonix regia species, and examining its effect on the thickening time/consistency, compressive strength, and rheological properties on the slurry of a Class G cement At a Bottom Hole Circulating Temperature (BHCT) of 90°C, thickening time tests conducted on the slurry samples revealed that as the concentration of the locally synthesized retarder increases, the thickening time of the concrete also increases, with minimal effect on compressive strength. The optimal thickening time result of 6 hours and 13 minutes was achieved with 0.5% Sodium Lignin (Retarder from Delonix regia sawdust) replacing a portion of the cement. At a bottomhole static temperature (BHST) of 100°C, increasing the concentration of the formulated sample led to higher Plastic Viscosities (PV) and yield points in the slurries. The findings indicate that slurries formulated with sodium lignin maintain viscosities within recommended values, making them suitable for pumping.

Enhancing Data Governance with Real-Time Analytics in Oil Production Forecasting

This article explores the transformative impact of real-time analytics on oil production forecasting in the oil and gas industry. It discusses the shift from traditional batch processing to real-time data analysis, highlighting the limitations of batch processing and the benefits of real-time analytics. The article examines how advanced data processing platforms enable more effective data governance and faster insights, leading to improved decision-making, enhanced operational efficiency, and increased production. It also delves into the role of real-time analytics in strengthening compliance and accountability through automated data collection, validation, and consistent data handling. The article presents various case studies and numerical data demonstrating the significant improvements in forecasting accuracy, anomaly detection, and regulatory compliance achieved by implementing real-time analytics systems.

Application of machine learning for reconstruction of multiphase fluid structure measured by a capacitance multi-electrode sensor

Non-invasive real-time measurements of phase content in the reservoir fluid are highly advantageous in the oil and gas industry and remain a current research topic. The paper presents an innovative, self-designed multi-electrode capacitance meter intended for detecting multiphase flow patterns in a low-permittivity medium, such as the reservoir fluid. The ca-pacitance sensor is built with delta-sigma charge modulators capacitance-to-digital converters. Machine learning is applied to convert the capacitance measurements into a tomographic image of the flow pattern. At present, the meter is built with eight electrodes. It is shown that the measurements are repeatable and have a good signal-to-noise ratio. The implemented neural network is capable of correctly reconstructing the tomographic images for a test tube filled with reservoir fluid and placed in various locations inside the test section.

Cross-industry innovation for sustainable repositioning through cluster collaboration

ABSTRACT Recent literature on sustainable transitions seeks to bridge macro – and micro-level dynamics; this study examines how agents at different levels interact, and what implications these lower-level dynamics have on sustainable repositioning. A new normal characterized by frequent industry crises, coupled with green policy initiatives, creates push and pull opportunities for sustainable repositioning; thus, incumbent firms must continuously adjust to changing conditions. Cross-industry innovation (CII) has emerged as a favorable option for sustaining competitiveness. To illustrate this development, this study provides insights into how firms from the oil and gas industry attempt CII in the aquaculture industry based on cluster-to-cluster collaboration and facilitation. Our findings indicate a form of tandem development for both industry clusters and incumbent firms, with the cluster responding to bottom-up changes in its firms. Moreover, cluster collaboration lowers initial CII obstacles in terms of knowledge and entry points and offers a gateway for incumbent firms looking to enter different industries. This facilitates an outbound and coupled CII that emerges to maintain a knowledge continuum for incumbent actors within sustainable repositioning. On a larger scale, these cluster initiatives can act as agents for stepwise sustainability transitions and further evolve into a cross-industry cluster.

Predictive Modeling and Adsorption Behavior, Kinetics, and Thermodynamic Studies of <i>Anthocleista grandiflora </i>Leaf Extract for Corrosion Inhibition of Carbon Steel in Seawater

This study investigates the corrosion inhibition performance of Anthocleista grandiflora leaf (AGL) extract on carbon steel in seawater, considering the effects of temperature, immersion time, and inhibitor concentration. Predictive modeling, adsorption behavior, and the kinetics and thermodynamics of the inhibition process were examined. The weight loss technique,characterization techniques combined with response surface methodology (RSM), revealed that the AGL extract follows the Langmuir adsorption model, exhibiting physical adsorption with ΔG values between −16.24 to −15.49kJ/mol, indicating spontaneous and endothermic inhibition. The thermodynamic parameters entropy (−198.87 to −52.58 J/mol), enthalpy (20.42 to 53.42 kJ/mol), and activation energy (13.68 to 56.32 kJ/mol further support this. The corrosion reaction follows first-order kinetics, with the half-life decreasing as the rate constant and extract concentration increase.The SEM images revealed that the AGL extract formed a protective surface layer on the mild steel, effectively preventing pitting. This protective effect became more pronounced as the concentration of the extract increased. RSM optimization identified optimal conditions for maximum inhibition efficiency (98.70%) and corrosion rate (0.058 mm/y) at 800 ppm, 303 K, and 45 days, with a prediction accuracy of 95%, making it suitable for application in the oil and gas industry.

THIN FILM METHANE SENSOR BASED ON CARBON COMPOSITE

Today, with the oil and gas industry playing a key role in the global energy sector, issues related to the effective control and detection of methane leaks are becoming an integral part of the strategic tasks of the industry. The article is devoted to the research and development of a thin-film sensor for detecting methane in the air using a carbon composite. The technical result of this study is the creation of a highly sensitive and energy efficient sensor for measuring methane concentration. The basis for the sensitive element is a thin film of a carbon composite, consisting of a polymer, single-wall carbon nanotubes and graphene oxide, with aluminum electrodes. The sensor has a number of advantages, including responding to the presence of methane without the need for heating, the simplicity of the sensitive material preparation process, and low cost. The ability to quickly start small-scale production, simplified manufacturing due to the use of only one pair of electrodes, as well as increased energy efficiency due to the absence of the need for heating are also positive characteristics of this device. The study presents the results of manufacturing the sensor, including the steps of cleaning the surface of the substrate, creating a shadow mask, spraying electrodes, forming a thin film of the composite by centrifugation, as well as analyzing the microstructure of the sensor using an atomic force microscope. Also presented are data on obtaining multilayer structures of thin-film resistive sensors based on a polymer matrix nanocomposite, single-wall carbon nanotubes and graphene oxide, including the process of creating electrodes. This study is important for the development of methane monitoring methods in the oil and gas industry and other industrial areas, offering promising technical solutions to ensure environmental and economic sustainability of production.

REDUCING THE COST OF HYDRAULIC FRACTURING

The purpose of this article is to disclose the methodology for reducing costs associated with the use of special equipment for hydraulic fracturing at the wells of Tatneft PJSC. The paper considers in detail a method for optimizing the selection and use of specialized equipment aimed at reducing operating costs and increasing the efficiency of the hydraulic fracturing restoration process.Hydraulic fracturing is an important process in the oil and gas industry, which allows you to increase the flow rate of a well by creating cracks in natural formations. However, the use of specialized equipment for hydraulic fracturing can significantly increase operating costs. Therefore, the purpose of this study was to develop a methodology that would optimize the selection and use of such equipment in order to reduce operating costs.The study analyzed various types of specialized equipment available for hydraulic fracturing and compared them in terms of cost of use and efficiency. The results of the study allowed us to identify the optimal equipment options that most effectively combine low operating costs and good hydraulic fracturing recovery rates.Further implementation of the proposed methodology may allow Tatneft PJSC to significantly reduce the cost of hydraulic fracturing and thereby increase its competitiveness in the oil products market. The discoveries made in the framework of this study may be useful for other oil and gas companies involved in hydraulic fracturing, and contribute to more efficient use of resources and lower operating costs in this area.

NEW ASPECTS OF CORE MICROTOMOGRAPHY

Compared to many other technologies of laboratory studies of core samples, the modern «digital core» technology is a rather young scientific and industrial direction, but for decades it has attracted close attention of a wide range of scientists and specialists of the oil and gas industry (geologists, sedimentologists, lithologists, geophysicists, developers, geological modelers, hydrodynamicists, physicists). For many specialists of practical sphere new methodical techniques and methods of such researches remain little known. If earlier computer X-ray microtomographs in the Russian Federation were the prerogative of only academic structures or service organizations, nowadays many subsoil users have started to equip their own laboratory and research centers with the corresponding specialized equipment, understanding that without these data it is hardly possible to build high-quality geological, petrophysical, tectonic-physical, rheological, hydrodynamic and geological-field models of the studied sediments, as well as to prepare progragmatic models of the studied sediments. In this article the authors made an attempt to study and analyze the current state of development of «digital core» technology.Using extensive factual material the accumulated experience of special studies in the field of full-size core tomography is summarized, estimates of absolute permeability of low-permeability complex sediments are made, results of studying cores in frozen and unfrozen state are given, detailed analysis of fracturing at different stages of sample preparation and ongoing studies is carried out, and specific problems for which a solution using the technology is expected in the near future are formulated In this article the positive and negative sides of poroset and direct methods of modeling of filtration properties by digital core model are studied. Particular attention is paid to the need for large-scale studies to obtain statistically reliable data samples, which can be used to select the most qualitative methods of research and application of algorithms for processing of initial geological and geophysical information.

STRUCTURAL FEATURES OF DRILLING EQUIPMENT COMPONENTS USING MATERIALS WITH THERMOELASTIC PHASE TRANSFORMATIONS

Alloys exhibiting thermoelastic phase transformations are increasingly being used in various industries. This is due to their shape memory effect and the phenomenon of pseudoelasticity (superelasticity), which can significantly improve the performance of various technical systems. Equipment for drilling oil and gas wells is one of the most demanding in terms of reliability and service life, in connection with which the gradual introduction of the above-described intelligent materials and the phenomena they manifest in the oil and gas industry began. This article analyzes the developed design and proposes methods of mounting/dismounting roller bit elements made according to the proposed design using an alloy with thermoelastic phase transformations, namely the shape memory effect, and compares them with existing designs. Computational studies and computational analysis of the design of the fastening of drilling equipment elements using the Solid Works program and the Solid Works Simulation module were carried out, which showed significant advantages of the proposed method when applying the shape memory effect as a method of geometric fixation during installation compared to interference fit. Tests were also carried out on the IR-200-0 breaking machine in order to verify and confirm the theoretical results obtained using special equipment, with the use of which the reliability of fixation due to the intermediate bushing with taper was determined. As an example, sleeves with an inclination angle to the axis of the cone of 3 degrees were made, fixed in the hole with a reverse taper with a response angle of inclination. The tests confirmed the assumptions made and the results obtained in the course of theoretical calculations and computer modeling, showing that even in the martensitic state this alloy fixes the teeth in special holes of the rolling cutter more reliably compared to the interference fit, which, in turn, makes it possible to conclude that it is advisable to use the proposed installation method, which gives obvious advantages over existing ones.

The concept of big data and predictive analytics in reservoir engineering: The future of dynamic reservoir models

Reservoir engineering is a critical discipline in the oil and gas industry, aimed at optimizing the extraction and management of subsurface resources. Traditionally, reservoir models have relied on static data and conventional simulation methods, which often fall short in capturing the dynamic complexities of reservoirs. With the advent of big data and predictive analytics, there is a significant opportunity to revolutionize reservoir modeling by integrating real-time data for continuous updates and more accurate predictions. This review introduces a theoretical exploration of how big data and predictive analytics can transform dynamic reservoir models, highlighting their potential to improve reservoir management and hydrocarbon recovery. Big data technologies enable the collection, storage, and processing of vast amounts of structured and unstructured data from multiple sources, including well logs, seismic surveys, drilling operations, and production data. Predictive analytics, through machine learning and statistical techniques, can extract actionable insights from these datasets to predict reservoir behavior, enhance decision-making, and optimize production strategies. This integration facilitates real-time monitoring and adaptive reservoir management, allowing engineers to respond to changing reservoir conditions and uncertainties more effectively. The review further explores the implications of big data-driven predictive models for enhanced hydrocarbon recovery techniques, such as water flooding and gas injection. By automating data processing and integrating real-time field data, predictive analytics can improve the accuracy of reservoir simulations, reduce downtime, and increase operational efficiency. Ultimately, the future of dynamic reservoir modeling lies in the seamless integration of big data and predictive analytics, providing a pathway to smarter, more sustainable resource management and maximized recovery factors in increasingly complex reservoir environments. Keywords: Big Data, Predictive Analytics, Reservoir Engineering, Dynamic Reservoir, Models.

Exploring advanced techniques in process automation and control: A generic framework for oil and gas industry applications

This paper explores advanced techniques in process automation and control, offering a generic framework tailored for applications within the oil and gas industry. The framework addresses the growing need for operational efficiency, safety, and sustainability in an industry characterized by complex, high-risk processes. The adoption of cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning (ML), and Industrial Internet of Things (IIoT) has revolutionized process control systems, enabling real-time monitoring, predictive maintenance, and optimization of production assets. Advanced Process Control (APC) techniques, including model predictive control (MPC), multivariable control systems, and data-driven analytics, play a pivotal role in enhancing production, minimizing downtime, and reducing operational costs. This generic framework leverages the integration of AI and big data analytics for optimizing control strategies, ensuring accurate decision-making and improving operational responsiveness to changing conditions. Additionally, the paper examines how automated control systems mitigate risks associated with human error, while enhancing safety protocols by providing early warnings and real-time insights into equipment performance. Automation also supports the industry's shift towards more sustainable practices by improving energy efficiency, minimizing environmental impact, and enabling remote monitoring in hazardous locations. Through case studies and examples, the paper illustrates the successful implementation of these techniques in upstream, midstream, and downstream operations, highlighting the significant role of digital transformation in driving innovation. The proposed framework can be adapted to diverse oil and gas settings, serving as a foundation for developing scalable, flexible, and sustainable automation solutions. By outlining the key components and benefits of advanced process automation and control, this study contributes to the ongoing discourse on how technological advancements can reshape operational strategies in the oil and gas sector. Keywords: Process Automation, Control Systems, Oil And Gas Industry, Artificial Intelligence (AI), Machine Learning (ML), Industrial Internet of Things (IIoT), Advanced Process Control (APC), Model Predictive Control (MPC), Predictive Maintenance, Digital Transformation, Sustainability.

Effect of temperature on transport index of fish oil-based drilling mud in high pressure high temperature well: a hybrid model approach

Diesel Oil-based drilling mud used in the oil and gas industry causes severe environmental and public health issues due to its high toxicity, especially in high-pressure, high-temperature (HPHT) wells. Therefore, easily biodegradable synthetic oil-based mud with high drilling performance is essential. Fish oil is a bio-oil known for its non-toxicity, high lubricity, thermal stability, and biodegradability. Effective hole cleaning is crucial for drilling performance and is ensured by the rheological properties of drilling mud. While rheological parameter prediction can be performed using an artificial neural network (ANN) based model, such models often fail to provide accurate predictions for unseen data. In contrast, empirical models are more robust and facilitate generalization. This study aims at assessment of the suitability of invert emulsion fish oil-based drilling mud (IEFOBDM) in HPHT wells through evaluation of hole cleaning performance (HCP) using a hybrid approach that combines an ANN with nonlinear regression (NLR) for temperatures ranging from 40 °C to 80 °C.Firstly, an ANN model was developed considering mud weight, temperature, and shear rate. Secondly, the NLR model was used in the prediction of rheological parameters, namely, Yield point (YP) and Plastic viscosity (PV), based on the predicted shear stress from the ANN model. Finally, the performance metrics of the ANN-NLR rheological model were evaluated using the root mean square error (RMSE) and correlation coefficient (R2) and comparison made with the Single ANN rheological model. The results showed the outperformance of ANN-NLR model over the ANN model for rheological parameter prediction, with R2 values of (0.99) for YP and (1) for PV. The predicted PV and YP values were then used for evaluation of transport index (TI) for HCP analysis. The effect of temperature on TI analysis showed this with increase in temperature, TI also increased, indicating the proposed mud providing good HCP under HPHT conditions.

ANALYSIS OF THE CAUSES OF ACCIDENTS IN THE OIL AND GAS INDUSTRY

In the modern world, the oil and gas industry plays a key role in ensuring the energy security of countries. An important problem of this industry is the high level of accidents on main pipelines. The importance of issues of ensuring the safety of production processes and working conditions of personnel is obvious: accidents that occur at enterprises can paralyze work for a long time, attracting the attention of the media and regulatory authorities, causing not only financial but also significant image damage. In connection with the above, the analysis and statistical study of accidents in this area have become relevant in recent years. Occupational safety measures include risk assessment at workplaces, briefings, medical examinations, provision of personnel with personal protective equipment, special assessment of workplaces, occupational safety days, etc. In order to restore the technical and economic indicators of the oil and gas industry, increase reliability and trouble-free operation, complexes of measures are carried out to repair, reconstruct and modernize the components of the main gas pipeline. Ensuring industrial safety during repair work depends on both the reliability of the process equipment and the reliability of the personnel, since a significant number of accidents and incidents occur due to reasons related to the influence of the human factor. Many studies have been devoted to the problem of management in the field of labor protection and ensuring safety at work. Despite the significant number of works, a practical lack of developments on the issues of managing the processes of ensuring safety and labor protection at enterprises in the oil and gas industry has been discovered, which indicates the need for a more in-depth study of this topic.

Application of the integrated well-surface facility production system for selecting the optimal operating mode of equipment

This work is dedicated to the analysis and development of an integrated production system that combines wells and surface facilities to select the optimal operating mode for equipment. The main focus is on studying data analysis methods, as well as collecting and processing information, which ensures high accuracy in process control and optimal use of technological capabilities. The integration of data from various levels of the production chain, from the well to the surface facilities, opens up new opportunities for optimizing equipment performance and improving resource management quality. Integration of wells and surface facilities – The effective integration of wells and surface facilities is crucial for optimizing production processes, minimizing operational costs, and reducing environmental impact. Integrated management systems allow the automation of many processes, providing continuous monitoring of operating parameters, automatic adjustment of settings, and supplying data for quick decision-making. This includes real-time data collection, analysis, and the use of the obtained information to select optimal operating modes, which helps improve both overall efficiency and safety in production processes. In the modern oil and gas industry, the efficient use of equipment at all stages of hydrocarbon production is of particular importance. Optimizing the operation of wells and surface facilities is a key aspect for increasing economic efficiency and reducing environmental impact. In this regard, the development and application of integrated systems that enable real-time control and adaptation of equipment operating modes has become a relevant and significant task.

Data Reconciliation for Assessing Compliance of Physicochemical Properties of Petroleum Products in Commercial Transactions

The physicochemical properties of petroleum products in commercial transactions are crucial for quality control in the oil and gas industry. However, different laboratories often produce slightly different measurement results. These variations can be significant when approving or rejecting properties based on regulatory agency and environmental body specifications. A simple arithmetic average is typically used to determine the most probable value in disputes. This study proposed using a Data Reconciliation approach to address the disparity between the projected model and empirical data. An objective function was employed to optimize and evaluate parameters using maximum likelihood estimation, considering the experimental uncertainty values. This study found that the flash point of jet fuel, as determined by the Tag Closed Cup Tester, was within the specified range (maximum of 40 °C). The application of this tool resolved a dispute between a supplier and a customer, as the reconciled value with minimized uncertainty was determined to be 37.5 ± 2.0 °C. Additionally, the study utilized experimental results from 12 accredited laboratories to determine a single reconciled value for the final boiling point of gasoline. Despite the varying experimental uncertainties ranging from 6.0 °C to 13 °C, the reconciled uncertainty was minimized to 2.6 °C. The last case study identified that ASTM D4294 was incompatible with other test methods for evaluating the mass fraction in diesel oil. In this manner, Data Reconciliation enhanced the accuracy and effectively reduced measurement uncertainties, rendering it a potent tool for resolving legal disputes when evaluating the compliance of the physicochemical properties of petroleum products in commercial transactions.

Analysis of the Principle of Gas Turbine and State-of-art Applications

Abstract. As a matter of fact, gas turbines have become a cornerstone of modern energy systems, playing a pivotal role in power generation, the oil and gas industry, and the integration of renewable energy sources. This study provides an in-depth analysis of the principles underlying gas turbine operation, alongside a summary of state-of-the-art facilities that represent the pinnacle of current technological advancements. These turbines, known for their high efficiency and flexibility, are crucial for meeting the growing global demand for reliable and cleaner energy. However, significant challenges remain, particularly in terms of material limitations, emissions reduction, and the need for greater operational flexibility to accommodate renewable energy sources. The future of gas turbines may involve a shift towards alternative fuels like hydrogen, which could drastically reduce their environmental impact. These results highlight the ongoing evolution of gas turbines and underscore the importance of continuous innovation to address these challenges, ensuring that gas turbines can meet the energy needs of the future while minimizing their ecological footprint. The findings of this research are essential for advancing the understanding of gas turbine technology and guiding future developments in this field.

STRESS ANALYSIS OF THE MAIN COMPONENTS OF IMPROVED GATE VALVE CONSTRUCTION

This study explores the stress resistance of an improved valve design under high-pressure conditions, focusing on stress distribution across both the sealing element and the valve body. A 2D model of the valve was created in AutoCAD and analyzed using Python’s Matplotlib, simulating performance under pressures up to 75 MPa, 5 MPa above the operational limit. Results reveal critical stress patterns, with stress concentrations reaching up to 325 MPa along the inner radius of the sealing element and valve body. Radial, longitudinal, and circumferential stresses were evaluated, showing a gradual decrease in compressive forces from the inner to outer surfaces. The contact areas between the sealing element and valve body emerged as critical zones, with high-pressure exposure raising the risk of early material fatigue and functional degradation. Overall, the improved valve design exhibits significant resilience under high-pressure conditions. However, sustaining this performance over time will require proactive inspection and maintenance to prevent premature wear and material fatigue. This research provides valuable insights for enhancing valve designs and improving durability in high-pressure applications within the oil and gas industry. The analysis demonstrates that the improved valve design can withstand high pressures effectively, but highlights the importance of routine inspection and maintenance to prevent premature failure due to material fatigue. Continuous monitoring, particularly around the inner radius and critical contact areas, is recommended to ensure operational longevity and reliability under fluctuating pressure conditions. Keywords: Linear motion valve, body, finite element analysis, wear, seal.

EFFECTIVE DOSAGE OF CORROSION INHIBITORS FOR OILFIELD EQUIPMENT PROTECTION FROM LOCALIZED CARBON DIOXIDE CORROSION

The article presents the results of laboratory tests of the industrial corrosion inhibitor VITERASOYUZ-2000 of the BP-2 brand (class of imidazolines) used in the oil and gas industry. In 2021, one of the vertically integrated companies purchased 20 thousand tons of this reagent, which is 10 % of the total purchased volume. The volume of corrosion inhibitors purchased by the company annually (~ 200 thousand t) is 97 % a product of domestic manufacturers. The main line of inhibitors purchased in 2021 is represented by the following brands: Sonkor (> 40 thousand t), Azol (~ 40 thousand t), Kormaster (> 30 thousand t), UnoKem (~ 30 thousand t), VITERASOYUZ (~ 20 thousand t), Scimol (> 10 thousand t), ETK (~ 10 thousand t), others (~ 20 thousand t). These reagents were used to inhibit carbon dioxide, hydrogen sulfide and mixed corrosion of oilfield equipment.In order to evaluate the effectiveness of inhibitors in suppressing local carbon dioxide corrosion and determine their effective dosage laboratory studies were performed using a specially developed authors’ technique. A distinctive feature of the technique is the modeling of an essential feature of corrosion processes occurring in real oilfield systems in the presence of carbon dioxide – a high rate of local corrosion exceeding the corrosion rate by 2–9 times. The standard environment and conditions of laboratory experiments used in laboratory tests make it possible to reproduce local corrosion damage visible and measured using a needle micrometer (> 0.01 mm) on corrosion control samples by the weight method. It is shown that the protective effect of the corrosion inhibitor VITERASOYUZ-2000 of the BP-2 brand in relation to local corrosion is achieved at a dosage of 100–150 mg/dm3.The practical significance of the research is shown by a retrospective analysis of data from weight (gravimetric) corrosion monitoring during industrial testing of corrosion inhibitors, which confirm that the use of «traditional» dosages of 15–40 mg/dm3 (based on the total volume of liquid = oil + water) with a high protective effect against corrosion consumption («general corrosion») do not provide a protective effect against local corrosion. It is noted that in some cases, corrosion inhibitors, due to reducing corrosion consumption, can not help only reduce the rate of local corrosion, but, on the contrary, stimulate its development. The target of the tests is to increase the reliability of pipeline operation and environmental safety while reducing the increase in accidents and the number of failures.

Review of Power-to-Liquid (PtL) Technology for Renewable Methanol (e-MeOH): Recent Developments, Emerging Trends and Prospects for the Cement Plant Industry

The cement industry is a significant contributor (around 8%) to CO2 global emissions. About 60% of the industry’s emissions come from limestone calcination, which is essential for clinker production, while 40% are the result of fuel combustion. Reducing these emissions is challenging due to limestone’s role as the primary raw material for cement. Cement plants are required to achieve carbon neutrality by 2050, as outlined in the 13th United Nations Sustainable Goals. One strategy to achieve this goal, involves Carbon Capture and utilization (CCU). Among the options for CO2 utilization, the Power-to-Liquid (PtL) strategy offers a means to mitigate CO2 emissions. In PtL, the CO2 captured from cement industrial flue gas is combined with the hydrogen generated by renewable electrolysis (green hydrogen) and is catalytically converted into renewable methanol (e-MeOH). In this sense, this review provides a comprehensive overview of the worldwide existing pilot and demonstration units and projects funded by the EU across several industries. It specifically focuses on PtL technology worldwide within cement plants. This work covers 18 locations worldwide, detailing technology existent at plants of different capacities, location, and project partners. Finally, the review analyses techno-economic assessments related to e-MeOH production processes, highlighting the potential impact on achieving carbon neutrality in the cement industry.