Gas Allocation Research Articles

Entropy-Based Stochastic Optimization of Multi-Energy Systems in Gas-to-Methanol Processes Subject to Modeling Uncertainties.

In gas-to-methanol processes, optimizing multi-energy systems is a critical challenge toward efficient energy allocation. This paper proposes an entropy-based stochastic optimization method for a multi-energy system in a gas-to-methanol process, aiming to achieve optimal allocation of gas, steam, and electricity to ensure executability under modeling uncertainties. First, mechanistic models are developed for major chemical equipments, including the desulfurization, steam boilers, air separation, and syngas compressors. Structural errors in these models under varying operating conditions result in noticeable model uncertainties. Second, Bayesian estimation theory and the Markov Chain Monte Carlo approach are employed to analyze the differences between historical data and model predictions under varying operating conditions, thereby quantifying modeling uncertainties. Finally, subject to constraints in the model uncertainties, equipment capacities, and energy balance, a multi-objective stochastic optimization model is formulated to minimize gas loss, steam loss, and operating costs. The entropy weight approach is then applied to filter the Pareto front solution set, selecting a final optimal solution with minimal subjectivity and preferences. Case studies using Aspen Hysys-based simulations show that optimization solutions considering model uncertainties outperform the counterparts from a standard deterministic optimization in terms of executability.

A new gas lift allocation method in the IoT environment using a hybrid optimization algorithm

In the realm of petroleum extraction, well productivity declines as reservoirs deplete, eventually reaching a point where continued extraction becomes economically unfeasible. To counteract this, artificial lift techniques are employed, with gas injection being a prevalent method. Ideally, unrestricted gas injection could maximize oil output. However, gas scarcity necessitates judicious resource management to optimize oil production while minimizing gas usage. Gas injection serves to alleviate hydrostatic pressure within wells, thereby enhancing oil recovery. Conventional gas allocation strategies often prove inadequate when confronted with the complex, non-linear constraints of real-world scenarios, particularly under gas supply limitations. This research introduces an innovative approach to gas allocation optimization, leveraging Internet of Things (IoT) technology in conjunction with advanced computational methods. The study melds two optimization algorithms: Particle Swarm Optimization (PSO) and Atom Search Optimization (ASO). This hybrid technique harnesses IoT capabilities for real-time data acquisition and processing, enabling more precise and adaptive optimization. The proposed methodology incorporates PSO’s individual and collective learning mechanisms into the ASO framework, accelerating the solution refinement process. Additionally, it introduces dynamic parameters to balance broad exploration with focused exploitation of the solution space. The algorithm’s efficacy is further enhanced by implementing an adaptive force constant for each “atom” (solution candidate), which evolves based on the atom’s performance over successive iterations. Empirical evaluation of this novel approach demonstrated significant improvements in both energy efficiency and gas utilization. Specifically, the hybrid method achieved average reductions of 12.12% in energy consumption and 18.05% in gas injection volume compared to existing techniques. Also, the results showed that battery life and cost are better than the other methods and have been improved by an average of 7.67% and 9.48%, respectively.

Credible Uncertainties for Natural Gas Properties Calculated from Normalised Natural Gas Composition Data

The evaluation of measurement uncertainty of natural gas properties calculated from composition data are an essential aspect of fiscal metering in the trade of natural gas. For conformity assessment, and in gas allocation, it is essential to have a reliable value for the uncertainty. This need is also reflected in, e.g., ISO 6976, the standard for computing natural gas properties, which follows the requirements of the “Guide to the expression of uncertainty in measurement” much more closely. Normalised compositions and their associated standard uncertainties do not suffice for this purpose. A novel algorithm is provided to recover these correlations from the normalised fractions and associated standard uncertainties, enabling the industry work with the data already stored in their repositories. The standard uncertainties are reproduced within 2%, which is acceptable for uncertainty calculations. The correlation coefficients obtained from the recovery algorithm agree with the ones obtained by normalisation.

Multi-period optimisation of flexible natural gas production network infrastructure with an operational perspective: A mixed integer linear programming approach

With the increased complexities in end markets, advanced quantitative tools became essential for efficient decision-making. Traditional methods that implement average forecasted demand and prices often fail to account for the dynamic nature of network behaviour. Hence, this study introduces a multi-period mixed integer linear programming (MILP) model for flexible natural gas allocation in complex networks. The model was formulated based on Qatar as a case study, utilising a Qatar-based natural gas monetisation network that includes four direct and two indirect monetisation processes. By integrating flexible operational boundaries with annual demand and price forecasts, the model optimises annual allocation strategies to maximise profitability. A scenario-based evaluation of three cases demonstrated the model’s robustness and potential for enhancing profitability. Scenarios limited to operational constraints achieved the highest profitability, especially in allocating high-value commodities such as ammonia and urea. However, the proposed production capacities surpass the current infrastructure capabilities, requiring further infrastructure investments. The validation of results showed a slight profitability decrease of 1.2% when these investment costs were included. Notably the case considering fixed and operating cost variables together as a single cost variable in the objective function, referred to as annualised cost (case C), offered optimal cost quantification with relaxed technical constraints. Overall, the multi-level multi-period MILP model aids decision-makers in understanding system capabilities and explores the benefits of flexible operation in proactively addressing endogenous and exogenous uncertainties.

The Influence of Gas Lift on Oil Drainage Area in Reservoirs

Abstract Gas lift is a widely employed artificial lift method in the oil and gas industry, with the potential to significantly impact oil recovery from reservoirs. This study investigates the effect of gas lift on the oil drainage area within reservoirs, aiming to enhance our understanding of its role in reservoir management. Using advanced reservoir engineering and simulation techniques, the research explores the dynamics of gas lift and its influence on the accessibility of hydrocarbon resources. The study provides a comprehensive analysis of gas lift parameters, including gas injection rates, depths, and configurations, and their collective impact on the drainage area. It also considers the interplay between gas lift operations and reservoir characteristics such as permeability, porosity, and fluid properties. Findings from this study reveal that gas lift can effectively expand the drainage area by reducing bottomhole pressures and facilitating the movement of oil within the reservoir. This expansion enables the recovery of hydrocarbons from previously untapped regions, resulting in improved oil production. The research underscores the importance of optimizing gas lift design to maximize reservoir performance. Additionally, the study addresses operational challenges associated with gas lift, including gas allocation, injection optimization, and equipment reliability. It provides insights into the economic and operational considerations of gas lifts, offering valuable recommendations for reservoir management. The outcomes of this research contribute to a deeper understanding of how gas lift influences the oil drainage area in reservoirs, providing valuable insights for reservoir engineers, operators, and industry professionals involved in oil production. This knowledge supports informed decision-making to optimize oil recovery, reservoir performance, and the efficient use of gas lift technologies.

Data-Driven Decision-Making for Flexible Natural Gas Allocation Under Uncertainties: An Agent-Based Modelling Approach

Despite the anticipated growth in the global demand for energy commodities, the frequently changing market dynamics imposed by environmental regulations and political sanctions create end-user demand uncertainties. This imposes the need for prompt quantitative decision-making approaches to understand how various market structures affect the planning of current natural gas projects. Agent-based modelling (ABM) emerges as a powerful approach to facilitate expedited and well-informed decisions amidst limited timeframes. This study deploys agent-based modelling to investigate natural gas allocation across various utilisation routes under diverse economic and environmental scenarios. Results from four main cases and two sub-scenarios imply that the allocation strategy is driven by utilisation routes considered in each case, followed by the allocation target (i.e., economic or environmental) and the operational bounds. The results reveal that cases prioritising natural gas monetisation for export outperform those meeting power requirements in average annual profitability. In case 4, considering a full network with power, the average annual profitability in the economic scenario reduces by approximately 47% compared to case 3, representing the optimal network configuration with $5.22 billion in average annual profitability. However, the economic scenario of case 3 demonstrates the second-highest rate of emissions (0.66 CO2-eq t/y), following the hydrogen-rich process routes in case 2. Overall, this study presents an innovative data-driven framework for enhancing strategic resource allocation in dynamic business environments. By integrating empirical evidence and technical data with an advanced technical tool (i.e., ABM), the framework provides decision-makers and policymakers with valuable insights for managing uncertainties and shifts in market structures, particularly in existing natural gas projects.

Increasing productivity by using smart gas for optimal management of the gas lift process in a cluster of wells

In the face of the escalating global energy demand, the challenge lies in enhancing the extraction of oil from low-pressure underground reservoirs. The conventional artificial gas lift method is constrained by the limited availability of high-pressure gas for injection, which is essential for reducing hydrostatic bottom hole pressure and facilitating fluid transfer to the surface. This study proposes a novel ‘smart gas’ concept, which involves injecting a gas mixture with an optimized fraction of CO2 and N2 into each well. The research introduces a dual optimization strategy that not only determines the optimal gas composition but also allocates the limited available gas among wells to achieve multiple objectives. An extensive optimization process was conducted to identify the optimal gas injection rate for each well, considering the limited gas supply. The study examined the impact of reducing available gas from 20 to 10 MMSCFD and the implications of water production restrictions on oil recovery. The introduction of smart gas resulted in a 3.1% increase in overall oil production compared to using natural gas. The optimization of smart gas allocation proved effective in mitigating the decline in oil production, with a 25% reduction in gas supply leading to only a 10% decrease in oil output, and a 33% reduction resulting in a 26.8% decrease. The study demonstrates that the smart gas approach can significantly enhance oil production efficiency in low-pressure reservoirs, even with a substantial reduction in gas supply. It also shows that imposing water production limits has a minimal impact on oil production, highlighting the potential of smart gas in achieving environmentally sustainable oil extraction. Furthermore, the implementation of the smart gas approach aligns with global environmental goals by potentially reducing greenhouse gas emissions, thereby contributing to the broader objective of environmental sustainability in the energy sector.

Modeling the dynamic allocation problem of multi-service storage system with strategy learning

Gas storage, especially underground gas storage (UGS), plays a particularly important role in coping with supply disruptions. UGSs provide flexible services, e.g. firm storage service (FSS), interruptible storage service (ISS), and price hedging service (PHS), to their customers to harmonize the fluctuations in gas demand. At the beginning of each period, UGS has to allocate the overall capacity for different services for market auction. Allocation of available capacity is generally based on historical data and hands-on experience, which is operationally and economically inefficient. To address the problem, the paper developed a reinforcement learning model that captures the stochastic and dynamic features of the gas allocation problem. We study the optimal gas dispatching strategy under conditions where uncertain market states (e.g., demand load and price) exist in a continuous state space. The decision maker can improve their strategy by evaluating the expected rewards of storage actions in response to specific market states (or “learning” in the environment). In a 360-day numerical experiment, the model and algorithm demonstrate high efficiency in solving the problem. Meanwhile, several managerial implications regarding working gas volume (WGV) and agreed capacity are proposed to help improve the operational efficiency of UGS.

Maximizing Production Profits: Optimizing Gas Lift Design in the Halfaya Oil Field

Gas lift is one of the most common artificial lift methods which is effectively utilized in the oil industry for enhancing production. However, proper gas allocation into wells can be challenging due to various limitations such as shortage in injected gas and economic considerations. Therefore, the current research is conducted to address the critical requirement to effectively distribute gas to maximize profits in the Halfaya Oil Field- Mishrif formation. Continuous gas lift is one of the most commonly used artificial lift methods. To enhance production rate, a sufficient amount of gas is injected into the production tubing at specific depths to reduce the liquid column pressure as each well has an optimal point for production in an oil reservoir. On the other hand, constraints of gas availability restrict achieving the optimal state of production. Such restrictions combined with economic limitations including high gas prices and compression costs, emphasized the necessity for optimal methodology to enhance oil production. Aside from the importance of the Halfaya oil field, there are limited relevant studies on artificial lifting methods specifically associated with the gas-lifting method used in this paper. Thus, the purpose of the current investigation is to propose a well-tested gas lifting design for oil production improvement. The approach combines the skill of the fmincon function built in MATLAB as an optimizer and the PIPESIM network model to create gas lift performance curves. This resulted in an oil production rate of 18860 STB/d, with a gas lift rate of 9.42 mmscf/d. Establishing such a systematic optimization process can manage the challenges of gas allocation in the Halfaya Oil Field towards maximizing production rates and ultimately increasing net profits.

An innovative gas management methodology based on PSA for efficient gas allocation and utilization in hybrid hydrogen network: Integrating process simulation, modeling, and machine learning

Pressure swing adsorption(PSA) based gas separation technology is widely applied in chemical industries, especially in hydrogen purification. Its complex mechanism and non-steady state feature restrict the separation performance adjustment and application on efficient gas utilization. Process simulation, machine learning and modelling combined innovating gas management methodology is proposed in this paper to perform accurate gas separation for natural gas based syngas mixture. VB script program is employed to drive Aspen adsorption simulation, and then classical and regression tree(CART), polynomial regression(PNR) and surrogate model are comprehensively used to perform machine learning. Consequently, the coefficient matrix is obtained to quantify the separation performance. This method holds the potential for broader application in refineries and SPCs hybrid hydrogen network, facilitating the comprehensive utilization of hydrogen containing gases. Case study results show this methodology can conserve natural gas and gas productivity by 21.3 %, as well as carbon emission by 8211.6 Nm3/h. This work highlights the promising application of digital twin technology within the chemical industry, showcasing how process simulation, machine learning, and modeling can collectively revolutionize gas management and resource conservation.

Gas and energy security in Germany and central and Eastern Europe

Russia's weaponization of gas supplies caused a shock to the energy security of Central and Eastern Europe in 2022. Countries responded by increasing alternative energy supplies and developing new natural gas supply routes, namely through increased LNG import capacity and new interconnectors. At the same time, market forces in the form of higher prices largely ensured an efficient allocation of scarce gas across the region and encouraged the necessary savings. We examine how gas flows to and within the region have been changing in the short- and medium-term and explore the role of renewable and nuclear energy as well as hydrogen in the long-term. We conclude that mitigating the effects of this shock requires the EU to prioritize policies that foster the integrity of its energy market. Government policies and higher fossil fuel prices will encourage the build-up of renewables, increasing energy security medium term. Only in combination with consistently high energy savings and the procurement and distribution of sufficient gas volumes from the global LNG market can Russian supplies be replaced. It is thus the interplay of supply and demand measures that allowed Central and Eastern Europe to withstand the extraordinary shock of 2022.

Optimization of Gas Lifting Design in Mishrif Formation of Halfaya Oil Field

The optimization of artificial gas lift techniques plays a crucial role in the advancement of oil field development. This study focuses on investigating the impact of gas lift design and optimization on production outcomes within the Mishrif formation of the Halfaya oil field. A comprehensive production network nodal analysis model was formulated using a PIPESIM Optimizer-based Genetic Algorithm and meticulously calibrated utilizing field-collected data from a network comprising seven wells. This well group encompasses three directional wells currently employing gas lift and four naturally producing vertical wells. To augment productivity and optimize network performance, a novel gas lift design strategy was proposed. The optimization of gas allocation was executed to maximize oil production rates while minimizing the injected gas volume, thus achieving optimal oil production levels at the most effective gas injection volume for the designated network. The utilization of the PIPESIM Optimizer, founded on genetic algorithm principles, facilitated the attainment of these optimal parameters. The culmination of this study yielded an optimal oil production rate of 18,814 STB/d, accompanied by a gas lift injection rate of 7.56 MMscf/d. This research underscores the significance of strategic gas lift design and optimization in enhancing oil recovery and operational efficiency in complex reservoir systems like the Mishrif formation within the Halfaya oil field.

Two-stage stochastic programming approach for gas allocation network under uncertainty

Pipelines enable enormous amounts of various products (fluids) to be transported from supply nodes to demand nodes. They have traditionally been recognized as the most efficient and secure method of transferring gases. Uncertainties in the parameters may develop in the actual world for a variety of reasons which reduces the efficiency of the gas allocation network (GAN). The amount of available gas cannot be forecasted exactly due to the uncertainty in gas supply and shared usage by other demands. Design of GAN is carried out in a two stage manner: installation and operation. During operation, there is always a chance of change in number of sources or demands than that of initial design phase. In this paper, to deal with such discrete uncertainties, a two-stage stochastic programming approach for GAN is developed. The first stage represents the installation and commissioning of supplying nodes in order to satisfy the demands, and the second stage represents the actual allocation network under different available supply stations scenarios. Illustrative examples are presented to demonstrate the proposed solution procedure and annualized investment for the examples are calculated. The calculated annualized investment is 12%, 28% and 38% less than the worst case solutions. Result explains the benefits of the model in reducing investment costs while incorporating such discrete uncertainties.

Multi-objective decision making of natural gas distribution optimization considering clean heating——evidence from Beijing

ABSTRACT Since the implementation of China’s clean heating plan in 2017, the progress of natural gas replacing coal for heating is still not ideal. With the proposal of the goal of carbon neutralization and the impact of international war, the price, supply, and demand of natural gas have attracted attention. In case of gas shortage, reasonable schemes are required to be allocated to different industries and categories. Therefore, this research divides the gas field into four levels according to the social utility, and establishes the multi-objective distribution optimization model considering the social and economic benefits. The solution of the model is based on genetic algorithm and realized on MATLAB. Multi-objective optimization strategies and several constraints are used to make the algorithm’s process of the search is continuously moving closer to the Pareto optimal front. Taking Beijing as an example, two scenarios are set to simulate and optimize, so as to verify the optimization effect of the model. Better quantitative results are created after reallocation of natural gas. The ideal scenario realizes 5.64% more social benefits than 2019 through additional allocation of 117.288 million cubic meters of reserve gas. The constraint scenario creates 0.67% more socialbenefits with only 0.07% decrease of the economic benefits. The conclusions show that allocating more gas to three main industries can improve the optimization effect and it is necessary to expand the supply of natural gas reserve volume. This paper provides a new perspective of natural gas allocation category and priority, providing theoretical and practical ideas for the planning of natural gas security emergency plan and the development of gas sales plan.

Aceh Special Autonomy Funding Sustainability in Supporting National Resilience

Aceh has been given the eligibility to manage its own territory and government called special autonomy as one of Helsinki MoU compensation. Moreover, special autonomy fund also presents for Aceh in funding infrastructural development and maintenance, people empowerment, poverty eradication, education, social and health that lasts in five years’ time according to existing policy. This research analysed the Aceh special autonomy fund sustainability in supporting national resilience. In analysing the case, descriptive analysis was conducted through identifying existing issues and solution to the issues on both local and national perspective that predominantly on national perspective. The identified issues were less seriousness of Aceh government elite in managing the fund, unclear mechanism in its implementation and less participation of people. The solution offered to the issues were more consistent and appropriate plan for funding implementation, more focus on non-oil and gas allocation, people development concentrated plan.

Economical Optimum Gas Allocation Model Considering Different Types of Gas-Lift Performance Curves

The traditional optimum modes of gas-lift production are usually established by taking the injected gas rate as a decision variable and maximum oil production as the objective function. After solving the model, the injected gas rates of single wells are obtained, and then the oil productions of single wells, the total oil productions of well groups and economic profit can be obtained. However, the models do not take both different types of gas-lift performance curves (GLPCs) and the cost factors of gas-lift production technique into account. On the basis of GLPCs, this paper introduces the factors of a gas-lift production technique, which includes the water cut of crude oil, cost of gas injection and water treatment, and oil and gas prices. The concept of a gas-lift economic performance curve (GLEPC) is proposed, and an optimum gas allocation model is established, considering different types of GLPCs and taking economic benefits as the objective, and the model is solved by the method of mixed penalty function. Taking gas-lift well group JD as an example, four gas-lift gas allocation schemes are obtained, and the proposed economical optimum model is applied to optimize gas allocation and analyze profit. What is more, the oil production rate and the result of optimum gas allocation taking maximum oil production rate as the objective in the model are calculated and compared. Then the gas allocation scheme with maximum economical profit is selected, and the significance of considering different types of GLPCs and taking economic benefits as the objective to gas allocation is confirmed.

Analysis of Constraints Affecting the Allocation of 3 Kg LPG Gas to Base (Case Study of PT. Sintora Putra Gasindo)

This study was conducted aiming to find out what are the obstacles or problems that often or often occur in the allocation of 3 KG LPG gas to the base at PT. SINTORA PUTRA GASINDO. , company finances as well as cooperation within the company with gas agents who contract with the company. In this study, the analysis method of observation interviews and focused discussion was used (Focus Group Discussion). Mr. Muhammad Ichan Pratama as the main director of the company PT. SINTORA PUTRA GASINDO, then we make observations going into the field in the distribution of DO gas which is distributed according to the contract to find out what fraud has been committed, both the base party and the employees who work in the company. PT. SINTO RA PUTRA GASINDO. There is also the use of this research to improve the company's work system, increase the production system or the allocation of 3KG LPG gas to the base so that there is no contract termination from the base to the company due to allocations that are not in accordance with the contract. which is quite a big impact from the constraints that occur in the allocation of 3kg LPG Gas.

An Asymmetric Bargaining Model for Natural-Gas Distribution

For the sustainable socio-economic growth, the energy supply is one of the foundations for any country. The gas shortage is one of the most significant impediments to any emerging country’s economic progress, making it a contested and disputed resource. In the middle of a supply–demand mismatch, distributing limited available gas across administrative units/provinces with competing requirements is a key challenge. In this work, an asymmetric gas allocation bargaining model is proposed under gas shortage to resolve natural gas-related disputes among Pakistan’s administrative units/provinces. Each administrative unit/province is characterized by its gas demand. Results show that the Nash bargaining theory, when applied under equal and bargaining weights, can address the supply–demand mismatches of the gas sector in Pakistan. Such an approach could help policymakers to make a fair gas-supply management system during gas shortage periods and would help in resolving the disputes between the provinces.

Targeting compression energy in batch process

Batch process is widely used in food, agrochemical, and pharmaceutical industries to generate high-priced fine chemicals. Resource conservation problems such as water minimization, energy minimization and hydrogen management has been extensively explored using mathematical optimization and graphical methods for the continuous process in the last few decades. On the other hand, resource conservation in batch process is comparatively less focussed. Batch processes require pumping, piping, and capital investment for the transfer of flow within different time intervals in addition to the intra-pressure level requirements. Compression procedures involve huge amounts of energy that are typically supplied by the compressors. A systematic method is needed for calculating minimum compression energy while satisfying the demand in batch gas allocation network (GAN). In this paper, a novel graphical method based on Pinch Analysis for minimization of compression energy in batch GAN while satisfying minimum resource requirement is presented. This methodology applies to any fixed-scheduled batch GAN. Applications of the proposed methodology are illustrated via two examples. In one of the illustrative examples, reduction of 8% in resource requirement and almost 5% in compression energy is observed when compared to the case where integration between intervals is not considered. This demonstrates that the proposed methodology has significant energy as well as resource-saving potential.

Effects of Inlet Gas Allocations on Performance of an Active Drainage Flow Field Proton Exchange Membrane Fuel Cell.

In developing a proton exchange membrane fuel cell, a high current density is encountered, thus requiring a novel flow field with great drainage performance. Our previous study proposed a novel compound flow field called an active drainage flow field with three inlets, which has an excellent output and drainage performance. Furthermore, the influence of muti-inlet reactant gas allocation on the three inlets is discussed in this study. The results showed that the small mass flow in the active drainage (AD) channel causes a waste of active area, while the large mass flow in the AD channel causes under-ribs convection. Considering the output and drainage performance, the 15% AD mass flow case shows the best performance under the high relative humidity investigation.