This study gives a first insight into the use of wood flour as a plant-based and cellulosic-based alter-native additive for newsprint and paperboard production using 100% recycled fibers as a raw material. The study compares four varieties of a spruce wood flour product serving as cellulosic-based additives at addition rates of 2%, 4%, and 6% during operation of a 12-in. laboratory pilot paper machine. Strength properties of the produced newsprint and linerboard products were analyzed. Results suggested that spruce wood flour as a cellulosic-based additive represents a promising approach for improving physical properties of paper and linerboard products made from 100% recycled fiber content. This study shows that wood flour pretreated with a plant-based polysaccharide and untreated spruce wood flour product with a particle size range of 20 μm to 40 μm and 40 μm to 70 μm can increase the bulk and tensile properties in newsprint and linerboard applications.
{"title":"Application of spruce wood flour as a cellulosic-based wood additive for recycled paper applications— A pilot paper machine study","authors":"Klaus Dolle, Sandro Zier","doi":"10.32964/tj20.10.641","DOIUrl":"https://doi.org/10.32964/tj20.10.641","url":null,"abstract":"This study gives a first insight into the use of wood flour as a plant-based and cellulosic-based alter-native additive for newsprint and paperboard production using 100% recycled fibers as a raw material. The study compares four varieties of a spruce wood flour product serving as cellulosic-based additives at addition rates of 2%, 4%, and 6% during operation of a 12-in. laboratory pilot paper machine. Strength properties of the produced newsprint and linerboard products were analyzed. \u0000Results suggested that spruce wood flour as a cellulosic-based additive represents a promising approach for improving physical properties of paper and linerboard products made from 100% recycled fiber content. This study shows that wood flour pretreated with a plant-based polysaccharide and untreated spruce wood flour product with a particle size range of 20 μm to 40 μm and 40 μm to 70 μm can increase the bulk and tensile properties in newsprint and linerboard applications.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79092136","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}
Huang SHAN-CONG, Liu Chang, D. Lei, D. Sheng, Ding Ming-qi, Xia Xin-xing
White water is highly recycled in the papermaking process so that its quality is easily deteriorated, thus producing lots of malodorous gases that are extremely harmful to human health and the environment. In this paper, the effect of hydrogen peroxide (H2O2) on the control of malodorous gases released from white water was investigated. The results showed that the released amount of total volatile organic compounds (TVOC) decreased gradually with the increase of H2O2 dosage. Specifically, the TVOC emission reached the minimum as the H2O2 dosage was 1.5 mmol/L, and meanwhile, the hydrogen sulfide (H2S) and ammonia (NH3) were almost completely removed. It was also found that pH had little effect on the release of TVOC as H2O2 was added, but it evidently affect-ed the release of H2S and NH3. When the pH value of the white water was changed to 4.0 or 9.0, the emission of TVOC decreased slightly, while both H2S and NH3 were completely removed in both cases. The ferrous ions (Fe2+) and the copper ions (Cu2+) were found to promote the generation of hydroxyl radicals (HO•) out of H2O2, enhancing its inhibition on the release of malodorous gases from white water. The Fe2+/H2O2 system and Cu2+/H2O2 system exhibited similar efficiency in inhibiting the TVOC releasing, whereas the Cu2+/H2O2 system showed better perfor-mance in removing H2S and NH3.
{"title":"Control of malodorous gases emission from wet-end white water with hydrogen peroxide","authors":"Huang SHAN-CONG, Liu Chang, D. Lei, D. Sheng, Ding Ming-qi, Xia Xin-xing","doi":"10.32964/tj20.10.615","DOIUrl":"https://doi.org/10.32964/tj20.10.615","url":null,"abstract":"White water is highly recycled in the papermaking process so that its quality is easily deteriorated, thus producing lots of malodorous gases that are extremely harmful to human health and the environment. In this paper, the effect of hydrogen peroxide (H2O2) on the control of malodorous gases released from white water was investigated. The results showed that the released amount of total volatile organic compounds (TVOC) decreased gradually with the increase of H2O2 dosage. Specifically, the TVOC emission reached the minimum as the H2O2 dosage was 1.5 mmol/L, and meanwhile, the hydrogen sulfide (H2S) and ammonia (NH3) were almost completely removed. It was also found that pH had little effect on the release of TVOC as H2O2 was added, but it evidently affect-ed the release of H2S and NH3. When the pH value of the white water was changed to 4.0 or 9.0, the emission of TVOC decreased slightly, while both H2S and NH3 were completely removed in both cases. The ferrous ions (Fe2+) and the copper ions (Cu2+) were found to promote the generation of hydroxyl radicals (HO•) out of H2O2, enhancing its inhibition on the release of malodorous gases from white water. The Fe2+/H2O2 system and Cu2+/H2O2 system exhibited similar efficiency in inhibiting the TVOC releasing, whereas the Cu2+/H2O2 system showed better perfor-mance in removing H2S and NH3.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76145322","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}
Several reports of accidents involving serious mechanical failures of sootblower lances in chemical recovery boilers are known in the pulp and paper industry. These accidents mainly consisted of detachment and ejection of the lance tip, or even of the entire lance, to the inside of the furnace, towards the opposite wall. At least one of these cases known to the author resulted in a smelt-water explosion in the boiler. In other events, appreciable damage or near-miss conditions have already been experienced. The risk of catastrophic consequences of the eventual detachment of the lance tip or the complete lance of a recovery boiler soot-blower has caught the attention of manufacturers, who have adjusted their quality procedures, but this risk also needs to be carefully considered by the technical staff at pulp mills and in industry committees. This paper briefly describes the failure mechanisms that prevailed in past accidents, while recommending inspection and quality control policies to be applied in order to prevent further occurrences of these dangerous and costly component failures. Digital radiography, in conjunction with other well known inspection techniques, appears to be an effective means to ensure the integrity of sootblower lances in chemical recovery boilers used in the pulp and paper industry.
{"title":"Corrosion damage and in-service inspection of retractable sootblower lances in recovery boilers","authors":"Flávio Paoliello","doi":"10.32964/tj20.10.655","DOIUrl":"https://doi.org/10.32964/tj20.10.655","url":null,"abstract":"Several reports of accidents involving serious mechanical failures of sootblower lances in chemical recovery boilers are known in the pulp and paper industry. These accidents mainly consisted of detachment and ejection of the lance tip, or even of the entire lance, to the inside of the furnace, towards the opposite wall. At least one of these cases known to the author resulted in a smelt-water explosion in the boiler.\u0000In other events, appreciable damage or near-miss conditions have already been experienced. The risk of catastrophic consequences of the eventual detachment of the lance tip or the complete lance of a recovery boiler soot-blower has caught the attention of manufacturers, who have adjusted their quality procedures, but this risk also needs to be carefully considered by the technical staff at pulp mills and in industry committees.\u0000This paper briefly describes the failure mechanisms that prevailed in past accidents, while recommending inspection and quality control policies to be applied in order to prevent further occurrences of these dangerous and costly component failures. Digital radiography, in conjunction with other well known inspection techniques, appears to be an effective means to ensure the integrity of sootblower lances in chemical recovery boilers used in the pulp and paper industry.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80981713","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}
V. Raju, M. Engblom, E. Rantala, S. Enestam, Jarmo Mansikkasalo
In this work, we study a boiler experiencing upper furnace plugging and availability issues. To improve the situation and increase boiler availability, the liquor spray system was tuned/modified by testing different combinations of splash plate and beer can nozzles. While beer cans are typically used in smaller furnaces, in this work, we considered a furnace with a large floor area for the study. The tested cases included: 1) all splash plate nozzles (original operation), 2) all beer can nozzles, and 3) splash plate nozzles on front and back wall and beer cans nozzles on side walls. We found that operating according to Case 3 resulted in improved overall boiler operation as compared to the original condition of using splash plates only. Additionally, we carried out computational fluid dynamics (CFD) modeling of the three liquor spray cases to better understand the furnace behavior in detail for the tested cases. Model predictions show details of furnace combustion characteristics such as temperature, turbulence, gas flow pattern, carryover, and char bed behavior. Simulation using only the beer can nozzles resulted in a clear reduction of carryover. However, at the same time, the predicted lower furnace temperatures close to the char bed were in some locations very low, indicating unstable bed burning. Compared to the first two cases, the model predictions using a mixed setup of splash plate and beer can nozzles showed lower carryover, but without the excessive lowering of gas temperatures close to the char bed.
{"title":"Kraft recovery boiler operation with splash plate and/or beer can nozzles — a case study","authors":"V. Raju, M. Engblom, E. Rantala, S. Enestam, Jarmo Mansikkasalo","doi":"10.32964/tj20.10.625","DOIUrl":"https://doi.org/10.32964/tj20.10.625","url":null,"abstract":"In this work, we study a boiler experiencing upper furnace plugging and availability issues. To improve the situation and increase boiler availability, the liquor spray system was tuned/modified by testing different combinations of splash plate and beer can nozzles. While beer cans are typically used in smaller furnaces, in this work, we considered a furnace with a large floor area for the study. The tested cases included: 1) all splash plate nozzles (original operation), 2) all beer can nozzles, and 3) splash plate nozzles on front and back wall and beer cans nozzles on side walls. We found that operating according to Case 3 resulted in improved overall boiler operation as compared to the original condition of using splash plates only.\u0000 Additionally, we carried out computational fluid dynamics (CFD) modeling of the three liquor spray cases to better understand the furnace behavior in detail for the tested cases. Model predictions show details of furnace combustion characteristics such as temperature, turbulence, gas flow pattern, carryover, and char bed behavior. Simulation using only the beer can nozzles resulted in a clear reduction of carryover. However, at the same time, the predicted lower furnace temperatures close to the char bed were in some locations very low, indicating unstable bed burning. Compared to the first two cases, the model predictions using a mixed setup of splash plate and beer can nozzles showed lower carryover, but without the excessive lowering of gas temperatures close to the char bed.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89631390","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}
Nano particle-assisted engineered water is one of the newest hybrid methods of Enhanced Oil Recovery (EOR) that is gaining attention in the oil and gas industry. This is attributed to the low cost of the technique and environmental friendliness of the materials involved. Low salinity and ions adjustment of the injection brine has been reported to be very useful for improving oil production in carbonates, and application of nanoparticles (NPs) to improve oil recovery via different mechanisms such as wettability alteration, interfacial tension reduction, disjoining pressure and viscosity modification. This paper therefore investigates the combined effects of these two techniques on oil-brine-rock (OBR) interactions in carbonate reservoirs. Caspian Sea Water salinity of 13000 ppm was synthesized in the laboratory, potential determining ions such as Mg2+, Ca2+ and SO42- were adjusted to obtain the desired engineered waters used as dispersant for SiO2 nanoparticle. A series of experiments were performed ranging from zeta potential, interfacial tension, contact angle, electron scanning environmental imaging, pH analysis and particle size to determine the optimum formulation of engineered low salinity brine and nanoparticle. The salinities and concentration of NP considered in this experimental study ranges between (3,250 - 40,000) ppm and (0.05 - 0.5) wt.%, respectively. It was observed that optimum homogenization time for achieving stability of the chosen nanofluid without using stabilizer is 45 minutes. Four times sulphate and calcium ions in the engineered water reduced the contact angle from 163 to 109 and 151 to 118 degrees respectively. However, in the presence of NP, the contact angle further reduced to a very low values of 5 and 41 degrees. This confirms the combined effects of EW and that of nanofluid (NF) in altering wettability from the hydrophobicity state to hydrophilicity one that rapidly improves oil recovery in carbonate reservoir. IFT measurements were made between oil and formation brine as well as between oil and different EWs at room temperature. The Formation water has the least value of interfacial tension- 15mN/m. Four times diluted sea water spiked with four times sulphate is denoted as 4dsw4S. The zeta potential values showed dsw4S-NF to be the most stable, whereas EW-NF spiked with 4 times Mg2+ show detrimental effects on NF stability. The nanoparticles sizes were measured to be less than 50 nm. Rheological studies of the EW-NF at different temperatures (25, 40, 60 and 80 degrees Celsius) shows similar trend of Newtonian and non-Newtonian behavior at shear rate less than 100 and above 100 per seconds respectively. We conclude that spiking calcium ion and sulphate ion into the injected brine in combination with 0.1wt% NP yielded the wettability alteration in carbonate rock samples. The significant reduction in wettability is attributed to the combined effects of the active mechanisms present in the hybrid method and is cons
{"title":"Synergistic Effects of Engineered Water-Nanoparticle on Oil/Brine/Rock Interactions in Carbonates","authors":"I. Salaudeen, M. Hashmet, P. Pourafshary","doi":"10.2118/205150-ms","DOIUrl":"https://doi.org/10.2118/205150-ms","url":null,"abstract":"\u0000 Nano particle-assisted engineered water is one of the newest hybrid methods of Enhanced Oil Recovery (EOR) that is gaining attention in the oil and gas industry. This is attributed to the low cost of the technique and environmental friendliness of the materials involved. Low salinity and ions adjustment of the injection brine has been reported to be very useful for improving oil production in carbonates, and application of nanoparticles (NPs) to improve oil recovery via different mechanisms such as wettability alteration, interfacial tension reduction, disjoining pressure and viscosity modification. This paper therefore investigates the combined effects of these two techniques on oil-brine-rock (OBR) interactions in carbonate reservoirs.\u0000 Caspian Sea Water salinity of 13000 ppm was synthesized in the laboratory, potential determining ions such as Mg2+, Ca2+ and SO42- were adjusted to obtain the desired engineered waters used as dispersant for SiO2 nanoparticle. A series of experiments were performed ranging from zeta potential, interfacial tension, contact angle, electron scanning environmental imaging, pH analysis and particle size to determine the optimum formulation of engineered low salinity brine and nanoparticle. The salinities and concentration of NP considered in this experimental study ranges between (3,250 - 40,000) ppm and (0.05 - 0.5) wt.%, respectively.\u0000 It was observed that optimum homogenization time for achieving stability of the chosen nanofluid without using stabilizer is 45 minutes. Four times sulphate and calcium ions in the engineered water reduced the contact angle from 163 to 109 and 151 to 118 degrees respectively. However, in the presence of NP, the contact angle further reduced to a very low values of 5 and 41 degrees. This confirms the combined effects of EW and that of nanofluid (NF) in altering wettability from the hydrophobicity state to hydrophilicity one that rapidly improves oil recovery in carbonate reservoir. IFT measurements were made between oil and formation brine as well as between oil and different EWs at room temperature. The Formation water has the least value of interfacial tension- 15mN/m. Four times diluted sea water spiked with four times sulphate is denoted as 4dsw4S. The zeta potential values showed dsw4S-NF to be the most stable, whereas EW-NF spiked with 4 times Mg2+ show detrimental effects on NF stability. The nanoparticles sizes were measured to be less than 50 nm. Rheological studies of the EW-NF at different temperatures (25, 40, 60 and 80 degrees Celsius) shows similar trend of Newtonian and non-Newtonian behavior at shear rate less than 100 and above 100 per seconds respectively. We conclude that spiking calcium ion and sulphate ion into the injected brine in combination with 0.1wt% NP yielded the wettability alteration in carbonate rock samples. The significant reduction in wettability is attributed to the combined effects of the active mechanisms present in the hybrid method and is cons","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86290737","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}
Constructing and maintaining integrity for different types of wells requires accurate assessment of posed risk level, especially when one barrier element or group of barriers fails. Risk assessment and well integrity (WI) categorization is conducted typically using traditional spreadsheets and in-house software that contain their own inherent errors. This is mainly because they are subjected to the understanding and the interpretation of the assigned team to WI data. Because of these limitations, industrial practices involve the collection and analysis of failure data to estimate risk level through certain established probability/likelihood matrices. However, those matrices have become less efficient due to the possible bias in failure data and consequent misleading assessment. The main objective of this work is to utilize machine learning (ML) algorithms to develop a powerful model and predict WI risk category of gas-lifted wells. ML algorithms implemented in this study are; logistic regression, decision trees, random forest, support vector machines, k-nearest neighbors, and gradient boosting algorithms. In addition, those algorithms are used to develop physical equation to predict risk category. Three thousand WI and gas-lift datasets were collected, preprocessed, and fed into the ML model. The newly developed model can predict well risk level and provide a unique methodology to convert associated failure risk of each element in the well envelope into tangible value. This shows the total potential risk and hence the status of well-barrier integrity overall. The implementation of ML can enhance brownfield asset operations, reduce intervention costs, better control WI through the field, improve business performance, and optimize production.
{"title":"Machine Learning Application for Gas Lift Performance and Well Integrity","authors":"M. S. Yakoot, A. Ragab, O. Mahmoud","doi":"10.2118/205134-ms","DOIUrl":"https://doi.org/10.2118/205134-ms","url":null,"abstract":"\u0000 Constructing and maintaining integrity for different types of wells requires accurate assessment of posed risk level, especially when one barrier element or group of barriers fails. Risk assessment and well integrity (WI) categorization is conducted typically using traditional spreadsheets and in-house software that contain their own inherent errors. This is mainly because they are subjected to the understanding and the interpretation of the assigned team to WI data.\u0000 Because of these limitations, industrial practices involve the collection and analysis of failure data to estimate risk level through certain established probability/likelihood matrices. However, those matrices have become less efficient due to the possible bias in failure data and consequent misleading assessment.\u0000 The main objective of this work is to utilize machine learning (ML) algorithms to develop a powerful model and predict WI risk category of gas-lifted wells. ML algorithms implemented in this study are; logistic regression, decision trees, random forest, support vector machines, k-nearest neighbors, and gradient boosting algorithms. In addition, those algorithms are used to develop physical equation to predict risk category. Three thousand WI and gas-lift datasets were collected, preprocessed, and fed into the ML model.\u0000 The newly developed model can predict well risk level and provide a unique methodology to convert associated failure risk of each element in the well envelope into tangible value. This shows the total potential risk and hence the status of well-barrier integrity overall. The implementation of ML can enhance brownfield asset operations, reduce intervention costs, better control WI through the field, improve business performance, and optimize production.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"132 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79646950","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}
M. Shamlooh, A. Hamza, I. Hussein, M. Nasser, S. Salehi
Lost circulation is one of the most common problems in the drilling of oil and gas wells where mud escapes through natural or induced fractures. Lost circulation can have severe consequences from increasing the operational cost to compromising the stability of wells. Recently, polymeric formulations have been introduced for wellbore strengthening purposes where it can serve as Loss Circulation Materials (LCMs) simultaneously. Polymeric LCMs have the potential to be mixed with drilling fluids during the operation without stopping to avoid non-productive time. In this study, the significance of most common conventional mud additives and their impact on the gelation performance of Polyacrylamide (PAM) / Polyethyleneimine (PEI) has been investigated. Drilling fluid with typical additives has been designed with a weight of 9.6 ppg. Additives including bentonite, barite, CarboxyMethylCellulose (CMC), lignite, caustic soda, desco, and calcium carbonate has been studied individually and combined. Each additive is mixed with the polymeric formulation (PAM 9% PEI 1%) with different ratios, then kept at 130°C for 24 hrs. Rheological performance of the mature gel has been tested using parallel plate geometry, Oscillatory tests have been used to assess the storage Modulus and loss modulus. Moreover, the gelation profile has been tested at 500 psi with a ramped temperature to mimic the reservoir conditions to obtain the gelation time. The gelation time of the polymer-based mud was controllable by the addition of a salt retarder (Ammonium Chloride), where a gelation time of more than 2 hours could be achieved at 130°C. Laboratory observations revealed that bentonite and CMC have the most effect as they both assist in producing stronger gel. While bentonite acts as a strengthening material, CMC increases the crosslinking network. Bentonite has successfully increased the gel strength by 15% providing a storage modulus of up to 1150 Pa without affecting the gelation time. This work helps in better understanding the process of using polymeric formulations in drilling activities. It provides insights to integrate gelling systems that are conventionally used for water shut-off during the drilling operation to replace the conventional loss circulation materials to provide a higher success rate.
{"title":"Investigation on the Effect of Mud Additives on the Gelation Performance of PAM/PEI System for Lost Circulation Control","authors":"M. Shamlooh, A. Hamza, I. Hussein, M. Nasser, S. Salehi","doi":"10.2118/205184-ms","DOIUrl":"https://doi.org/10.2118/205184-ms","url":null,"abstract":"\u0000 Lost circulation is one of the most common problems in the drilling of oil and gas wells where mud escapes through natural or induced fractures. Lost circulation can have severe consequences from increasing the operational cost to compromising the stability of wells. Recently, polymeric formulations have been introduced for wellbore strengthening purposes where it can serve as Loss Circulation Materials (LCMs) simultaneously. Polymeric LCMs have the potential to be mixed with drilling fluids during the operation without stopping to avoid non-productive time. In this study, the significance of most common conventional mud additives and their impact on the gelation performance of Polyacrylamide (PAM) / Polyethyleneimine (PEI) has been investigated.\u0000 Drilling fluid with typical additives has been designed with a weight of 9.6 ppg. Additives including bentonite, barite, CarboxyMethylCellulose (CMC), lignite, caustic soda, desco, and calcium carbonate has been studied individually and combined. Each additive is mixed with the polymeric formulation (PAM 9% PEI 1%) with different ratios, then kept at 130°C for 24 hrs. Rheological performance of the mature gel has been tested using parallel plate geometry, Oscillatory tests have been used to assess the storage Modulus and loss modulus. Moreover, the gelation profile has been tested at 500 psi with a ramped temperature to mimic the reservoir conditions to obtain the gelation time. The gelation time of the polymer-based mud was controllable by the addition of a salt retarder (Ammonium Chloride), where a gelation time of more than 2 hours could be achieved at 130°C.\u0000 Laboratory observations revealed that bentonite and CMC have the most effect as they both assist in producing stronger gel. While bentonite acts as a strengthening material, CMC increases the crosslinking network. Bentonite has successfully increased the gel strength by 15% providing a storage modulus of up to 1150 Pa without affecting the gelation time.\u0000 This work helps in better understanding the process of using polymeric formulations in drilling activities. It provides insights to integrate gelling systems that are conventionally used for water shut-off during the drilling operation to replace the conventional loss circulation materials to provide a higher success rate.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82874724","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 provides the field application of the bee colony optimization algorithm in assisting the history match of a real reservoir simulation model. Bee colony optimization algorithm is an optimization technique inspired by the natural optimization behavior shown by honeybees during searching for food. The way that honeybees search for food sources in the vicinity of their nest inspired computer science researchers to utilize and apply same principles to create optimization models and techniques. In this work the bee colony optimization mechanism is used as the optimization algorithm in the assisted the history matching workflow applied to a reservoir simulation model of WD-X field producing since 2004. The resultant history matched model is compared with with those obtained using one the most widely applied commercial AHM software tool. The results of this work indicate that using the bee colony algorithm as the optimization technique in the assisted history matching workflow provides noticeable enhancement in terms of match quality and time required to achieve a reasonable match.
{"title":"Successful Application of Honey-Bee Optimization Technique in Reservoir Engineering Assisted History Matching: Case Study","authors":"M. Shams","doi":"10.2118/208662-ms","DOIUrl":"https://doi.org/10.2118/208662-ms","url":null,"abstract":"\u0000 This paper provides the field application of the bee colony optimization algorithm in assisting the history match of a real reservoir simulation model. Bee colony optimization algorithm is an optimization technique inspired by the natural optimization behavior shown by honeybees during searching for food. The way that honeybees search for food sources in the vicinity of their nest inspired computer science researchers to utilize and apply same principles to create optimization models and techniques.\u0000 In this work the bee colony optimization mechanism is used as the optimization algorithm in the assisted the history matching workflow applied to a reservoir simulation model of WD-X field producing since 2004. The resultant history matched model is compared with with those obtained using one the most widely applied commercial AHM software tool.\u0000 The results of this work indicate that using the bee colony algorithm as the optimization technique in the assisted history matching workflow provides noticeable enhancement in terms of match quality and time required to achieve a reasonable match.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79017310","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}
Khalid L. Alsamadony, E. U. Yildirim, G. Glatz, Umair Bin Waheed, Sherif M. Hanafy
Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation. In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions. The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics. The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.
{"title":"Deep Learning Enabled Deblurring of Computed Tomography Images of Porous Media","authors":"Khalid L. Alsamadony, E. U. Yildirim, G. Glatz, Umair Bin Waheed, Sherif M. Hanafy","doi":"10.2118/208665-ms","DOIUrl":"https://doi.org/10.2118/208665-ms","url":null,"abstract":"\u0000 Computed tomography (CT) is an important tool to characterize rock samples allowing quantification of physical properties in 3D and 4D. The accuracy of a property delineated from CT data is strongly correlated with the CT image quality. In general, high-quality, lower noise CT Images mandate greater exposure times. With increasing exposure time, however, more wear is put on the X-Ray tube and longer cooldown periods are required, inevitably limiting the temporal resolution of the particular phenomena under investigation.\u0000 In this work, we propose a deep convolutional neural network (DCNN) based approach to improve the quality of images collected during reduced exposure time scans. First, we convolve long exposure time images from medical CT scanner with a blur kernel to mimic the degradation caused because of reduced exposure time scanning. Subsequently, utilizing the high- and low-quality scan stacks, we train a DCNN. The trained network enables us to restore any low-quality scan for which high-quality reference is not available. Furthermore, we investigate several factors affecting the DCNN performance such as the number of training images, transfer learning strategies, and loss functions.\u0000 The results indicate that the number of training images is an important factor since the predictive capability of the DCNN improves as the number of training images increases. We illustrate, however, that the requirement for a large training dataset can be reduced by exploiting transfer learning. In addition, training the DCNN on mean squared error (MSE) as a loss function outperforms both mean absolute error (MAE) and Peak signal-to-noise ratio (PSNR) loss functions with respect to image quality metrics.\u0000 The presented approach enables the prediction of high-quality images from low exposure CT images. Consequently, this allows for continued scanning without the need for X-Ray tube to cool down, thereby maximizing the temporal resolution. This is of particular value for any core flood experiment seeking to capture the underlying dynamics.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87753939","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}
Salman Sadeg Deumah, Wahib Ali Yahya, A. M. Al-Khudafi, K. Ba-Jaalah, Waleed Tawfeeq Al-Absi
Gas viscosity is an important physical property that controls and influences the flow of gas through porous media and pipe networks. An accurate gas viscosity model is essential for use with reservoir and process simulators. The objective of this study is to assess the predictability of gas viscosity of Yemeni gas fields using machine learning techniques. Performance of some machine learning techniques in the prediction of gas viscosity investigated in this work. The techniques include K-nearest neighbors (KNN), Random Forest (RF), Multiple Linear Regression (MLR), and Decision Tree (DT). About 440 data points were collected from different Yemeni gas fields were used to develop the machine-learning model. The input data used in the training include pressure, temperature, gas density, specific gravity, gas formation volume factor, gas deviation factor, gas molecular weight, pseudo-reduced temperature and pressure, pseudo-critical temperature and pressure, and non-hydrocarbon gas components (N2, CO2, and H2S). Part of the data (75%) was used to train the developed models using the algorithms while another part of the data (25%) was used to predict the viscosity of gas for samples. Trained machine learning models were constructed using the Python programming language. The performance and accuracy of the machine learning models were tested and compared their results based on four different functional input datasets. The result of this study found that that the DT model predicted the gas viscosity with higher accuracy, and gave very good results better than other models based on input parameters of the dataset (A) and (B). This was evidenced by lower the Root mean square error (0.000832), lower mean absolute percent relative error (0.042%), and higher coefficient of determination (R2=0.9465). The proposed approach in the present study provides an accurate and inexpensive model for estimating the viscosity of gases as a function of all input parameters of the dataset (A). Overall, the relative effects of these different input parameters have verified that the gas viscosity has the uppermost relevant to the gas density and specific gravity that have the highest percentage of 51%.
{"title":"Prediction of Gas Viscosity of Yemeni Gas Fields Using Machine Learning Techniques","authors":"Salman Sadeg Deumah, Wahib Ali Yahya, A. M. Al-Khudafi, K. Ba-Jaalah, Waleed Tawfeeq Al-Absi","doi":"10.2118/208667-ms","DOIUrl":"https://doi.org/10.2118/208667-ms","url":null,"abstract":"\u0000 Gas viscosity is an important physical property that controls and influences the flow of gas through porous media and pipe networks. An accurate gas viscosity model is essential for use with reservoir and process simulators. The objective of this study is to assess the predictability of gas viscosity of Yemeni gas fields using machine learning techniques.\u0000 Performance of some machine learning techniques in the prediction of gas viscosity investigated in this work. The techniques include K-nearest neighbors (KNN), Random Forest (RF), Multiple Linear Regression (MLR), and Decision Tree (DT). About 440 data points were collected from different Yemeni gas fields were used to develop the machine-learning model. The input data used in the training include pressure, temperature, gas density, specific gravity, gas formation volume factor, gas deviation factor, gas molecular weight, pseudo-reduced temperature and pressure, pseudo-critical temperature and pressure, and non-hydrocarbon gas components (N2, CO2, and H2S). Part of the data (75%) was used to train the developed models using the algorithms while another part of the data (25%) was used to predict the viscosity of gas for samples. Trained machine learning models were constructed using the Python programming language. The performance and accuracy of the machine learning models were tested and compared their results based on four different functional input datasets. The result of this study found that that the DT model predicted the gas viscosity with higher accuracy, and gave very good results better than other models based on input parameters of the dataset (A) and (B). This was evidenced by lower the Root mean square error (0.000832), lower mean absolute percent relative error (0.042%), and higher coefficient of determination (R2=0.9465).\u0000 The proposed approach in the present study provides an accurate and inexpensive model for estimating the viscosity of gases as a function of all input parameters of the dataset (A). Overall, the relative effects of these different input parameters have verified that the gas viscosity has the uppermost relevant to the gas density and specific gravity that have the highest percentage of 51%.","PeriodicalId":10904,"journal":{"name":"Day 2 Tue, October 19, 2021","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82693144","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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