This study was prepared as part of a preliminary research program to study the effect of bacteria on the specifications of petrophysical models for reservoir rock -contrast-General in its porous reservoir permeability of the AL-Atta main/south Rumaila fluid. The technique was used permeability for the passage of fluid KL in the evaluation of the accounts permeability of a typical section diameter (1.5 inches) in this study, contrast between them in permeability and porosity, prepared charts and graphs (figures) in particular include the effect of bacterial on the permeability when injected liquid plus bacteria were calculated permeability of the rock for the passage of fluid First, the traditional way and then calculated the permeability for the passage of fluid inoculated with bacteria and the extent of change in the permeability for comparison purposes. To know how much this vulnerability isolated for this study three types of bacteria depending on their need for oxygen to produce energy used for effective bio-growth, reproduction and that gets my way, the redox, and the statement of the different effects of these species and types of vulnerability that could get the rocks of different permeability and identify the problem non-knowledge or non-diagnostic and the amount of impairment laboratory as a basis for opening a new science devoted to the importance of what caused microbiology damage and can be used in aspects of benefit in the field of oil production and minimize material losses due to this type of pollution produced by these microorganisms and to find ways to protect the oil fields of this serious problem. The findings of this study to the major axis are the effect of specific types of microorganisms on the rocks, the reservoir of permeability high, medium and thus lower the rate of oil production and this, in turn, leads to economic return is bad, which can be treated in ways that modern art advanced and modern methods in Part II of the study to achieve better ways to extract oil from oil wells by treating affected.
{"title":"Microorganism Influence on Petrophysics Specifications","authors":"Ayyed Ak","doi":"10.23880/ppej-16000338","DOIUrl":"https://doi.org/10.23880/ppej-16000338","url":null,"abstract":"This study was prepared as part of a preliminary research program to study the effect of bacteria on the specifications of petrophysical models for reservoir rock -contrast-General in its porous reservoir permeability of the AL-Atta main/south Rumaila fluid. The technique was used permeability for the passage of fluid KL in the evaluation of the accounts permeability of a typical section diameter (1.5 inches) in this study, contrast between them in permeability and porosity, prepared charts and graphs (figures) in particular include the effect of bacterial on the permeability when injected liquid plus bacteria were calculated permeability of the rock for the passage of fluid First, the traditional way and then calculated the permeability for the passage of fluid inoculated with bacteria and the extent of change in the permeability for comparison purposes. To know how much this vulnerability isolated for this study three types of bacteria depending on their need for oxygen to produce energy used for effective bio-growth, reproduction and that gets my way, the redox, and the statement of the different effects of these species and types of vulnerability that could get the rocks of different permeability and identify the problem non-knowledge or non-diagnostic and the amount of impairment laboratory as a basis for opening a new science devoted to the importance of what caused microbiology damage and can be used in aspects of benefit in the field of oil production and minimize material losses due to this type of pollution produced by these microorganisms and to find ways to protect the oil fields of this serious problem. The findings of this study to the major axis are the effect of specific types of microorganisms on the rocks, the reservoir of permeability high, medium and thus lower the rate of oil production and this, in turn, leads to economic return is bad, which can be treated in ways that modern art advanced and modern methods in Part II of the study to achieve better ways to extract oil from oil wells by treating affected.","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114659609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The actual work evaluated the effect of initial phenol concentration (CPh0) of 500, 1000 and 1500 mg.L-1, the molar stoichiometric ratio of Phenol/Hydrogen peroxide (RP/H) of 25, 50 and 75 % and time (t) of 30, 90 and 150 min on the oxidation of phenolic effluents by called Direct Contact Thermal Treatment (DiCTT). This process provides a novel means to induce degradation and mineralization of organic pollutants in water. The experimental studies were carried out at semi-industrial plant. The organic pollutant was degraded with a conversion higher than 99% and a Total Organic Carbon (TOC) mineralization exceeding 40%, to a (RP/H) of 75%, independent of the CPh0, that was identified as the optimal condition by thermochemical process. The initial phenol concentration was quantified and identified by the High Performance Liquid Chromatography (HPLC) technique followed by statistical design tools to optimization using Response Surface Methodology (RSM) and an analytical mathematical modelling via Artificial Neural Networks (ANNs). The results also showed the dynamic concentration evolution of the intermediates formed (catechol, hydroquinone and para-benzoquinone). Artificial Neural Networks were applied to model the step experimental of Phenol Degradation (PD) and Total Organic Carbon (TOC) conversion by DiCTT thermochemical process. For the ANN modelling, “statistic 8.0” software was used with a Multi-Layer Perceptron (MLP) feed-forward networks by input-output data using a back-propagation algorithm. The correlation coefficients R2 between the network predictions and the experimental results were in the range of 0.95–0.99.
{"title":"Thermochemical Advanced Oxidation Process by DiCTT for the Degradation/Mineralization of Effluents Phenolics with Optimization using Response Surface Methodology and Artificial Neural Networks Modelling","authors":"Brandão Yb","doi":"10.23880/ppej-16000329","DOIUrl":"https://doi.org/10.23880/ppej-16000329","url":null,"abstract":"The actual work evaluated the effect of initial phenol concentration (CPh0) of 500, 1000 and 1500 mg.L-1, the molar stoichiometric ratio of Phenol/Hydrogen peroxide (RP/H) of 25, 50 and 75 % and time (t) of 30, 90 and 150 min on the oxidation of phenolic effluents by called Direct Contact Thermal Treatment (DiCTT). This process provides a novel means to induce degradation and mineralization of organic pollutants in water. The experimental studies were carried out at semi-industrial plant. The organic pollutant was degraded with a conversion higher than 99% and a Total Organic Carbon (TOC) mineralization exceeding 40%, to a (RP/H) of 75%, independent of the CPh0, that was identified as the optimal condition by thermochemical process. The initial phenol concentration was quantified and identified by the High Performance Liquid Chromatography (HPLC) technique followed by statistical design tools to optimization using Response Surface Methodology (RSM) and an analytical mathematical modelling via Artificial Neural Networks (ANNs). The results also showed the dynamic concentration evolution of the intermediates formed (catechol, hydroquinone and para-benzoquinone). Artificial Neural Networks were applied to model the step experimental of Phenol Degradation (PD) and Total Organic Carbon (TOC) conversion by DiCTT thermochemical process. For the ANN modelling, “statistic 8.0” software was used with a Multi-Layer Perceptron (MLP) feed-forward networks by input-output data using a back-propagation algorithm. The correlation coefficients R2 between the network predictions and the experimental results were in the range of 0.95–0.99.","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127329371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Petroleum was used in its raw state for various purposes in different parts of the world before the discovery of the overwhelming uses of the refined products following the first distillation of lamp oil/ illuminating fuel (kerosene age, 1859-1900) and the development of the internal combustion engines using gasoline or diesel (for vehicles, trucks and ships), the rise in commercial aviation (airplanes and rockets) and other devices, near the beginning of the twentieth century. The burning/combustion of petroleum fuels release greenhouse gases, mainly carbon dioxide (CO2), which creates environmental problems such as global warming, acid rain from sulfur and nitrogen oxide emissions. This rise in CO2 / temperature “global warming” in turn causes other environmental problems such as flooding of coastlines due to melting of the glaciers (polar ice cap melting); disrupted weather patterns i.e. change in wind and rainfall patterns as well as soil moisture; etc., hence the strong quest for an alternative source. On the other hand, apart from serving the aforementioned traditional purposes (transportation fuels), the other petroleum refined products are now the chief source of raw materials (primary petrochemicals such as methanol, ethylene, propylene, butadiene, benzene, toluene and xylene) for the manufacture of chemicals especially organic chemicals, such as textiles, artificial fibers, and plastics of all descriptions, rubber, nitrogen fertilizers, dyestuffs, detergents, pharmaceuticals, medicines, furniture, appliances, solar panels, PVC pipes, bulletproof vests, consumer electronics, wind turbines and automobile parts. Simply put, the use of fossil petroleum refined products goes beyond transportation fuels; it is virtually everything to mankind development. In contrast, synthetic liquid fuels (Synfuels) are liquid fuels (such as gasoline, kerosene, diesel, et cetera) which are produced from substitute/synthetic natural gas (S.N.G.) otherwise known as syngas {derived from virtually any hydrocarbon feedstock, by reaction with steam or oxygen or by reforming of natural gas i.e. methane} and application of the FT-GTL process technique. The appeal of these liquid products (from the FT-GTL process technique) is that they are free from sulfur, aromatics, metals and out performs crude oil petroleum refined products, for instance the diesel will have a very high Octane number and can be a premium blending product while the naphtha would be low in Octane and represents a good petrochemical feedstock. In general, the most significant breakthrough is in syngas for other chemical processes and industries (it is the building block for many petrochemicals, i.e. methanol, ammonia or urea etc.).The theoretical background and basic concepts of the synergy of the existing petroleum crude oil refining technique and the FT-GTL process technique is presented in sufficient detail to tackle the global dual energy challenges (i.e. energy security Petroleum & Petrochemic
{"title":"Synergy of the Conventional Crude Oil and the FT-GTL Processes for Sustainable Synfuels Production: The Game Changer Approach-Phase One Category","authors":"Ekejiuba Aib","doi":"10.23880/ppej-16000330","DOIUrl":"https://doi.org/10.23880/ppej-16000330","url":null,"abstract":"Petroleum was used in its raw state for various purposes in different parts of the world before the discovery of the overwhelming uses of the refined products following the first distillation of lamp oil/ illuminating fuel (kerosene age, 1859-1900) and the development of the internal combustion engines using gasoline or diesel (for vehicles, trucks and ships), the rise in commercial aviation (airplanes and rockets) and other devices, near the beginning of the twentieth century. The burning/combustion of petroleum fuels release greenhouse gases, mainly carbon dioxide (CO2), which creates environmental problems such as global warming, acid rain from sulfur and nitrogen oxide emissions. This rise in CO2 / temperature “global warming” in turn causes other environmental problems such as flooding of coastlines due to melting of the glaciers (polar ice cap melting); disrupted weather patterns i.e. change in wind and rainfall patterns as well as soil moisture; etc., hence the strong quest for an alternative source. On the other hand, apart from serving the aforementioned traditional purposes (transportation fuels), the other petroleum refined products are now the chief source of raw materials (primary petrochemicals such as methanol, ethylene, propylene, butadiene, benzene, toluene and xylene) for the manufacture of chemicals especially organic chemicals, such as textiles, artificial fibers, and plastics of all descriptions, rubber, nitrogen fertilizers, dyestuffs, detergents, pharmaceuticals, medicines, furniture, appliances, solar panels, PVC pipes, bulletproof vests, consumer electronics, wind turbines and automobile parts. Simply put, the use of fossil petroleum refined products goes beyond transportation fuels; it is virtually everything to mankind development. In contrast, synthetic liquid fuels (Synfuels) are liquid fuels (such as gasoline, kerosene, diesel, et cetera) which are produced from substitute/synthetic natural gas (S.N.G.) otherwise known as syngas {derived from virtually any hydrocarbon feedstock, by reaction with steam or oxygen or by reforming of natural gas i.e. methane} and application of the FT-GTL process technique. The appeal of these liquid products (from the FT-GTL process technique) is that they are free from sulfur, aromatics, metals and out performs crude oil petroleum refined products, for instance the diesel will have a very high Octane number and can be a premium blending product while the naphtha would be low in Octane and represents a good petrochemical feedstock. In general, the most significant breakthrough is in syngas for other chemical processes and industries (it is the building block for many petrochemicals, i.e. methanol, ammonia or urea etc.).The theoretical background and basic concepts of the synergy of the existing petroleum crude oil refining technique and the FT-GTL process technique is presented in sufficient detail to tackle the global dual energy challenges (i.e. energy security Petroleum & Petrochemic","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115111035","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Using fuzzy logic technique, this work proposes a mathematical adjustment to the classical volumetric method for estimating oil reserves to manage the level of uncertainty associated with oil reserves estimation. This technique introduces a risk factor (α) into the volumetric method equation to account for the uncertainty associated with estimating the parameters that are used in the volumetric method equation. Risk types that may affect oil reserves estimation can be considered using the risk factor (α) in the modified equation. Results showed that the amount of proven oil reserves decreases exponentially as the value of risk factor (α) increases. It also showed that the ratio of the expected proven oil reserves with respect to proven oil reserves (N*/N) goes to zero when the value of risk factor (α) reaches a value of (5). Three cases were proposed to categorize uncertainty in proven oil reserves estimation: high risk estimate, middle risk estimate and risk-free estimate. Results showed that, for the case of high-risk estimate, expected proven oil reserves (N*) was appreciably lower than the proven oil reserves (N) due to the inclusion of risk. Sources of risk may include, but not limited to, lack of expertise of the evaluator, level of integrity of the evaluator, engineering errors during measurement and calculation and governmental laws. Results also showed that the calculated amount of proven oil reserves (N*), for the middle risk estimate case, is much higher than that for high risk estimate case. As for the riskfree estimate case, the calculated proven oil reserves (N*) using the modified formula equals to the proven oil reserves (N) calculated by the classical volumetric formula. This case reflects 100% confidence and reliability in the parameters’ estimation which also places great trust in the evaluator’s integrity and expertise.
{"title":"Mathematical Modification to the Classical Volumetric Method for Estimating Oil Reserves","authors":"","doi":"10.23880/ppej-16000324","DOIUrl":"https://doi.org/10.23880/ppej-16000324","url":null,"abstract":"Using fuzzy logic technique, this work proposes a mathematical adjustment to the classical volumetric method for estimating oil reserves to manage the level of uncertainty associated with oil reserves estimation. This technique introduces a risk factor (α) into the volumetric method equation to account for the uncertainty associated with estimating the parameters that are used in the volumetric method equation. Risk types that may affect oil reserves estimation can be considered using the risk factor (α) in the modified equation. Results showed that the amount of proven oil reserves decreases exponentially as the value of risk factor (α) increases. It also showed that the ratio of the expected proven oil reserves with respect to proven oil reserves (N*/N) goes to zero when the value of risk factor (α) reaches a value of (5). Three cases were proposed to categorize uncertainty in proven oil reserves estimation: high risk estimate, middle risk estimate and risk-free estimate. Results showed that, for the case of high-risk estimate, expected proven oil reserves (N*) was appreciably lower than the proven oil reserves (N) due to the inclusion of risk. Sources of risk may include, but not limited to, lack of expertise of the evaluator, level of integrity of the evaluator, engineering errors during measurement and calculation and governmental laws. Results also showed that the calculated amount of proven oil reserves (N*), for the middle risk estimate case, is much higher than that for high risk estimate case. As for the riskfree estimate case, the calculated proven oil reserves (N*) using the modified formula equals to the proven oil reserves (N) calculated by the classical volumetric formula. This case reflects 100% confidence and reliability in the parameters’ estimation which also places great trust in the evaluator’s integrity and expertise.","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121933048","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Modelling Supercritical CO2 Migration and Storage in Fractured Reservoirs","authors":"G. P.","doi":"10.23880/ppej-16000334","DOIUrl":"https://doi.org/10.23880/ppej-16000334","url":null,"abstract":"","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124105851","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Methanol production is basically through the traditional methods of Steam Methane Reforming (SMR), Auto-Thermal Reforming (ATR), and Dry Methane Reforming (DMR). However, the process is usually energy intensive, up to 100 bar and 1000oC, leading to high associated and operating costs. This study investigates the techno-economic feasibility of methanol synthesis at lower temperatures and pressures, based on the equilibrium expression presented by Turton, et al. Three (3) methanol production routes were investigated; The Steam Methane Reforming (SMR), the Auto-Thermal Reforming (ATR), and the Dry Methane Reforming (DMR). The peculiarity in each production option was simulated using Aspen HYSYS v11 software. The process parameters were rigorously optimized using the optimizer tool in Aspen HYSYS V11, in order to achieve the most economical yield while meeting the acceptable quality benchmark for the product. The initial simulations were carried out using values from the upper end of the operating ranges as stated in literatures. While monitoring the product yield and quality, the process operating parameters which essentially are the pressures and temperatures point to point through the flow schemes, were reduced to either the lower end of the operating ranges or even much lower provided, an optimum product yield rate and quality was obtained as output. The involved process equipment was sized on a preliminary level in order to estimate the plant cost using the same feed rate of 100MMscf/d of natural gas for all three cases. The simulation results showed that methanol synthesis was optimized at 40o C and 15 bar. a. Furthermore, the ATR option gave the most methanol throughput at 5128.8 MTPD, the SMR option produced 4802.4 MTPD, while the DMR had the least output at 3434.4 MTPD. All three cases proved profitable, with a payback period ranging between 4.82 to 6.52 years. Despite requiring the most capital investment of USD2.136 billion, the ATR option is the most viable technology for this production scale and the quickest to pay back invested capital (4.82 years). As such, it is the most recommended option.
{"title":"Gas-to-Methanol Production at Lower Operating Conditions: Techno-economic Analysis of SMR, ATR, and DMR","authors":"","doi":"10.23880/ppej-16000327","DOIUrl":"https://doi.org/10.23880/ppej-16000327","url":null,"abstract":"Methanol production is basically through the traditional methods of Steam Methane Reforming (SMR), Auto-Thermal Reforming (ATR), and Dry Methane Reforming (DMR). However, the process is usually energy intensive, up to 100 bar and 1000oC, leading to high associated and operating costs. This study investigates the techno-economic feasibility of methanol synthesis at lower temperatures and pressures, based on the equilibrium expression presented by Turton, et al. Three (3) methanol production routes were investigated; The Steam Methane Reforming (SMR), the Auto-Thermal Reforming (ATR), and the Dry Methane Reforming (DMR). The peculiarity in each production option was simulated using Aspen HYSYS v11 software. The process parameters were rigorously optimized using the optimizer tool in Aspen HYSYS V11, in order to achieve the most economical yield while meeting the acceptable quality benchmark for the product. The initial simulations were carried out using values from the upper end of the operating ranges as stated in literatures. While monitoring the product yield and quality, the process operating parameters which essentially are the pressures and temperatures point to point through the flow schemes, were reduced to either the lower end of the operating ranges or even much lower provided, an optimum product yield rate and quality was obtained as output. The involved process equipment was sized on a preliminary level in order to estimate the plant cost using the same feed rate of 100MMscf/d of natural gas for all three cases. The simulation results showed that methanol synthesis was optimized at 40o C and 15 bar. a. Furthermore, the ATR option gave the most methanol throughput at 5128.8 MTPD, the SMR option produced 4802.4 MTPD, while the DMR had the least output at 3434.4 MTPD. All three cases proved profitable, with a payback period ranging between 4.82 to 6.52 years. Despite requiring the most capital investment of USD2.136 billion, the ATR option is the most viable technology for this production scale and the quickest to pay back invested capital (4.82 years). As such, it is the most recommended option.","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129244497","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper seeks to expatiate on the impact of Petroleum Industry Act 2020, given its provision authorizing the Federal Government to withdraw subsidy payment at the downstream sector and deregulate same. Without necessarily antagonizing this initiative, given that it would rid government’s expenditure from the massive the cost that goes with the maintaining subsidy payment, and enable the government focus on other sectors of the economy that could thrive better with increased funding, I attempt to show that the initiative without more could engineer a grim impact on the economy from a pricing standpoint, and ultimately proffering solutions across various timelines to manage the deregulation plans.
{"title":"Deregulation and Inflation: Preventing a Petroleum Industry Act's Paradox","authors":"A. O","doi":"10.23880/ppej-16000332","DOIUrl":"https://doi.org/10.23880/ppej-16000332","url":null,"abstract":"This paper seeks to expatiate on the impact of Petroleum Industry Act 2020, given its provision authorizing the Federal Government to withdraw subsidy payment at the downstream sector and deregulate same. Without necessarily antagonizing this initiative, given that it would rid government’s expenditure from the massive the cost that goes with the maintaining subsidy payment, and enable the government focus on other sectors of the economy that could thrive better with increased funding, I attempt to show that the initiative without more could engineer a grim impact on the economy from a pricing standpoint, and ultimately proffering solutions across various timelines to manage the deregulation plans.","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121075589","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The inevitable result that gas wells witness during their life production is the liquid loading problem. The liquids that come with gas block the production tubing if the gas velocity supplied by the reservoir pressure is not enough to carry them to surface. Researchers used different theories to solve the problem naming, droplet fallback theory, liquid film reversal theory, characteristic velocity, transient simulations, and others. While there is no definitive answer on what theory is the most valid or the one that performs the best in all cases. This paper comes to involve a different approach, a combination between physics-based modeling and statistical analysis of what is known as Machine Learning (ML). The authors used a refined ML algorithm named XGBoost (extreme gradient boosting) to develop a novel full procedure on how to diagnose the well with liquid loading issues and predict the critical gas velocity at which it starts to load if not loaded already. The novel procedure includes a combination of a classification problem where a well will be evaluated based on some completion and fluid properties (diameter, liquid density, gas density, liquid viscosity, gas viscosity, angle of inclination from horizontal (alpha), superficial liquid velocity, and the interfacial tension) as a “Liquid Loaded” or “Unloaded”. The second practice is to determine the critical gas velocity, and this is done by a regression method using the same inputs. Since the procedure is a data-driven approach, a considerable amount of data (247 well and lab measurements) collected from literatures has been used. Convenient ML technics have been applied from dividing the data to scaling, modeling and assessment. The results showed that a wellconstructed XGBoost model with an optimized hyperparameters is efficient in diagnosing the wells with the correct status and in predicting the onset of liquid loading by estimating the critical gas velocity. The assessment of the model was done relatively to existing correlations in literature. In the classification problem, the model showed a better performance with an F-1 score of 0.947 (correctly classified 46 cases from 50 used for testing). In contrast, the next best model was the one by Barnea with an F-1 score of 0.81 (correctly classified 37 from 50 cases). In the regression problem, the model showed an R2 of 0.959. In contrast, the second best model was the one by Shekhar with an R2 of 0.84. The results shown here prove that the model and the procedure developed give better results in diagnosing the well correctly if properly used by engineers.
{"title":"Evaluation of Liquid Loading in Gas Wells Using Machine Learning","authors":"","doi":"10.23880/ppej-16000333","DOIUrl":"https://doi.org/10.23880/ppej-16000333","url":null,"abstract":"The inevitable result that gas wells witness during their life production is the liquid loading problem. The liquids that come with gas block the production tubing if the gas velocity supplied by the reservoir pressure is not enough to carry them to surface. Researchers used different theories to solve the problem naming, droplet fallback theory, liquid film reversal theory, characteristic velocity, transient simulations, and others. While there is no definitive answer on what theory is the most valid or the one that performs the best in all cases. This paper comes to involve a different approach, a combination between physics-based modeling and statistical analysis of what is known as Machine Learning (ML). The authors used a refined ML algorithm named XGBoost (extreme gradient boosting) to develop a novel full procedure on how to diagnose the well with liquid loading issues and predict the critical gas velocity at which it starts to load if not loaded already. The novel procedure includes a combination of a classification problem where a well will be evaluated based on some completion and fluid properties (diameter, liquid density, gas density, liquid viscosity, gas viscosity, angle of inclination from horizontal (alpha), superficial liquid velocity, and the interfacial tension) as a “Liquid Loaded” or “Unloaded”. The second practice is to determine the critical gas velocity, and this is done by a regression method using the same inputs. Since the procedure is a data-driven approach, a considerable amount of data (247 well and lab measurements) collected from literatures has been used. Convenient ML technics have been applied from dividing the data to scaling, modeling and assessment. The results showed that a wellconstructed XGBoost model with an optimized hyperparameters is efficient in diagnosing the wells with the correct status and in predicting the onset of liquid loading by estimating the critical gas velocity. The assessment of the model was done relatively to existing correlations in literature. In the classification problem, the model showed a better performance with an F-1 score of 0.947 (correctly classified 46 cases from 50 used for testing). In contrast, the next best model was the one by Barnea with an F-1 score of 0.81 (correctly classified 37 from 50 cases). In the regression problem, the model showed an R2 of 0.959. In contrast, the second best model was the one by Shekhar with an R2 of 0.84. The results shown here prove that the model and the procedure developed give better results in diagnosing the well correctly if properly used by engineers.","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131462428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The Role of Sulphate Reducing Bacteria in Biocorrosion in the Oil and Gas Industry","authors":"","doi":"10.23880/ppej-16000340","DOIUrl":"https://doi.org/10.23880/ppej-16000340","url":null,"abstract":"","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"31 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114719494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Oil has a very important place in modern industry. However, the safe application of petroleum products is very important for the safety of human life and environmental safety. Therefore, many researchers have conducted extensive research on the combustion properties of petroleum products and the performance of fire extinguishing agents. This paper first describes the progress of several different influencing factors in terms of their impact on the development pattern of oil pool fires, including ambient wind, pressure, tunnel environment, and obstructions. In addition, the progress of research on risk assessment models for oil pool fires is summarized. Finally, the research progress in some new fire extinguishing materials, such as protein foam, two-fluid water mist and modified ultra-fine dry powder, is presented.
{"title":"Review of Oil Pool Fire Research","authors":"W. J","doi":"10.23880/ppej-16000326","DOIUrl":"https://doi.org/10.23880/ppej-16000326","url":null,"abstract":"Oil has a very important place in modern industry. However, the safe application of petroleum products is very important for the safety of human life and environmental safety. Therefore, many researchers have conducted extensive research on the combustion properties of petroleum products and the performance of fire extinguishing agents. This paper first describes the progress of several different influencing factors in terms of their impact on the development pattern of oil pool fires, including ambient wind, pressure, tunnel environment, and obstructions. In addition, the progress of research on risk assessment models for oil pool fires is summarized. Finally, the research progress in some new fire extinguishing materials, such as protein foam, two-fluid water mist and modified ultra-fine dry powder, is presented.","PeriodicalId":282073,"journal":{"name":"Petroleum & Petrochemical Engineering Journal","volume":"154 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-01-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123531633","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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