Surface Facilities Research Articles

Effects of the Characterization Methods of Heptane Plus Components (C7+) on the Phase Behavior of Volatile Fluids

For reservoir fluids with strong volatile tendencies, such as gas condensate and volatile oil, the determination of the characteristic parameters of the equation of state has a great influence on the fluid phase behavior and subsequent reservoir engineering, production engineering, and surface facilities design calculations. However, the process of characterizing reservoir fluid lacks a unified and standardized procedure, and the methods are largely empirical and rather complicated. In this paper, three main stages are defined for the characterization of the equation of state parameters for these volatile reservoir fluids, including (1) determining the C7+ distribution, (2) calculating the equation of state parameters for the C7+ single-carbon component, and (3) lump components. The typical calculation methods in each step and their effects on the fluid phase behavior calculations are quantitatively analyzed and compared. Finally, the workflow and the suitable characterization methods for gas condensate and volatile oil phase behavior calculations are selected. The results show that C7+ distribution and C7+ single carbon component properties have a greater influence on the phase behavior prediction of condensate gas and volatile oil, while the lumping of C7+ single carbon component has relatively little influence.

New intelligent models for predicting wax appearance temperature using experimental data – Flow assurance implications

Wax deposition is a major problem during petroleum production causing flow rate reduction and blockage of tubing, pipelines, and surface facilities. Wax deposition occurs when crude oil temperature falls below wax appearance temperature (WAT). WAT is an important parameter for wax deposition modeling and developing, testing, and selecting appropriate wax inhibitors. WAT can be determined in laboratory using several different techniques. Although, each method has its own disadvantages including being time-consuming, expensive, inaccurate, or posing health and safety risks. Experimental WAT data are very scarce in the literature. Reliable and accurate correlations and models for WAT prediction are rare, as well. Hence, development of fast, easy, and reliable models for prediction of WAT is inevitable. The main objective of this research work was to develop and introduce novel intelligent models for precise production of WAT. The artificial intelligent (AI) algorithms used include least-squares support-vector machines (LSSVM), recurrent neural network (RNN), and Adaptive neuro-fuzzy inference system (ANFIS). A high-quality dataset was assembled using experimental WAT data collected from the literature. The dataset consists of 81 experimental which is the largest dataset ever reported. The gathered dataset covers a wide range of density from 0.71 to 0.89 g/cm3, wax content from 0.61 to 25.78 wt%, and Pour point from −36 to 37°C. The selected input parameters include oil density, wax content, and pour point determined using a rigorous feature selection analysis. Statistical analysis showed that pour point has the highest impact on WAT prediction. The modeling results showed that the LSSVM model is the superior model with coefficient of determination (R2) of 0.9893, a root mean squared error (RMSE) of 0.1655, and an average absolute relative deviation (AARD) of 9%. The LSSVM model outperformed the only existing smart model in terms of both performance and accuracy. The proposed smart model can be used for prediction of WAT which is an essential parameter in all wax related research.

Improvement of the delumping method in order to obtain detailed characteristics of the fluid

In compositional modeling, the phase characteristics and behavior of reservoir fluids are calculated using the equation of state. To simplify the calculations and speed them up, the components of the heptane plus fraction (C7+) are grouped into pseudo-components. However, after this procedure, the results of compositional modelling of the reservoir containing the pseudo-components need to be delumped so that they can be used in surface models. It follows from this that the problem lies in the fact that the grouped composition cannot be applied to the modeling of ground structures. The object of the study is Kazakhstani oil, on the example of which a detailed component composition was obtained by analytical delumping based on reduction parameters. In laboratory conditions, the component composition of oil, and other fluid properties were determined. The study presents the results that prove the importance of applying the delumping algorithm in the context of compositional modeling. The results obtained analytically correspond quite accurately to the numerical calculations of the PVTsim software and laboratory experiments. A comparison of existing methods showed a difference of 5 %, which suggests that delumping is an excellent method for describing and obtaining a detailed composition of the hydrocarbon mixture. In practice, the results of the detailed composition of hydrocarbons can be used for the design of refineries. Also, the findings from this research can enhance the planning and design of surface facilities, particularly when developed under reservoir conditions where the pressure exceeds the saturation pressure

Perceived Traffic Risk on Road Infrastructure Conditions among Car Drivers in Lhokseumawe City, Aceh, Indonesia

According to the Indonesian Department of Transportation, 86% of traffic accidents are attributed to human factors. Additionally, road conditions are identified as a contributing factor to road accidents. Therefore, this study aimed to explore road conditions and their impact on the driving behaviour of car drivers. Data for this exploration were collected by distributing questionnaires to 200 respondents, utilizing a Likert scale for answer options. The technique used was random sampling, utilizing descriptive statistical analysis and a Confirmatory Factor Analysis model. Consequently, the result showed that each latent variable construct examined by car drivers had a value exceeding 80%. The most prominent latent variable was the "Condition of Road Surface Facilities," accounting for 88.1% and featuring eight indicators. The indicator "Disturbed when passing through a narrow road," part of the latent variable "Condition of Road Equipment Facilities," had the highest standardized value at 1.223. Finally, this study suggested that the government should prioritize attention to road infrastructure conditions to enhance driver comfort and mitigate the risk of traffic accidents.

HPAM-Cr (III) Gel Syneresis in Bulk Volume

This paper explores the crucial factors influencing the stability of polymer gels utilized in oil well treatments, with a focus on the effects of polymer mixing time and concentrations of polymer and crosslinker. Operating within a practical constraint of a 30-minute mixing time in surface facilities, our findings reveal that incomplete polymer dissolution leads to gel segregation and substantial water release over extended aging at 50 ºC. Notably, a reduction in the crosslinker/ polymer ratio, achieved by lowering crosslinker concentration or increasing polymer concentration, proves effective in mitigating water release to as low as 0‒1% over 90‒185 h of aging. Furthermore, gels prepared in sea and reservoir brines exhibit superior stability compared to those in distilled water, suggesting that ion presence in brines counteracts the repulsion between charged polymer chains observed in distilled water. This study provides valuable insights for optimizing polymer gel formulations under time constraints.

Development and simulation of the gas-downhole water sink-assisted gravity drainage (GDWS-AGD) process to reduce carbon footprint and improve clean oil production by injecting CO2 and petroleum-associated gas

Implementing continuous gas injection into oil reservoirs, surrounded by strong water drives, enhances oil production. Nevertheless, water coning/cresting in these reservoirs leads to high water cuts, substantial oil production reductions, and major water-handling issues for surface facilities. The Gas-Downhole Water-Sink-Assisted Gravity Drainage (GDWS-AGD) process can enhance oil recovery (EOR) in such reservoirs and mitigate the influx of aquifer water into the oil zone. This study compares the performance of GDWS-AGD and GAGD processes based on reservoir-simulation results for two heterogeneous reservoirs: the PUNQ-S3 reservoir and the giant South Rumaila (SR) oil field (Iraq).The PUNQ-S3 simulation exclusively employs carbon dioxide (CO2) as the injected gas. In contrast, the SR simulation evaluates the CO2 injection in comparison to petroleum-associated gas (PAG). The GDWS-AGD process performance was assessed based on oil recovery factor, water cut, gas-to-oil ratio (GOR) in oil producers, cumulative volumes of gas injected, and oil/gas saturations. The PUNQ-S3 simulation employs six wells: two vertical CO2 injectors with perforations at the top of the reservoir to form a gas-cap zone. Additionally, there are two horizontal oil-production wells and two horizontal water-production wells positioned below the oil-water contact (OWC). The GDWS-AGD process was assessed in the SR simulation, using CO2 and PAG, in several configurations to compare it with the basic GAGD. In the SR design, twenty-two vertical gas-injection wells, ten horizontal oil-production wells, and six horizontal water-production wells were placed in the gas-, oil-, and water-bearing zones, respectively. A vertical well was dual-completed with two lateral sections originating from separate kick-off points above and underneath the OWC with 2–3/8-inch tubing for oil and water production, respectively. Hydraulic packers in the vertical wellbore separate oil and water production tubing in the dual-completion configuration, and electric-submersible pumps are then employed for water production.The downhole-water-sink configuration effectively reduces oil-reservoir pressure and minimizes water cut/cresting tendencies. CO2 remains immiscible due to reduced gas-injection pressure and decreased solubility. The CO2-based simulation of the SR field yielded oil-recovery factors of 71.67%, 75.25%, and 76.162% for the GAGD, basic, and advanced GDWS-AGD setups, respectively. The oil-recovery factor of GDWS-AGD was further increased to 82.64% by implementing PAG injection, resulting in a decrease in water cut to 5% in all oil producers. Although PAG gas injection outperforms CO2 in oil recovery, water cut, and gas injectivity in the SR field simulation, the GDWS-AGD approach with CO2 remains a feasible option. This technology has the capability to effectively capture and store significant amounts of CO2 in the flooded reservoir for many years, thereby mitigating carbon emissions associated with fossil fuel usage.

An integrated framework for sand handling in wellbore and surface facilities

Sand production affects over 70% of the oil and gas wells in the world. At higher production rates, the collision of the produced sand particles on the pipelines and fittings at an increased speed leads to material erosion. Over time, as the well's production rate decreases, sand deposition occurs in the wellbore when the fluid velocity drops below a threshold velocity for sand transport. Erosion can lead to equipment damage, and sand deposition can cause blockage. The oil and gas industries spend millions of dollars to control sand production. Such control techniques reduce production rates, thereby reducing cash flow. Alternatively, the co-production of sand and oil can increase the production rate and provide economic benefits if the risk and extent of sand production problems are minimized. Sand management techniques can be employed to determine the operating conditions under which production is high while the risks due to sand production problems are minimal. We developed a framework that integrates models of sand transport, erosion rate, and sand settling characteristics to tackle three major problems related to sand production: sand deposition in wellbores, erosional damage of pipelines and fittings, and sand accumulation in the surface facilities. This framework, SEAD (Sand Erosion, Accumulation, and Deposition), is implemented in a simulation environment to model sand production problems using the fluid properties from a wellbore simulation and user-defined solid properties. We employed the framework to study the effects of varying solid, fluid, and flow properties for different pipe orientations, fittings, and surface facilities as case studies. The results confirm that the risk of sand deposition is high for flow in horizontal pipes, and the erosion rate is high for the fittings such as tee, elbow, and choke. The framework was used to determine the operability range of a hypothetical wellbore to minimize sand deposition, erosion, and accumulation. The hypothetical wellbore produces fluid containing 10.5 m3/h of oil, 15 m3/h of water, and 4.5 m3/h of gas at 75 °C and 200 bar, and carrying 0.01 vol% sand of size 150 μm. Maintaining the production rate of the wellbore between 28 m3/h and 64 m3/h ensures at least 90% sand transport probability (STP) and keeps the erosion rate below 0.1 mm/year. It also keeps the sand accumulation in the separator over a week less than 5% of the separator height, which can be removed with weekly clean-ups.

Prediction of Ground Vibration Velocity Induced by Long Hole Blasting Using a Particle Swarm Optimization Algorithm

Obtaining accurate basic parameters for long hole blasting is challenging, and the resulting vibration damage significantly impacts key surface facilities. Predicting ground vibration velocity accurately and mitigating the harmful effects of blasting are crucial aspects of controlled blasting technology. This study focuses on the prediction of ground vibration velocity induced by underground long hole blasting tests. Utilizing the fitting equation based on the US Bureau of Mines (USBM) formula as a baseline for predicting peak particle velocity, two machine learning models suitable for small sample data, Support Vector Regression (SVR) machine and Random Forest (RF), were employed. The models were optimized using the particle swarm optimization algorithm (PSO) to predict peak particle velocity with multiple parameters specific to long hole blasting. Mean absolute error (MAE), mean Squared error (MSE), and coefficient of determination (R2) were used to assess the model predictions. Compared with the fitting equation based on the USBM model, both the Support Vector Regression (SVR) and Random Forest (RF) models accurately and effectively predict peak particle velocity, enhancing prediction accuracy and efficiency. The SVR model exhibited slightly superior predictive performance compared to the RF model.

Upper limit estimate to wellhead flowing pressure and applicable gas production for a downhole throttling technique in high-pressure–high-temperature gas wells

In recent years, China has explored and exploited several high-pressure deep gas fields. Normally, high-pressure gas wells are gathered and processed through multichoke manifolds on well sites, creating hazards such as high wellhead flowing pressure (Pt) and high risk for on-site operation personnel. Moreover, downhole chokes have been used in place of surface chokes. In doing this, the Joule–Thomson (JT) effect is geothermally regulated, alleviating the formation of hydrates in surface facilities. However, its applicability to high-pressure gas wells is less explored. In an effort to guide its use, the objective of this study is to set selection criteria in terms of the allowable wellhead Pt and gas flow rate. First, isenthalpic lines are separately estimated for dry gas and high liquid hydrocarbon (LHC) content gas condensate at various inlet temperatures with the use of commercial software. Next, by analysis of the resulting isenthalpic curves, several results are obtained on the JT inversion curves and throttling process through a choke. Third, building on these insights, a method for projecting the maximum Pt is presented, leading to a value of 52.5 MPa. Finally, multiparameter models are separately run for two deep gas wells (8100 m and 5000 m), reinforcing the result of the pressure upper limit while maintaining a maximum daily gas production of 14 E4 m3. Both upper limits with a maximum Pt of 52.5 MPa and daily gas production of 14 E4 m3 are corroborated with field data records. These findings are vital to the selection of a viable high-pressure gas well for applying the downhole throttling technique.

Pressure-volume-temperature analysis of Caspian oil to improve the analytical delumping procedure

Compositional modeling of the reservoir isn’t complete without calculations of phase equilibrium and is a complex process involving many calculations. In nature, hydrocarbons don’t occur as separate components, they’re mainly mixtures. When modeling the reservoir composition for phase equilibrium calculations, in order to reduce computational costs, in practice, hydrocarbon mixtures are grouped into pseudo-components. The number of grouped pseudo-components varies from 4 to 10. This grouping process is called lumping. However, when crude oil comes to the surface, it’s important to know its detailed composition, since mixtures grouped into pseudo-components don’t allow you to know this. In this regard, modeling of the detailed composition of hydrocarbons is the main tool for understanding the detailed phase separation and design of surface facilities. In practice, this process is called delumping. In the case of this process, the detailed composition of the fluid is presented and the amount reaches up to 36 components, sometimes more. The delumping process, due to the precise separation of heavy plus fractions into carbons with single ordinal numbers (C7, C8, etc.), makes it possible to clearly recognize non-zero BIP’s in the equation of state. The new analytical approach has not previously been applied to the oils of the Caspian region, so this approach has an important role for the oil industry in the Caspian region. Analytical delumping in this paper was done to improve the PVT modeling. This paper presents the results of PVT research of Caspian oil and presents a comparison of analytical and numerical methods of delumping. As a result of the study, it was found that the analytical approach is in excellent agreement with experimental data and data from software such as PVTsim

Phase equilibria calculations of a hydrocarbon system consisting water and asphaltene using association equation of state: A generalized auto-tune procedure

A generalized algorithm is proposed for four-phase flash calculations in hydrocarbon-water systems in which asphaltene has tendency to be precipitated. The systems which are going to be studied in this paper can be either single phase, two-phase, three-phase or four-phase of gas, oil-rich, asphaltene-rich and aqueous phases. The properties of vapor and oil-rich (liquid) phases can be predicted using any EOS while Henry's law is performed for calculating the properties of water-rich (aqueous) phase. In addition, an association equation of state (AEOS) has been used to predict the amount of the precipitated asphaltenes at different conditions. Using the proposed algorithm, one can predict the number and types of phases and also the properties of each phase, as accurate as possible. As tuning of EOS models is tedious and time consuming, a generalized and auto-tune method based on sensitivity and optimization algorithms is also applied in this study so that the best model could be found easily, quickly and automatically. The proposed approach proved to be efficient, and very good agreement between the results of the presented method and experimental data was found for different test problems. This new procedure can be very useful in compositional reservoir simulation of certain enhanced oil recovery methods and in surface facility process simulation.

Modeling apparent viscosity of waxy crude oils doped with polymers using tree-based models, radial basis function neural networks, and Gaussian process regression

The probability of wax precipitation increases as temperature decreases and this happens during the flow of crude oil from a high temperature reservoir to the cool surface facilities. The present work aims to evaluate the apparent viscosity of waxy crude oils in two conditions: without presence of polymers and doped with them. To reach this purpose, a comprehensive databank consists of 622 actual values of waxy crude oils’ apparent viscosity were gathered. Various intelligent networks including Radial Basis Function (RBF) coupled with three algorithms, namely Teaching-Learning Based Optimizer (TLBO), Marine Predators Algorithm (MPA), and Barnacles Mating Optimizer (BMO), Decision Tree (DT), Random Forest (RF), Extra Tree (ET), and Gaussian Process Regression (GPR) were trained to predict waxy crude oil viscosity values. According to the findings, the GPR model demonstrates exemplary performance and offers forecasts with the minimum average absolute percent relative error (AAPRE = 0.30 %), the highest coefficient of determination (R2 = 0.999), and the lowest standard deviation (SD = 0.011). The accuracy of the other models was also acceptable and overall, tree-based techniques (except ET) and GPR provided better results than RBF coupled with different optimizers. Ultimately, the Leverage method was applied to identify the suspected dataset utilizing outlier detection. The outlier discovery represented that majority of data points, which were used for modeling in this paper, are statistically valid and trustworthy.

Approach Maximizes Marginal Field Value by Going Back to Basics

_ This article, written by JPT Technology Editor Chris Carpenter, contains highlights of paper OTC 32379, “Marginal Field Value Maximization by Going Back to Basics With Design-to-Value Enhancement,” by Yen P. Chan, Muhammad Y.S.M. Yasin, and Ahgheelan S. Thurai, Petronas, et al. The paper has not been peer reviewed. Copyright 2023 Offshore Technology Conference. Reproduced by permission. _ Value is defined by a quantum of functions or returns received from resources invested. In extremely marginal oil and gas fields, returns hardly meet invested resources profitably. Stranded, widely scattered resources add to the complication. The complete paper shares an approach to notional well- and facility-development concept generation for such fields by shifting emphasis to designing for value to improve project viability rigorously. Problem Statement Based on Case Study Case study Field X, located offshore Asia, is one of many oil resources in the region with virgin reservoir pockets and layers that are marginal in volume yet can provide a considerable recoverable volume if developed collectively. However, being horizontally scattered and shallow, with a reservoir true vertical depth (TVD) of less than 2000 m, the field was designed initially with up to 12 wells to maximize reach, thus requiring a large wellhead platform (WHP) at the surface. Gas lift was required from the first day of production, leading to two pipelines at a minimum. Given the number of wells and surface facilities required, this led to a highly negative economic outcome. To quantify the extent of cost reductions and increase the recoverable targeted resources required to meet targeted economic indicators for a business case, the project team performed a series of sensitivity analyses. Despite efforts to phase out field development, minimize surface facilities, and simplify well design where possible, the project remained negative in net present value because reducing the number of wells and facilities will lead to a dramatically lower estimated ultimate recovery (EUR), which is the key driver in any oil and gas development. The project team thus was presented with an almost unworkable situation where scope and cost reduction to satisfy economic returns simply meant that installation of neither wells nor facilities was viable. The authors refer to the approach they describe in the complete paper as “going back to basics.” This approach, coupled with a design-to-value (D2V) approach, was effective in countering seemingly irreversible economic results. This approach is a systematic way to develop an optimal concept for maximum value realization because the agile approach questions every aspect of concept development to achieve minimum technical requirements while providing better clarity in cost/risk tradeoff through concept evaluation in a staircase manner. The process of executing D2V for marginal field development while managing technical risks is detailed in the following subsections for both well design and in facility-concept design.

Analysis of the Application of Micro Steel Pipe Pile Technology in Emergency Maintenance of Ordinary National and Provincial Trunk Highway Subgrade

A provincial road in Guizhou Province experienced cracking and bursting of retaining walls, roadbed subsidence, and road surface cracking, with a rapid trend of disease development. After multiple on-site inspections, it was decided to implement emergency maintenance of the roadbed. For sudden disaster sections that affect traffic safety, emergency plans should be initiated and emergency maintenance and repair work should be carried out in a timely manner. The treatment plan adopts micro steel pipe piles to reinforce the roadbed, demolish and rebuild the original retaining wall, and finally restore the road surface, drainage, and traffic safety facilities.

Development and application of fluid characterization algorithms to obtain an accurate description of a PVT model for Kazakhstani oil

Compositional reservoir modeling often requires a large number of instantaneous calculations. To reduce the dimension of the problem and the amount of computation, several components are usually grouped into pseudo-components. However, to model surface processes, it is important to have a detailed phase composition. Accurate fluid composition requires laboratory analysis, which is costly and time-consuming. This study addresses this issue by introducing an advanced delumping procedure aimed at providing an accurate fluid characterization. The primary objective was to develop a sophisticated delumping procedure capable of precisely describing detailed fluid compositions from calculations involving mixtures. Its goal is to reduce reliance on laboratory analysis, making the process more efficient. The results obtained from this research can improve the planning and modeling of surface facilities, developed under conditions of reservoir pressure above saturation pressure. To test the effectiveness of the proposed delumping and achieve the goals set, experiments were carried out on compositional simulation of oil production in the Caspian basin. The procedure uses reduction parameters calculated from simulation data and an analytical approach to characterize fluid compositions from the original data. Detailed fluid compositions obtained through laboratory PVT analysis were then compared with the results of simulation using PVTsim software and numerical delumping procedures. The findings showed a close agreement between the outcomes of detailed compositions obtained through the delumping procedure and of laboratory analysis, with an average deviation less than 5 %, confirming the effectiveness of delumping as an alternative method for obtaining an accurate fluid composition

Application of fusion property estimation within the multisolid wax prediction model on Kazakhstani crude

AbstractPrecipitation of deposited wax on pipe walls is one of the complex flow assurance problems that cause a decrease and complete blockage of oil production rates by reducing the cross‐sectional area of the flow in the pipelines. In addition, surface facilities require higher energy consumption and equipment failure due to paraffin plugs. The purpose of the research is to use the assessment of melting properties in the model of multisolid (MS) forecasting of paraffin in Kazakhstani oil. This article presents the calculation and modification of the fusion properties for a certain Kazakhstani oil for the subsequent calculation of the MS model for predicting wax deposition; numerous approaches have been developed to predict and prevent the flow assurance problems in both science and industry. Most of them are based on predicting the temperature of crystallization of paraffin considering the temperature dependence of the solubility parameters of individual components in the liquid and solid phases, as well as the molar volumes of individual components. Wax deposition can occur anywhere from a reservoir to surface facilities and pipelines. One of the main limitations of the existing models is their applicability to a wide range of crude oil types, while this modified paraffin predicting model will target Kazakhstani crude oil.

Imidazolium-based ionic liquids as dispersants to improve the stability of asphaltene in Egyptian heavy crude oil

Deposition of asphaltene aggregates can easily depress the oil production, because it may clog the wellbores, annulus, pipelines, and surface facilities. Moreover, asphaltene molecules have a negative effect on the catalytic reactions in the refinery process. Therefore, in this work, three different ionic liquids (IL-H, IL-CH3, and IL-NO2) were synthesized, and characterized using FT-IR and NMR spectroscopy to evaluate their efficiency as asphaltene dispersants. The thermal gravimetric analysis of the prepared ILs showed that IL-H, IL-NO2, and IL-CH3 were thermally stable up to 280 °C. The ILs showed good dispersion activity of the petroleum asphaltenes, where the asphaltene onset precipitation (AOP) was changed from 7.5 to 10.5, 11, and 13.5 ml added n-heptane after the use of IL-H, IL-NO2, and IL-CH3, respectively. Moreover, the colloidal instability index of crude oil was changed from 0.92 (unstable asphaltene) to 0.69 (stable asphaltene). It is noted during the experiments that the presence of an alkyl chain attached to the ionic liquid moiety increases the efficiency of the dispersant. This may be owing to the formation of π–π* with asphaltene molecules due to the presence of electron donating group. Quantum chemical parameters were calculated for the prepared ILs, and the theoretical data confirmed the experimental results.

Predicting hydrogen storage requirements through the natural gas market for a low-emission future

This study presents a systematic workflow for estimating hydrogen storage capacity for a smooth energy transition from natural gas market to hydrogen market to achieve net-zero emissions. The workflow establishes maximum and minimum thresholds for hydrogen storage and considers appropriate storage techniques, including underground or surface facilities. The technique assesses environmental footprint of each storage technique and evaluates associated emission reductions. We apply this workflow to energy market in Alberta and project minimum and maximum storage thresholds of 0.28 and 5 megatonne/year (Mt/year) by 2050. Under three distinct storage scenarios, the storage capacity necessitates 128 salt caverns, 10 million compressed gas tanks, or 78,150 cryogenic liquefied tanks. Salt cavern storage emerges as a promising choice owing to its relatively lower emissions of 0.16 MtCO2e/year and a land footprint of 2.56 km2. The net emission reduction achieved is 1.47 MtCO2e/year, equivalent to 0.8% of Canada's target emission reduction of 190 MtCO2e/year by 2050.

Underground hydrogen storage: Integrated surface facilities and fluid flow modelling for depleted gas reservoirs

We report a new techno-economic model to assess performance and capital costs for large-scale underground hydrogen storage in depleted gas reservoirs. A simulation toolbox is developed to model surface facilities and to simulate the hydrogen flow in geological formations in an integrated fashion.Integrated modelling revealed the following key insights: 1) A buffer system is highly desirable to absorb inherent variability in upstream hydrogen production; 2) hydrogen mixing with existing gases in the reservoir, together with gravity segregation and diffusion, results in a decline in hydrogen purity at the wellhead over time and can require increased purification; 3) the capital cost is dominated by the cost of cushion gas (hydrogen) and the compression system where about 9% of the total energy content of the hydrogen is consumed for compression. The scenarios modelled in our study result in a levelized cost of storage in Australia ranging from 2.3 to 4.29 A$/(kg).

Efficient qualitative risk assessment of pipelines using relative risk score based on machine learning

Pipelines are observed one of the economic modes of transport for transporting oil, gas, and water between various locations. Most of the countries in the world transport petroleum and other flammable products through underground pipelines. The underground and aboveground pipelines are facing various damages due to corrosion, dents, and ruptures due to the environment and operational fluid conditions. The danger of leaks and accidents increases as a result of these damages. Pipelines must be evaluated on a regular basis to make sure they are fit for transmission. By evaluating the effects of damages and the possibility of catastrophic failures using a variety of techniques, pipeline integrity is controlled. Applying the relative risk scoring (RRS) technique, pipeline failures are predicted. One of the probabilistic techniques used to forecast risk based on an impartial assessment is machine learning. With different parameters like corrosion, leakage, materials, atmosphere, surface, earth-movements, above-ground and underground facilities, etc., the RRS method provides an accuracy of 97.5% in identifying the risk and gives a precise classification of risk, whether the pipeline has a high, medium, or low risk without any delay on the prediction compared with Naive Bayes, decision tree, support vector machine, and graph convolutional network.