Oil And Gas Industry Research Articles

Development, challenges and strategies of natural gas industry under carbon neutral target in China

In the mid-21st century, natural gas will enter its golden age, and the era of natural gas is arriving. This paper reviews the development stages of global natural gas industry and the enlightenment of American shale gas revolution, summarizes the development history and achievements of the natural gas industry in China, analyzes the status and challenges of natural gas in the green and low-carbon energy transition, and puts forward the natural gas industry development strategies under carbon neutral target in China. The natural gas industry in China has experienced three periods: start, growth, and leap forward. At present, China has become the fourth largest natural gas producer and third largest natural gas consumer in the world, and has made great achievements in natural gas exploration and development theory and technology, providing important support for the growth of production and reserves. China has set its goal of carbon neutrality to promote green and sustainable development, which brings opportunities and challenges for natural gas industry. Natural gas has significant low-carbon advantages, and gas-electric peak shaving boosts new energy development; the difficulty and cost of development are more prominent. For the national energy security and harmonious development between economy and ecology under the carbon neutral goal, based on the principle of “comprehensive planning, technological innovation, multi-energy complementarity, diversified integration, flexibility and efficiency, optimization and upgrading”, the construction of the production-supply- storage-marketing system has to be improved so as to boost the development of the natural gas industry. First, it is necessary to strengthen efforts in the exploration and development of natural gas, making projects and arrangement in key exploration and development areas, meanwhile, it is urgent to make breakthroughs in key science theories and technologies, so as to increase reserve and production. Second, it should promote green and innovative development of the natural gas by developing new techniques, expanding new fields and integrating with new energy. Third, there is a demand to realize transformation and upgrading of the supply and demand structure of natural gas by strengthening the layout of pipeline gas, liquefied natural gas and the construction of underground gas storage, establishing reserve system for improving abilities of emergency response and adjustment, raising the proportion of natural gas in the primary energy consumption and contributing to the transformation of energy consumption structure, realizing low-carbon resources utilization and clean energy consumption.

Characteristics of carbon isotopic composition of alkane gas in large gas fields in China

Exploration and development of large gas fields is an important way for a country to rapidly develop its natural gas industry. From 1991 to 2020, China discovered 68 new large gas fields, boosting its annual gas output to 1 925×108 m3 in 2020, making it the fourth largest gas-producing country in the world. Based on 1696 molecular components and carbon isotopic composition data of alkane gas in 70 large gas fields in China, the characteristics of carbon isotopic composition of alkane gas in large gas fields in China were obtained. The lightest and average values of δ13C1, δ13C2, δ13C3 and δ13C4 become heavier with increasing carbon number, while the heaviest values of δ13C1, δ13C2, δ13C3 and δ13C4 become lighter with increasing carbon number. The δ13C1 values of large gas fields in China range from −71.2‰ to −11.4‰ (specifically, from −71.2‰ to −56.4‰ for bacterial gas, from −54.4‰ to −21.6‰ for oil-related gas, from −49.3‰ to −18.9‰ for coal-derived gas, and from −35.6‰ to −11.4‰ for abiogenic gas). Based on these data, the δ13C1 chart of large gas fields in China was plotted. Moreover, the δ13C1 values of natural gas in China range from −107.1‰ to −8.9‰, specifically, from −107.1‰ to −55.1‰ for bacterial gas, from −54.4‰ to −21.6‰ for oil-related gas, from −49.3‰ to −13.3‰ for coal-derived gas, and from −36.2‰ to −8.9‰ for abiogenic gas. Based on these data, the δ13C1 chart of natural gas in China was plotted.

Energy-efficient routing protocol for reliable low-latency Internet of Things in oil and gas pipeline monitoring.

The oil and gas industries (OGI) are the primary global energy source, with pipelines as vital components for OGI transportation. However, pipeline leaks pose significant risks, including fires, injuries, environmental harm, and property damage. Therefore, maintaining an effective pipeline maintenance system is critical for ensuring a safe and sustainable energy supply. The Internet of Things (IoT) has emerged as a cutting-edge technology for efficient OGI pipeline leak detection. However, deploying IoT in OGI monitoring faces significant challenges due to hazardous environments and limited communication infrastructure. Energy efficiency and fault tolerance, typical IoT concerns, gain heightened importance in the OGI context. In OGI monitoring, IoT devices are linearly deployed with no alternative communication mechanism available along OGI pipelines. Thus, the absence of both communication routes can disrupt crucial data transmission. Therefore, ensuring energy-efficient and fault-tolerant communication for OGI data is paramount. Critical data needs to reach the control center on time for faster actions to avoid loss. Low latency communication for critical data is another challenge of the OGI monitoring environment. Moreover, IoT devices gather a plethora of OGI parameter data including redundant values that hold no relevance for transmission to the control center. Thus, optimizing data transmission is essential to conserve energy in OGI monitoring. This article presents the Priority-Based, Energy-Efficient, and Optimal Data Routing Protocol (PO-IMRP) to tackle these challenges. The energy model and congestion control mechanism optimize data packets for an energy-efficient and congestion-free network. In PO-IMRP, nodes are aware of their energy status and communicate node's depletion status timely for network robustness. Priority-based routing selects low-latency routes for critical data to avoid OGI losses. Comparative analysis against linear LEACH highlights PO-IMRP's superior performance in terms of total packet transmission by completing fewer rounds with more packet's transmissions, attributed to the packet optimization technique implemented at each hop, which helps mitigate network congestion. MATLAB simulations affirm the effectiveness of the protocol in terms of energy efficiency, fault-tolerance, and low latency communication.

Artificial Intelligence Applications in Natural Gas Industry: A Literature Review

One of the more controversial uses of artificial intelligence (AI) in the petroleum industry has been in technological advancement. The gas business generates data on a constant basis from several operational procedures. The gas sector is now very concerned about recording these data and using them appropriately. Making decisions based on inferential and predictive data analytics facilitates timely and accurate decision-making. The gas business is seeing a significant increase in the use of data analytics for decision-making despite numerous obstacles. Considerable progress has been made in the aforementioned field of study. With the use of artificial intelligence (AI) and machine learning (ML) techniques, many complicated issues may now be resolved with ease. This study, which looks at artificial intelligence applications in the natural gas sector, collected its data from numerous sources between 2005 and 2023. The current work might offer a technical framework for selecting pertinent technologies that will enable efficient information extraction from the massive amount of data produced by the gas industry.

Hybrid modeling approach for natural gas desulfurization process: Coupling mechanism and data modeling via compact variable identification

The accurate process modelling in natural gas industry is beneficial for enterprises to increase potential profit and realize sustainable production. The integration of industrial data and first-principles computation is effective in hybrid process modelling. However, how to enhance their accuracy, robustness, and interpretability remains a challenge. Herein, a general framework integrating artificial intelligence algorithms and process mechanisms for natural gas desulfurization processes is proposed for accurate hybrid modelling. Firstly, the modelling is driven by the biased sulfur content target data generated by the mechanism model. Secondly, the general data information estimation of multivariate interaction and the independence test framework based on proxy data resampling are proposed based on the high-order information relationship. Then, based on Markov Blanket and causal feature selection principle, the compact variable recognition algorithm and the bias prediction model are further established. Finally, the industrial case of natural gas desulfurization is applied for method validation. On one hand, the prediction effect of the developed hybrid model (R2 0.9453, MAE 0.0982, MSE 0.0168, MAPE 0.03018) performs better than that of the mechanism model. On the other hand, it is indicated by the target deviation modelling that the proposed method can not only effectively reduce the excessive variables into 11 compact variables, which proves the effectiveness and accuracy of the proposed method, with the coefficient of determination R2 reaching 0.9152. Moreover, the rationality and interpretability of the proposed method is validated combining the process knowledge and some interpretability techniques. The contribution of this work is to provide an accurate, robust and interpretable method involving both process mechanism and realistic production data for the modelling for the natural gas industry.

Sealing capacity evaluation of underground gas storage under intricate geological conditions

Evaluating underground gas storage (UGS) sealing capacity is essential for its safe construction and operational efficiency. This involves evaluating both the static sealing capacity of traps during hydrocarbon accumulation and the dynamic sealing capacity of UGS under intensive gas injection and withdrawal, and alternating loads. This study detailed the methodology developed by Sinopec. The approach merges disciplines like geology, geomechanics, and hydrodynamics, employing both dynamic-static and qualitative-quantitative analyses. Sinopec's evaluation methods, grounded in the in situ stress analysis, include mechanistic studies, laboratory tests, geological surveys, stress analysis, and fluid-solid interactions. Through tests on the static and dynamic sealing capacity of UGS, alongside investigations into sealing mechanisms and the geological and geomechanical properties of cap rocks and faults, A geomechanics - rock damage - seepage mechanics dynamic coupling analysis method has been developed to predict in situ stress variations relative to pore pressure changes during UGS operations and evaluate fault sealing capacity and cap rock integrity, thereby setting the maximum operational pressures. Utilizing this evaluation technique, Sinopec has defined performance metrics and criteria for evaluating the sealing capacity of depleted gas reservoirs, enabling preliminary sealing capacity evaluations at UGS sites. These evaluations have significantly informed the design of UGS construction schemes and the evaluation of fault sealing capacity and cap rock integrity during UGS operations.

Two-phase oil and water flow pattern identification in vertical pipes applying long short-term memory networks

Accurate biphasic flow pattern recognition is essential in the design of coatings for the oil and gas sector because it enables engineers to create materials that are tailored to specific flow conditions. This results in enhanced corrosion protection, erosion resistance, flow efficiency, and overall performance of equipment and infrastructure in the challenging environments of the oil and gas industry. The development of flow maps has been based on empirical correlations that incorporate characteristics such as superficial velocities, volume fractions, and physical properties such as the density and viscosity of the analyzed substances. In addition, geometric parameters such as the inclination and the internal diameter of the pipes are considered. However, due to the difficult working conditions on offshore platforms and the limitations in monitoring internal flow patterns, technological advances have been implemented to improve this process using artificial intelligence techniques. In this context, this study proposes using a long-term memory (LSTM) recurrent neural network to predict the flow patterns generated in vertical pipes. This LSTM network was trained and validated using data obtained from a literature database. The results obtained showed that the model has a prediction error of less than 1%. These technological advances represent an important step towards optimizing the flow pattern identification process in the hydrocarbons industry. By leveraging the capabilities of artificial intelligence, more accurate and reliable forecasts can be obtained, enabling informed decisions and improving the efficiency and safety of operations.

Photocatalytic mineralization of diisopropanolamine (DIPA) from natural gas industry wastewater under visible light irradiation: Response surface optimization of synthesis parameters

The photocatalytic mineralization of diiospropanolamin (DIPA) in aqueous solution was investigated employing modified TiO2 under visible light. Response surface methodology based central composite design (CCD) was used to explore the effect of different synthesis parameters. The analysis showed substantial impact (p < 0.0195) of operational factors and their interactions on DIPA mineralization during photocatalytic degradation. Model predictions closely aligned with experimental outcomes (R2 = 0.9706, Adj-R2 = 0.9442). The morphology analysis revealed spherical agglomerates with well-dispersed iron on TiO2, which was corroborated by Raman spectroscopy showing similar spectra to bare TiO2 however, increased intensity and broader peaks at higher iron concentrations. Additionally, X-ray Diffraction confirmed the crystalline nature of the synthesized nanocomposite, while BET surface area analysis demonstrated an increasing trend with metal loading. Under optimal conditions, with 5% Fe loading and 300 °C calcination for 1 h, the photocatalytic efficiency significantly improved, achieving 45.78% DIPA mineralization in the first hour and complete (100%) mineralization in 2 h. This represents a promising solution for addressing natural gas industry wastewater challenges. Future goals include further optimization for enhanced efficiency.

Detection of Pumping Unit in Complex Scenes by YOLOv7 with Switched Atrous Convolution

The petroleum and natural gas industries exhibit a high dependency on lifting equipment for oil and gas. Any malfunction in these devices can lead to severe economic losses. Therefore, continuous and timely monitoring of the status of pumping equipment is of paramount importance to proactively prevent potential issues. In an effort to enhance this monitoring process, this study delves into multi-source data images at the well site and extends traditional information analysis methods. It introduces an improved YOLOv7 method based on switchable atrous convolution. While the YOLOv7 algorithm achieves a balance between speed and accuracy, its robustness in non-standard environments is suboptimal. To address this limitation, we propose the utilization of a switchable atrous convolution method for enhancement, thereby augmenting the adaptability of the model. Images of pumping units from diverse scenarios are actively collected and utilized to construct training, validation, and test sets. Different models, including YOLOv7SAC, YOLOv7, and YOLOv5-n, undergo testing, and their detection performances are systematically compared in complex environments. Experimental findings demonstrate that YOLOv7SAC consistently attains optimal detection results across various scenes. In conclusion, the study suggests that the combination of the YOLOv7 model with switchable atrous convolution proves effective for detecting pumping unit equipment in complex scenarios. This provides robust theoretical support for the detection and identification of pumping equipment issues under challenging conditions.

Carbonous Concealment: Governing “Wild” Substances and Subterranean Storage in an Era of Climate Change

Carbonous Concealment: Governing “Wild” Substances and Subterranean Storage in an Era of Climate Change

Life cycle cost and life cycle environmental analysis of the different waste-to-renewable natural gas pathways: An effort to identify an optimal pathway under different Multi-criteria decision-based scenarios

Currently, due to the insufficient resources of renewable natural gas, the development of renewable pathways to integrate into the natural gas-industry portfolio can reduce many concerns about the reserves depletion and the environmental crises. In the present article a structure-oriented framework for gas networks is developed to compare and prioritize different investment scenarios for renewable natural gas production projects. In this regard, different pathways to produce renewable natural gas through the waste-to-natural gas process were discussed and compared based on different waste feedstocks. To discuss and compare different pathways, a decision-making structure and framework under life cycle cost analysis (LCCA) and life cycle environmental analysis (LCEA) is employed to choose the appropriate pathway for the waste-to-natural gas process. The optimal pathway is identified through different multi-variable decision-making scenarios (under different geographical conditions, access to different feedstocks, and different interests of stakeholders). From the outcomes, under the neutral and Pro-economic scenarios, the renewable natural gas production pathway driven by landfill gas integrated with the pressure swing adsorption- biogas upgrading unit is the optimal and desirable option. While under an Eco-friendly scenario, the pathway driven by animal manure integrated with the pressure swing adsorption-upgrading unit is an optimal and desirable pathway.

Advancing Reservoir Performance Optimization through UserFriendly Excel VBA Software Development

This study addresses the pressing demand for streamlined field performance analysis within oil and natural gas development, which currently necessitates substantial expertise and time investment. The principal aim involves developing a user-friendly software tool dedicated to optimizing reservoir rendition. Leveraging the Havlena and Odeh material balance straight line equation form, this tool integrates a zero-dimensional reservoir model with Decline Curve Analysis. The implementation of this user-friendly software enables achievable material balance optimization by aligning cumulative produced fluid with historical production data, akin to the widely acknowledged concept of history matching in material balance analysis. This accomplishment not only facilitates further endeavors like pressure simulation and forecasting but also augments the comprehension of reservoir dynamics. The analysis incorporated three datasets: one modeled from L.P. Dake's textbook and two drawn from real-life reservoirs in the Niger Delta. Assessment of estimated water influx and cumulative oil production indicated minimal discrepancies between Np Real and Np model for these reservoirs. Consequently, material balance history matching for these reservoirs seems feasible. Achieving reservoir rendition optimization involved a Microsoft Excel VBA code consisting of two hundred and thirty-five (235) lines, meticulously designed to replicate MBAL functionality. The software demonstrated congruent outcomes with MBAL, affirming its reliability for history matching and enhancing reservoir performance. We strongly advocate the utilization of this software for optimizing reservoir performance across diverse global regions. Its capacity to streamline field analysis could significantly benefit the oil and natural gas industry.

In-Situ Hydrogen Production from Natural Gas Wells with Subsurface Carbon Retention

Summary There is a critical need to balance economic prosperity, energy security, and environmental protection. Instead of the conventional production of methane (or CH4), these valuable energy reserves can be exploited in a climate-beneficial way. Exploiting (not producing) methane eliminates the carbon value chain and the associated damage these greenhouse gases (GHGs) cause to the climate. When no carbon is produced to the surface, carbon capture requirements become significantly reduced, and their huge budgets are better used elsewhere. This paper discusses the viability of a wellbore natural gas gasification process, within a downhole completion tool, that converts methane wells into hydrogen (or H2) production wells. The process-generated carbon is simultaneously captured downhole, reinjecting the carbon dioxide (or CO2) into the surrounding geology and potentially gaining reservoir enhanced oil recovery (EOR) as a bonus. The proposed process is for natural gas fields and not for use in the gasification of heavy oils (which is covered by other “combustion type” reservoir management processes performed deep within the reservoir geology). The multiple revenue streams created by the process, together with process energy, feedstock (methane), and cost savings, make it climate-positive and financially viable. This provides a net-zero win-win that is mutually beneficial for the natural gas industry and the environment. Without enormous technical effort, competitive use of commodities, and huge capital costs, it could be possible to convert the natural gas industry directly into a hydrogen and carbon capture industry.

Field experimental study of high expansion foam coverage after LNG leakage

With the rapid development of the world’s natural gas industry, LNG (Liquefied Natural Gas) has received widespread attention as a clean energy source. However, the leakage accidents of LNG are usually accompanied by safety hazards such as explosions, low temperatures, and phase transitions. In this study, the field experiment is carried out to cover LNG leakage pool with HEF (High-Expansion Foam). In the process of LNG leakage, when the ambient temperature at the bottom of the reservoir drops to about − 82.3 °C (the critical temperature of LNG), the ambient temperature would rise briefly due to the effect of phase transition. Meanwhile, the temperature distribution of different vertical and horizontal positions in the reservoir during the process of leakage is analyzed. Besides, it is also found that the density of the spilled vapor is much lower than that of the air, but the water in the surrounding air encounters the cryogenic vapor to condense into small droplets, the small droplets entrap part of the flammable gas during the process of settling to the ground, therefore, the flammable vapor still shows a heavy gas effect. During the initial period of HEF coverage, evaporation from the liquid pools accelerates, resulting in a 386% increase in peak concentration of methane above the reservoir. Nevertheless, when HEF application is maintained an effective thickness, it can effectively reduce the methane vapor concentration downstream of the reservoir near the surface to less than 1/2 LFL (1/2 of the Lower Flammability Limit). In general, the large-scale field experiments have far-reaching engineering significance and reference value for the occurrence and emergency treatment of LNG leakage accidents under actual working conditions.

Insulation Detection of Electric Vehicles by Using FPGA-Based Recursive-Least-Squares Algorithm

The principal reason for why electric vehicles are required to serve as an alternative to the more widespread gasoline and petroleum-based vehicles used in modern times is due to the use of an environmentally conscious means of transportation or to circumvent the tumultuous economic dealings of the compressed natural gas and petroleum industries. There is a growing daily need for large, high-voltage e-mobilities, mostly driven by anticipated advancements in electric vehicle technology. Consequently, all of the various components of these vehicles must be able to be accommodated within a limited and compact space. The battery is an essential component in e-mobility. The insulation, health monitoring, and problem diagnostics of lithium-ion (Li-ion) batteries are of utmost importance in ensuring these vehicles’ safety and efficient functioning. Real-time and fast insulation detection techniques are required to ensure safety in high-voltage (HV) vehicles and to avoid insulation failure. This paper used the Recursive-Least-Squares (RLS) algorithm because it is computationally efficient for building the insulation detection system. Based on the RLS technique, we proposed field programmable gate array (FPGA)-based algorithms and implemented them using VHDL coding. The FPGA is very fast at detection, and the error is lower. We validated the FPGA results with MATLAB simulation results from the existing literature, and the errors are much less when using FPGAs. An experimental hardware platform was also created to validate the proposed FPGA technique with various motor and resistive loadings on electric vehicles (EVs).

Prediction of purified water quality in industrial hydrocarbon wastewater treatment using an artificial neural network and response surface methodology

This study addresses the critical issue of determining the environmental impact of wastewater discharged from a petroleum complex's wastewater treatment plant (WWTP). The emphasis is on assessing the quality of purified water and its potential environmental consequences. Using the water quality index (WQI) as a comprehensive metric for water quality assessment, the study investigates the effects of key parameters such as pH, temperature, phosphates, and hydrocarbons derived from an oil refining complex WWTP. The aim is to contribute to water resource management and environmental monitoring. Machine learning models were trained on Principal Component Analysis (PCA) data, representing 70 % of the total data. The results show that water quality indices vary significantly across seasons, with maximum values for WQI in autumn (40.66), winter (32.96), spring (37.92), and summer (49.34). The ANN and RSM-BBD models effectively forecast experimental values with high predictive accuracy indicated by determination coefficients (R2). For WQI in autumn, winter, spring, and summer, R2 values for ANN were 0.9897, 0.9834, 0.9909, and 0.9956, respectively, and 0.9279, 0.9378, 0.9802, and 0.98866 for RSM-BBD. While the ANN model outperformed the RSM-BBD model, both demonstrated efficacy in predicting water quality indices. The study adds to our understanding of the ecological implications of discharged water from petroleum complexes by providing valuable insights into seasonal variations in water quality factors.

Exploration of High-Quality Development Path of China's Natural Gas Industry

Exploration of High-Quality Development Path of China's Natural Gas Industry

The Impacts of Innovation Attribute, Business Environment, and Risk Management on the Artificial Intelligence Investment Decision in Oman’s Hydrocarbons Industry

The Impacts of Innovation Attribute, Business Environment, and Risk Management on the Artificial Intelligence Investment Decision in Oman’s Hydrocarbons Industry

Factors that Influence AI Investment Decisions in Oman's Hydrocarbons Industry: A Review of the Theoretical Literature and Proposed Theoretical Mode

Factors that Influence AI Investment Decisions in Oman's Hydrocarbons Industry: A Review of the Theoretical Literature and Proposed Theoretical Mode

Relationship between Wastewater Quality and Corporate Stock Price

Almost all industrial players handle wastewater treatment in one way or another by themselves, no matter the industry or company’s size. Some companies have wastewater treatment plants, and others are considering investments for construction or modernisation. Occasionally, industrial wastewater is discharged to a municipal wastewater treatment plant; however, it must sometimes be pre-treated. Corporate wastewater quality is imperative for environmental responsibility, corporate legitimacy and financial performance. This paper examines the relationship between the reduction of hydrocarbon content of offshore and onshore wastewater and the stock price presented in TotalEnergies. The data on hydrocarbon content reduction in offshore and onshore wastewater discharge was collected from the TotalEnergies 2022 Sustainability and Climate Progress Report. In addition, the data on TotalEnergy’s stock price was collected from the FusionMedia online stock price index. Data was analysed quantitatively through the application of a graphical approach. Findings from the graphical analysis of data depict two important dimensions of the stock value effect. Firstly, the results show a corresponding relationship between the reduction in the hydrocarbon content of wastewater and an increase in the stock price value of TotalEnergies. Secondly, the results show that the increase in stock price value continues even beyond the times of hydrocarbon reduction, which shows that investors compensate the company for its previously good performance in hydrocarbon reduction. This legitimacy-ingrained performance shows that hydrocarbon reduction protects the company’s value even during uncertainties and/or hindrances in wastewater quality. Based on this finding, the paper develops an inductive hypothesis, opening a new research agenda. The findings are of practical significance for industrial hydrocarbon content reduction and the attraction of short- and long-term financial value to the industry.