Behruz Shaker Shiran, K. Djurhuus, E. Alagic, A. Lohne, T. A. Rolfsvåg, Harald Syse, S. Riisøen
As oil is produced from a reservoir, the free-water-level (FWL) rises. Monitoring the FWL during oil production is of high value for the operators. This knowledge can aid placement of new wells on the field, improve the production strategy on a well level and reduce the production of water. We propose a new method for continuously measuring in-situ water pressure in an oil reservoir and investigate, both experimentally and by simulations, how this information can be used in reservoir monitoring. Laboratory experiments with Berea sandstone and Mons chalk core samples were performed using mineral oil and synthetic brine in a test setup designed for this study. The pressure in the water phase is measured with hydrophilic probes at five locations on the core during drainage and imbibition processes. Data including temperatures, pressures, resistance, water production, and pump logs were continuously collected in a cloud solution for live monitoring during the experiments. The experimental results were interpreted using a numerical simulator (IORCoreSim) to identify key mechanisms behind probe response and upscaling to reservoir scale. A new setup with 5 internal pressure probes for measuring in-situ water pressure with higher oil pressure was successfully designed and tested. An advanced watering system to inject water to the probe tips was included in the test setup and can be operated automatically. Experimental results showed that the water-wet probes can measure low water pressure inside high pressure oil column. The change in water pressure during drainage of low permeable Mons core and medium permeability Berea core was continuously measured. The probes were able to measure water pressure in different sections of the core with change of water saturation in the core. After the drainage process, the water pressure at one side of the core was increased. The propagation of water pressure at low water saturations were then detected in the 5 probes along the core sample. This paper presents a revolutionary technique to measure pressure in a thin film of water with low mobility. Continuous monitoring of water pressure inside the hydrocarbon phase can be used to enhance the production on a well level and improve the strategy on a field level. This results in increased production, reduced operational costs and environmental impacts.
{"title":"Continuous Monitoring of Water Pressure Change in an Oil Reservoir","authors":"Behruz Shaker Shiran, K. Djurhuus, E. Alagic, A. Lohne, T. A. Rolfsvåg, Harald Syse, S. Riisøen","doi":"10.2118/214370-ms","DOIUrl":"https://doi.org/10.2118/214370-ms","url":null,"abstract":"\u0000 As oil is produced from a reservoir, the free-water-level (FWL) rises. Monitoring the FWL during oil production is of high value for the operators. This knowledge can aid placement of new wells on the field, improve the production strategy on a well level and reduce the production of water. We propose a new method for continuously measuring in-situ water pressure in an oil reservoir and investigate, both experimentally and by simulations, how this information can be used in reservoir monitoring.\u0000 Laboratory experiments with Berea sandstone and Mons chalk core samples were performed using mineral oil and synthetic brine in a test setup designed for this study. The pressure in the water phase is measured with hydrophilic probes at five locations on the core during drainage and imbibition processes. Data including temperatures, pressures, resistance, water production, and pump logs were continuously collected in a cloud solution for live monitoring during the experiments. The experimental results were interpreted using a numerical simulator (IORCoreSim) to identify key mechanisms behind probe response and upscaling to reservoir scale.\u0000 A new setup with 5 internal pressure probes for measuring in-situ water pressure with higher oil pressure was successfully designed and tested. An advanced watering system to inject water to the probe tips was included in the test setup and can be operated automatically. Experimental results showed that the water-wet probes can measure low water pressure inside high pressure oil column. The change in water pressure during drainage of low permeable Mons core and medium permeability Berea core was continuously measured. The probes were able to measure water pressure in different sections of the core with change of water saturation in the core. After the drainage process, the water pressure at one side of the core was increased. The propagation of water pressure at low water saturations were then detected in the 5 probes along the core sample.\u0000 This paper presents a revolutionary technique to measure pressure in a thin film of water with low mobility. Continuous monitoring of water pressure inside the hydrocarbon phase can be used to enhance the production on a well level and improve the strategy on a field level. This results in increased production, reduced operational costs and environmental impacts.","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130058382","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}
Hamood Al-Hajri, M. Al-Sawafi, Abdulaziz R. Al-Hashimi, Khalsa Al-Hadidi, Osama M. Al-Kindi, Mohammed Al-Amri, M. Al-Abri, S. Al-hinai
Water and chemical EOR are the main secondary recovery mechanisms in many heavy oil fields in Oman. The development concept during EOR phase is through intense infill drilling with narrow well spacing. Field-M is currently under secondary recovery phase with both water and chemical EOR (Polymer) development. During this phase, water production increases significantly and all undesired water is being disposed through disposal wells. This increases carbon intensity as disposal process generates CO2 emissions with no additional benefit, which considered as uneconomical emissions. Due to increased amount of produced water during this phase, water handling capacity (including water disposal) was fully utilized to maximize oil production from this field. Creative solutions were certainly needed reduce uneconomical water disposal and increase oil gain. As per the field development, certain pre-defined polymer dosage need to be mixed with treated produced water to achieve a viscosity of around 15 cp to ensure effectiveness of chemical EOR. Field-M injection strategy was suggested to be under controlled fracture condition to maximize throughput. In controlled fracture injection environment, monitoring fracture propagation is very important as it can cause direct interference with producers leading to injection fluid short circuiting. Fracture propagation can be determined using pressure fall off test. In addition, water quality must be monitored regularly as it plays a major role in fracture propagation. Effective surveillance and sampling plan was generated and implemented to ensure to ensure effectiveness of the polymer injection and to capture any opportunities related to increasing injection within the field. The analytical work showed that fracture propagation is a function of injection pressure, injection rate, fluid properties (in this case produced water quality and polymer quality) and in-situ stresses. Most of this parameters are controls though effective surveillance, metering & sampling. However in-situ stress condition is dynamic as the reservoir pressure keeps changing based on dynamic changes in injection and offtake. Thus, fracture propagation was monitored carefully through periodic temperature surveys and pressure fall off test to identify opportunities to optimize injection in some of the injectors. The findings from these activities enabled increasing injection rate up to 30% in some of the injection patterns. This optimization provided additional sink for the produced water reducing water disposal and uneconomical CO2 emissions by at least 5%. This is considered this as the first step toward zero water disposal goal. In addition increasing injection in these patterns resulted in significant increase in oil gain associated with polymer injection peaking to maximum of 42% in some of the injector/producers patterns. The effective use of surveillance data was key enabler to achieve ultimate goal of increasing polymer injection and re
{"title":"Successful Additional Carbon Intensity Reduction and Oil Gain through Polymer Injection Optimization in Heavy Oil Field in the South of Oman","authors":"Hamood Al-Hajri, M. Al-Sawafi, Abdulaziz R. Al-Hashimi, Khalsa Al-Hadidi, Osama M. Al-Kindi, Mohammed Al-Amri, M. Al-Abri, S. Al-hinai","doi":"10.2118/214364-ms","DOIUrl":"https://doi.org/10.2118/214364-ms","url":null,"abstract":"\u0000 Water and chemical EOR are the main secondary recovery mechanisms in many heavy oil fields in Oman. The development concept during EOR phase is through intense infill drilling with narrow well spacing. Field-M is currently under secondary recovery phase with both water and chemical EOR (Polymer) development. During this phase, water production increases significantly and all undesired water is being disposed through disposal wells. This increases carbon intensity as disposal process generates CO2 emissions with no additional benefit, which considered as uneconomical emissions.\u0000 Due to increased amount of produced water during this phase, water handling capacity (including water disposal) was fully utilized to maximize oil production from this field. Creative solutions were certainly needed reduce uneconomical water disposal and increase oil gain. As per the field development, certain pre-defined polymer dosage need to be mixed with treated produced water to achieve a viscosity of around 15 cp to ensure effectiveness of chemical EOR. Field-M injection strategy was suggested to be under controlled fracture condition to maximize throughput. In controlled fracture injection environment, monitoring fracture propagation is very important as it can cause direct interference with producers leading to injection fluid short circuiting. Fracture propagation can be determined using pressure fall off test. In addition, water quality must be monitored regularly as it plays a major role in fracture propagation. Effective surveillance and sampling plan was generated and implemented to ensure to ensure effectiveness of the polymer injection and to capture any opportunities related to increasing injection within the field.\u0000 The analytical work showed that fracture propagation is a function of injection pressure, injection rate, fluid properties (in this case produced water quality and polymer quality) and in-situ stresses. Most of this parameters are controls though effective surveillance, metering & sampling. However in-situ stress condition is dynamic as the reservoir pressure keeps changing based on dynamic changes in injection and offtake. Thus, fracture propagation was monitored carefully through periodic temperature surveys and pressure fall off test to identify opportunities to optimize injection in some of the injectors. The findings from these activities enabled increasing injection rate up to 30% in some of the injection patterns. This optimization provided additional sink for the produced water reducing water disposal and uneconomical CO2 emissions by at least 5%. This is considered this as the first step toward zero water disposal goal. In addition increasing injection in these patterns resulted in significant increase in oil gain associated with polymer injection peaking to maximum of 42% in some of the injector/producers patterns.\u0000 The effective use of surveillance data was key enabler to achieve ultimate goal of increasing polymer injection and re","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"2013 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128224794","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}
F. Anifowose, M. Mezghani, Saleh Badawood, Javed Ismail
In our previous study, we presented the preliminary results of the first attempt to predict reservoir rock porosity from advanced mud gas (AMG) data within the wellbore. The objective was to investigate the feasibility of generating a porosity log while drilling prior to wireline logging and core description processes. Knowing that porosity remains a critical property of petroleum reservoirs, this work improves on the previous research to predict porosity within a field. The methodology leveraged the machine learning (ML) paradigm in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 15,000 data points collected from representative wells in a field were used to prove the possibility of predicting the missing porosity in a well within the field. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The dataset was randomly split into training and validation subsets in 70:30 ratio simulating the complete and missing sections respectively. Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model consistently outperformed the others. In one of the test cases, the RF model gave a correlation coefficient (R-Squared) value of 0.84 compared to 0.46, and 0.78 for ANN and DT models respectively. The RF model also has a mean squared error (MSE) of 0.001 compared to 0.02 and 0.01 respectively for ANN and DT models. Having showed in a previous publication that a multivariate linear regression model could not handle the complexity in the relationship between porosity and the flare gas components, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. It can be deduced that the ML approach to predicting reservoir rock porosity from advanced mud gas data is feasible and better results are achievable with more research. This study has confirmed the feasibility of predicting porosity at the field scale and the huge benefit in utilizing AMG data beyond the traditional fluid typing and petrophysical correlation processes. The presented approach has the capability to complement existing reservoir characterization processes in assessing reservoir quality at the early stage of exploration. Future work will investigate the impact of integrating the AMG with surface drilling parameters to possibly increase the prediction accuracy.
{"title":"A Field-Scale Real-Time Prediction of Reservoir Porosity from Advanced Mud Gas Data","authors":"F. Anifowose, M. Mezghani, Saleh Badawood, Javed Ismail","doi":"10.2118/214398-ms","DOIUrl":"https://doi.org/10.2118/214398-ms","url":null,"abstract":"\u0000 In our previous study, we presented the preliminary results of the first attempt to predict reservoir rock porosity from advanced mud gas (AMG) data within the wellbore. The objective was to investigate the feasibility of generating a porosity log while drilling prior to wireline logging and core description processes. Knowing that porosity remains a critical property of petroleum reservoirs, this work improves on the previous research to predict porosity within a field.\u0000 The methodology leveraged the machine learning (ML) paradigm in the absence of established physical relationship between AMG data, comprising light and heavy flare gas components, and reservoir rock porosity. More than 15,000 data points collected from representative wells in a field were used to prove the possibility of predicting the missing porosity in a well within the field. Optimized models of artificial neural network (ANN), decision trees (DT) and random forest (RF) were applied to the combined dataset. The dataset was randomly split into training and validation subsets in 70:30 ratio simulating the complete and missing sections respectively.\u0000 Comparing the results of the ANN, DT, and RF models using statistical model performance evaluation metrics, the RF model consistently outperformed the others. In one of the test cases, the RF model gave a correlation coefficient (R-Squared) value of 0.84 compared to 0.46, and 0.78 for ANN and DT models respectively. The RF model also has a mean squared error (MSE) of 0.001 compared to 0.02 and 0.01 respectively for ANN and DT models. Having showed in a previous publication that a multivariate linear regression model could not handle the complexity in the relationship between porosity and the flare gas components, these results have further confirmed the robustness of nonlinear solutions based on the ML methodology. It can be deduced that the ML approach to predicting reservoir rock porosity from advanced mud gas data is feasible and better results are achievable with more research.\u0000 This study has confirmed the feasibility of predicting porosity at the field scale and the huge benefit in utilizing AMG data beyond the traditional fluid typing and petrophysical correlation processes. The presented approach has the capability to complement existing reservoir characterization processes in assessing reservoir quality at the early stage of exploration. Future work will investigate the impact of integrating the AMG with surface drilling parameters to possibly increase the prediction accuracy.","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126394131","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}
It is very difficult to realize good economy returns using conventional SAGD process in many oil sands projects due to large CPF investment, massive steam injection, expensive surface diluent adding and increasing carbon emission tax. By contrast, warm solvent assisted gravity drainage process (WSAGD) is a promising low-carbon technology to deal with these SAGD challenges. This paper conducted feasibility evaluation by combined with Nsolv Best pilot analysis and a series of physical simulations. From 2014 to 2017, WSAGD pilot was successfully carried out by injecting butane at 60℃ in Suncor Dover oil sands. Its reservoir geological characteristics, physical properties, development technology policy and production performance were systematically analyzed. Combined with 4D seismic interpretation, RST and observation well data, the size and growth rate of solvent chamber were monitored and analyzed. Considering great uncertainty in numerical simulations influenced by many factors including grid size, solvent diffusion coefficient, interfacial tension and capillary force, a series of experimental tests and physical simulations were conducted. The behavior of viscosity reduction, interfacial tension reduction and microscopic oil displacement related to different solvents were systematically tested including propane, butane, pentane and hexane. Particularly, the performance of SAGD and WSAGD process were evaluated by 2D and 3D visual physical simulations. In Nsolv Best pilot, the target reservoir is low pressure, thin and shallow buried. The oil rate reached 250-300 barrels per day under 300 m horizontal section, and API degree of produced oil was upgraded to 13-16 from original 8. After 3 years of tests, the width of solvent chamber is 40-60m, lateral and vertical 1.56 m and 0.96 m per month, and horizontal conformance is 67%. The experiments results show that viscosity reduction trend will flatten out when the solvent concentration exceeds 10 vol% due to partial asphaltene precipitation. Both sweep efficiency and displacement efficiency of hot water, steam, gaseous and liquid hexane are increasing with temperature increase. Compared with other medium, sweep efficiency and displacement efficiency of gaseous hexane are higher due to greater dissolving ability and speed in bitumen. Both 2D and 3D experimental results indicate that WSAGD process achieves faster vertical solvent chamber and higher recovery factor than conventional SAGD process. Besides, gaseous pentane has significant upgrading effect considering substantial reduction of asphaltene and resin in the produced oil, which is not available in conventional SAGD process. This paper first systematically compares the mechanisms and performance of warm solvent assisted gravity drainage (WSAGD) process with SAGD process by physical simulations. It presents a promising low-carbon technology to enhance oil recovery, partially upgrade the produced oil and reduce carbon dioxide emissions in develop
{"title":"Feasibility Evaluation of Warm Solvent Assisted Gravity Drainage Process in Low-Carbon Developing Super-Heavy Oil or Oil Sands Project","authors":"Guangyue Liang, Qian Xie, Y. Liu, Shangqi Liu, Zhaohui Xia, Yu Bao, Jiuning Zhou","doi":"10.2118/214347-ms","DOIUrl":"https://doi.org/10.2118/214347-ms","url":null,"abstract":"\u0000 It is very difficult to realize good economy returns using conventional SAGD process in many oil sands projects due to large CPF investment, massive steam injection, expensive surface diluent adding and increasing carbon emission tax. By contrast, warm solvent assisted gravity drainage process (WSAGD) is a promising low-carbon technology to deal with these SAGD challenges. This paper conducted feasibility evaluation by combined with Nsolv Best pilot analysis and a series of physical simulations.\u0000 From 2014 to 2017, WSAGD pilot was successfully carried out by injecting butane at 60℃ in Suncor Dover oil sands. Its reservoir geological characteristics, physical properties, development technology policy and production performance were systematically analyzed. Combined with 4D seismic interpretation, RST and observation well data, the size and growth rate of solvent chamber were monitored and analyzed. Considering great uncertainty in numerical simulations influenced by many factors including grid size, solvent diffusion coefficient, interfacial tension and capillary force, a series of experimental tests and physical simulations were conducted. The behavior of viscosity reduction, interfacial tension reduction and microscopic oil displacement related to different solvents were systematically tested including propane, butane, pentane and hexane. Particularly, the performance of SAGD and WSAGD process were evaluated by 2D and 3D visual physical simulations.\u0000 In Nsolv Best pilot, the target reservoir is low pressure, thin and shallow buried. The oil rate reached 250-300 barrels per day under 300 m horizontal section, and API degree of produced oil was upgraded to 13-16 from original 8. After 3 years of tests, the width of solvent chamber is 40-60m, lateral and vertical 1.56 m and 0.96 m per month, and horizontal conformance is 67%. The experiments results show that viscosity reduction trend will flatten out when the solvent concentration exceeds 10 vol% due to partial asphaltene precipitation. Both sweep efficiency and displacement efficiency of hot water, steam, gaseous and liquid hexane are increasing with temperature increase. Compared with other medium, sweep efficiency and displacement efficiency of gaseous hexane are higher due to greater dissolving ability and speed in bitumen. Both 2D and 3D experimental results indicate that WSAGD process achieves faster vertical solvent chamber and higher recovery factor than conventional SAGD process. Besides, gaseous pentane has significant upgrading effect considering substantial reduction of asphaltene and resin in the produced oil, which is not available in conventional SAGD process.\u0000 This paper first systematically compares the mechanisms and performance of warm solvent assisted gravity drainage (WSAGD) process with SAGD process by physical simulations. It presents a promising low-carbon technology to enhance oil recovery, partially upgrade the produced oil and reduce carbon dioxide emissions in develop","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"78 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115397259","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}
A. Cely, Artur Siedlecki, A. Liashenko, Tao Yang, S. Donnadieu
Standard mud gas data is part of the basic mudlogging service and is used mainly for safety monitoring. Although the data is available for all wells, it is not used for reservoir fluid typing due to poor prediction accuracy. We recently developed a new manual method and significantly improved the reservoir fluid typing accuracy from standard mud gas data. However, there is a strong business for an automatic method to enable reservoir fluid interpretation while drilling. A machine learning method has been developed based on a well-established standard mud gas database. The standard mud gas compositions contain methane, ethane, and propane components with reasonable quality measurements. The butane and pentane compositions in the standard mud gas are low and sometimes close to the detection limit. Therefore, we only use methane to propane compositions in the machine learning algorithm. It is particularly challenging to predict reservoir fluid type accurately based on only three gas components. Therefore, we introduce additional data sources to increase the prediction accuracy: a large in-house reservoir fluid database and petrophysical logs. The machine learning algorithm extracts critical reservoir fluid information specifically for a known field by utilizing the geospatial location and the existing reservoir fluid database. When combined with the standard mud gas database, the reservoir fluid typing accuracy increased from 50-60% to nearly 80%. Petrophysical logs are the main tool in the industry to identify the reservoir fluid type. When combining the petrophysical logs with the machine learning model already with satisfactory performance, the final reservoir fluid type prediction accuracy is about 80%. Given the difficulties of distinguishing oil or gas for near-critical fluids or volatile oil, the current prediction accuracy is sufficient for industry applications. The innovation created significant business opportunities based on the standard mud gas, which has been regarded as not applicable data for accurate reservoir fluid typing for many decades. The new method makes accurate reservoir fluid typing possible for real-time well decisions like well placement, completion, and sidetracking. In addition, the new method can add lots of value for well integrity, maturating production targets, and cost-efficient Plug and Abandonment (P&A) in the overburden.
{"title":"Reservoir Fluid Typing from Standard Mud Gas - A Machine Learning Approach","authors":"A. Cely, Artur Siedlecki, A. Liashenko, Tao Yang, S. Donnadieu","doi":"10.2118/214341-ms","DOIUrl":"https://doi.org/10.2118/214341-ms","url":null,"abstract":"\u0000 Standard mud gas data is part of the basic mudlogging service and is used mainly for safety monitoring. Although the data is available for all wells, it is not used for reservoir fluid typing due to poor prediction accuracy. We recently developed a new manual method and significantly improved the reservoir fluid typing accuracy from standard mud gas data. However, there is a strong business for an automatic method to enable reservoir fluid interpretation while drilling.\u0000 A machine learning method has been developed based on a well-established standard mud gas database. The standard mud gas compositions contain methane, ethane, and propane components with reasonable quality measurements. The butane and pentane compositions in the standard mud gas are low and sometimes close to the detection limit. Therefore, we only use methane to propane compositions in the machine learning algorithm. It is particularly challenging to predict reservoir fluid type accurately based on only three gas components. Therefore, we introduce additional data sources to increase the prediction accuracy: a large in-house reservoir fluid database and petrophysical logs.\u0000 The machine learning algorithm extracts critical reservoir fluid information specifically for a known field by utilizing the geospatial location and the existing reservoir fluid database. When combined with the standard mud gas database, the reservoir fluid typing accuracy increased from 50-60% to nearly 80%. Petrophysical logs are the main tool in the industry to identify the reservoir fluid type. When combining the petrophysical logs with the machine learning model already with satisfactory performance, the final reservoir fluid type prediction accuracy is about 80%. Given the difficulties of distinguishing oil or gas for near-critical fluids or volatile oil, the current prediction accuracy is sufficient for industry applications.\u0000 The innovation created significant business opportunities based on the standard mud gas, which has been regarded as not applicable data for accurate reservoir fluid typing for many decades. The new method makes accurate reservoir fluid typing possible for real-time well decisions like well placement, completion, and sidetracking. In addition, the new method can add lots of value for well integrity, maturating production targets, and cost-efficient Plug and Abandonment (P&A) in the overburden.","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124233469","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}
Maira Alves Fortunato, S. Békri, D. Rousseau, Tiphaine Courtaud, N. Wartenberg
Designing chemical EOR processes requires reservoir simulations that need to be backed by a good understanding of the mechanisms at play when injecting surfactant-based solutions in porous media. One of the main challenges is that laboratory coreflood tests often show early surfactant breakthroughs that cannot be easily history matched. Indeed, contrary to polymer macromolecules, smaller surfactant molecules are not supposed to experience the inaccessible pore volume (IPV) effect. The study's aim was to determine if, in surfactant-polymer flooding, the polymer could influence the transport of the surfactant in such a way that it would not be able to invade a fraction of the pore space. To that end, two multi-steps coreflood tests were performed with cores of outcrop rock in conditions representative of a reference field case. In the first test, the surfactant was injected without polymer and then, after a brine injection flush, with polymer. In the second test, the surfactant was directly injected with polymer. For both tests, in order to bypass the adsorption effect, the surfactant injected volumes at breakthrough were determined on rocks having their surface already fully saturated by surfactant. Namely, a first surfactant slug was injected in order to fulfill maximum rock adsorption capacity, then, immediately after, a second at a higher concentration of which the breakthrough was potentially influenced by IPV only. The polymer IPV were estimated by the conventional two-slugs method. In the first test, the result showed that, without polymer, the surfactant accessed all of the pore volume of the core while, in presence of polymer, the surfactant could not access about 2% of the pore volume, which corresponded to the polymer IPV. In the second test, the surfactant was not able to access 12% of the pore volume, which also corresponded to the polymer IPV. These outcomes stand as evidence that the presence of polymer impacts the transport of surfactant, leading it to experience an "apparent" surfactant IPV effect equal to the polymer's one. This suggests that interactions between polymer and surfactant molecules take place at the pore level. This study illustrates that surfactant transport properties in reservoirs can be more complex than conventionally accounted for in dynamic reservoir simulation. As history-matching of the coreflood essays is needed to build a representative dataset for surfactant-based EOR processes, improvements of the simulation software appear required for cases where IPV cannot be neglected.
{"title":"Transport of EOR Surfactant in Reservoirs: Impact of Polymer on Apparent Surfactant Inaccessible Pore Volume","authors":"Maira Alves Fortunato, S. Békri, D. Rousseau, Tiphaine Courtaud, N. Wartenberg","doi":"10.2118/214411-ms","DOIUrl":"https://doi.org/10.2118/214411-ms","url":null,"abstract":"\u0000 Designing chemical EOR processes requires reservoir simulations that need to be backed by a good understanding of the mechanisms at play when injecting surfactant-based solutions in porous media. One of the main challenges is that laboratory coreflood tests often show early surfactant breakthroughs that cannot be easily history matched. Indeed, contrary to polymer macromolecules, smaller surfactant molecules are not supposed to experience the inaccessible pore volume (IPV) effect.\u0000 The study's aim was to determine if, in surfactant-polymer flooding, the polymer could influence the transport of the surfactant in such a way that it would not be able to invade a fraction of the pore space. To that end, two multi-steps coreflood tests were performed with cores of outcrop rock in conditions representative of a reference field case. In the first test, the surfactant was injected without polymer and then, after a brine injection flush, with polymer. In the second test, the surfactant was directly injected with polymer.\u0000 For both tests, in order to bypass the adsorption effect, the surfactant injected volumes at breakthrough were determined on rocks having their surface already fully saturated by surfactant. Namely, a first surfactant slug was injected in order to fulfill maximum rock adsorption capacity, then, immediately after, a second at a higher concentration of which the breakthrough was potentially influenced by IPV only. The polymer IPV were estimated by the conventional two-slugs method.\u0000 In the first test, the result showed that, without polymer, the surfactant accessed all of the pore volume of the core while, in presence of polymer, the surfactant could not access about 2% of the pore volume, which corresponded to the polymer IPV. In the second test, the surfactant was not able to access 12% of the pore volume, which also corresponded to the polymer IPV. These outcomes stand as evidence that the presence of polymer impacts the transport of surfactant, leading it to experience an \"apparent\" surfactant IPV effect equal to the polymer's one. This suggests that interactions between polymer and surfactant molecules take place at the pore level.\u0000 This study illustrates that surfactant transport properties in reservoirs can be more complex than conventionally accounted for in dynamic reservoir simulation. As history-matching of the coreflood essays is needed to build a representative dataset for surfactant-based EOR processes, improvements of the simulation software appear required for cases where IPV cannot be neglected.","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"41 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123321243","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}
Osama Mohammed Al Kindi, Suleiman Al Hinai, Hilal Ghefeili, Marwan Al Sawafi, Mohammed Abri, V. Hugonet, Taher Ghailani, Raied Dabbagh
The Net Zero pathway is critical for the sustainability and life quality on Earth, yet the decarbonization efforts will not come for free. In this paper, the in-depth investigation of all the applicable decarbonization levers for Asset-M has been investigated. Worth to mention that Asset-M is the second Largest Asset for Oil production in Petroleum Development Oman (PDO), which is located to the East region of Dhofar in the South of the Sultanate of Oman. The work presented here will illustrate the decarbonization cost for the different projects from a qualitative screening point of view. PDO as the main oil and gas producer in the Sultanate of Oman has pledged to reduce its Source 1 & 2 emissions by 50% in 2030 and to achieve net zero emissions by 2050. A mission that is not only difficult with the current available technologies but also very expensive and require a lot of funding and collaboration between the different research and governmental entities. The first step in this decarbonization exercise was to pinpoint the sources of emissions, for Asset M these are mainly characterized in Flaring, Power consumption, Fuel gas for crude processing and other emissions associated to the infrastructure such as stationary combustion, transportation and fugitives. A benchmark exercise was conducted to understand the cost of the different technologies capable to decarbonize Asset-M based on the different sources available. A Marginal Abatement Cost Curve (MACC) analysis was used to screen the different decarbonization levers from a comparison point of view. The analysis does illustrate options with viable commerciality yet for those options which appear noneconomical it does highlight the cost of Carbon per ton needed for the projects to fly either through government tax credit or other type of subsides. It is clear from the MACC analysis conducted based on global benchmark data of Renewables, batteries cost, gas and oil prices and others; that the decarbonization towards net zero emission will not come for free. Billions of Dollars will have to be spent for two main good reasons: The technology cost is still high due to the current level of maturity and scale (e.g. CCUS, Hydrogen, Renewables, batteries and more) Carbon is still not taxed in many countries and hence the attitude of the Oil and Gas industry is yet to pick up the momentum and urgency to accelerate new technologies trials which will help in unlocking more sustainable but economical solutions for decarbonization. The information presented here will be published for the first time specially when it comes to the potential of Carbon cost escalation if net Zero emission pathway is mandated, and under any circumstance, Decarbonization will not come for Free.
{"title":"Decarbonization Will Not Come for Free: Asset-M Marginal Abatement Cost Curve","authors":"Osama Mohammed Al Kindi, Suleiman Al Hinai, Hilal Ghefeili, Marwan Al Sawafi, Mohammed Abri, V. Hugonet, Taher Ghailani, Raied Dabbagh","doi":"10.2118/214414-ms","DOIUrl":"https://doi.org/10.2118/214414-ms","url":null,"abstract":"\u0000 The Net Zero pathway is critical for the sustainability and life quality on Earth, yet the decarbonization efforts will not come for free. In this paper, the in-depth investigation of all the applicable decarbonization levers for Asset-M has been investigated. Worth to mention that Asset-M is the second Largest Asset for Oil production in Petroleum Development Oman (PDO), which is located to the East region of Dhofar in the South of the Sultanate of Oman. The work presented here will illustrate the decarbonization cost for the different projects from a qualitative screening point of view.\u0000 PDO as the main oil and gas producer in the Sultanate of Oman has pledged to reduce its Source 1 & 2 emissions by 50% in 2030 and to achieve net zero emissions by 2050. A mission that is not only difficult with the current available technologies but also very expensive and require a lot of funding and collaboration between the different research and governmental entities.\u0000 The first step in this decarbonization exercise was to pinpoint the sources of emissions, for Asset M these are mainly characterized in Flaring, Power consumption, Fuel gas for crude processing and other emissions associated to the infrastructure such as stationary combustion, transportation and fugitives. A benchmark exercise was conducted to understand the cost of the different technologies capable to decarbonize Asset-M based on the different sources available.\u0000 A Marginal Abatement Cost Curve (MACC) analysis was used to screen the different decarbonization levers from a comparison point of view. The analysis does illustrate options with viable commerciality yet for those options which appear noneconomical it does highlight the cost of Carbon per ton needed for the projects to fly either through government tax credit or other type of subsides.\u0000 It is clear from the MACC analysis conducted based on global benchmark data of Renewables, batteries cost, gas and oil prices and others; that the decarbonization towards net zero emission will not come for free. Billions of Dollars will have to be spent for two main good reasons:\u0000 The technology cost is still high due to the current level of maturity and scale (e.g. CCUS, Hydrogen, Renewables, batteries and more) Carbon is still not taxed in many countries and hence the attitude of the Oil and Gas industry is yet to pick up the momentum and urgency to accelerate new technologies trials which will help in unlocking more sustainable but economical solutions for decarbonization.\u0000 The information presented here will be published for the first time specially when it comes to the potential of Carbon cost escalation if net Zero emission pathway is mandated, and under any circumstance, Decarbonization will not come for Free.","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"76 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114219129","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}
A. E. Legarreta, Rosina Cristina Barberis, F. Schein, L. Martino, S. Gandi
Survivorship bias is a well-known tendency to overweight available data and underestimate the missing information. Cañadón León in San Jorge basin, Argentina is a waterflooded field with a current water-cut of 95% where innovative recovery strategies such as Chemical Enhanced Oil Recovery (cEOR) become a condition for further development. Data acquisition is often biased towards the best reservoirs, leading to major uncertainty in assessing opportunities in mature fields. After 70 years of primary oil production and water injection, the study aims to evaluate the remaining opportunity, which leads to a double challenge: Estimation of bypassed oil during the inefficient waterflooding process because of poor mobility ratio and the potential of marginal reservoirs. Initial stage field exploitation and data acquisition at early stages of development aimed mainly to characterize the higher oil-saturation zones with better petrophysical properties, leading to a lack of data on marginal reservoirs which become critical targets for mature reservoirs analysis. The data interpretation within a semi regional geological framework to build the static model, allowed a representative construction of poorly characterized reservoirs due to survivorship bias effect. Several hypotheses were evaluated with dynamic simulation to avoid assuming recoverable oil based on survivorship bias due to missing information in secondary targets. Integration of what-if scenarios, both static and dynamic, and assessment of uncertainty provided a better understanding of critical constraints and optimum ranges of variability to analyze cEOR with polymer injection. A wide variety of fluid saturation scenarios, mobility ratios and reservoir properties were considered to quantify the field potential. Sensitivity analysis helped to identify the most relevant uncertainties in history matching and reliability in forecast: Primary gas cap contact and its expansion, water-oil contact, the transition zone (oil-water system), fluid mobility ratios and polymer characteristics. A major benefit from polymer injection is CO2 emissions reduction per barrel of oil by more than 40% compared to water injection, reducing project carbon footprint. Development strategy achieves a short-term incremental recovery factor of 10% with a total of 68 wells in 20 injection patterns (considering a period between 3 to 6 years due to oil production acceleration). This methodology allowed to establish the foundations for development strategies based on multi-modelling within conceptual geological frameworks reflecting the impact of the recognized uncertainties. This technique does not allow to determine the unknowns, but it does allow to estimate their impact.
{"title":"Opportunities and Uncertainty Mitigation Base on Survivor Bias in a Mature Field: Cañadón León, San Jorge Basin, Argentina","authors":"A. E. Legarreta, Rosina Cristina Barberis, F. Schein, L. Martino, S. Gandi","doi":"10.2118/214355-ms","DOIUrl":"https://doi.org/10.2118/214355-ms","url":null,"abstract":"\u0000 Survivorship bias is a well-known tendency to overweight available data and underestimate the missing information. Cañadón León in San Jorge basin, Argentina is a waterflooded field with a current water-cut of 95% where innovative recovery strategies such as Chemical Enhanced Oil Recovery (cEOR) become a condition for further development. Data acquisition is often biased towards the best reservoirs, leading to major uncertainty in assessing opportunities in mature fields.\u0000 After 70 years of primary oil production and water injection, the study aims to evaluate the remaining opportunity, which leads to a double challenge: Estimation of bypassed oil during the inefficient waterflooding process because of poor mobility ratio and the potential of marginal reservoirs. Initial stage field exploitation and data acquisition at early stages of development aimed mainly to characterize the higher oil-saturation zones with better petrophysical properties, leading to a lack of data on marginal reservoirs which become critical targets for mature reservoirs analysis. The data interpretation within a semi regional geological framework to build the static model, allowed a representative construction of poorly characterized reservoirs due to survivorship bias effect.\u0000 Several hypotheses were evaluated with dynamic simulation to avoid assuming recoverable oil based on survivorship bias due to missing information in secondary targets. Integration of what-if scenarios, both static and dynamic, and assessment of uncertainty provided a better understanding of critical constraints and optimum ranges of variability to analyze cEOR with polymer injection. A wide variety of fluid saturation scenarios, mobility ratios and reservoir properties were considered to quantify the field potential.\u0000 Sensitivity analysis helped to identify the most relevant uncertainties in history matching and reliability in forecast: Primary gas cap contact and its expansion, water-oil contact, the transition zone (oil-water system), fluid mobility ratios and polymer characteristics.\u0000 A major benefit from polymer injection is CO2 emissions reduction per barrel of oil by more than 40% compared to water injection, reducing project carbon footprint. Development strategy achieves a short-term incremental recovery factor of 10% with a total of 68 wells in 20 injection patterns (considering a period between 3 to 6 years due to oil production acceleration). This methodology allowed to establish the foundations for development strategies based on multi-modelling within conceptual geological frameworks reflecting the impact of the recognized uncertainties. This technique does not allow to determine the unknowns, but it does allow to estimate their impact.","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116811878","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}
Waldemar Szemat-Vielma, Jürgen Scheibz, Nihad Kasraoui, Faisal Al-Omar
The renewable energy sector, particularly the solar PV generation, is to play a key role in the energy transition and decarbonization process and the green hydrogen production is a subsequent element of this decarbonization process as a clean energy carrier. When power output from these renewable installations exceeds the grid requirements, instead of stopping the energy generation, that power surplus can be used to produce hydrogen by electrolysis process. Despite being a technically simple process to produce via electrolysis, fuel cost and equipment are the two most significant economical elements to consider as part of the LCOH equation and act as economical boundary conditions. Combining an in-depth analysis while applying the financial modeling toolbox, this project has evaluated specific conditions for solar PV installations in Morocco and Saudi Arabia markets in terms of a techno-economic analysis for a potential investment for green hydrogen production in 2021 as well as near future projections in 2023 and 2025. The most potential application of green hydrogen production and usage is to decarbonize heavy industries (e.g., cement and steel) that cannot be electrified but this will require an extensive transport infrastructure with low-cost incidence for the green hydrogen to be an economically viable solution. Near future projects will require public funding in the form of grants or tax redemption to scale up to economical maturity. After carrying out a detailed financial modeling and a discounted cash flow valuation model, the resulting LCOH for Morocco is $3,2695/kg while Saudi is $1,5757/kg as of the end of 2021 with a projected reduction to reach $2,3678/kg and $1,4417/kg respectively in 2025, which means that by 2025 both countries will be below the $1,5-2,5/kg green hydrogen threshold, on a competitive level with fossil fuels, enabling both countries to grasp unique commercial opportunities to lead the implementation of a green business models towards a hydrogen economy, and eventually a net zero world. The paper will elaborate on the rational driving the need for green hydrogen, will elaborate on the geopolitical framework supporting this emerging business and dives in with the techno-economic analysis while creating a 2023-2025 look-ahead.
{"title":"Sun Powered Green Hydrogen - A Comparative Analysis from the Kingdoms Of Morocco and Saudi Arabia","authors":"Waldemar Szemat-Vielma, Jürgen Scheibz, Nihad Kasraoui, Faisal Al-Omar","doi":"10.2118/214375-ms","DOIUrl":"https://doi.org/10.2118/214375-ms","url":null,"abstract":"\u0000 The renewable energy sector, particularly the solar PV generation, is to play a key role in the energy transition and decarbonization process and the green hydrogen production is a subsequent element of this decarbonization process as a clean energy carrier. When power output from these renewable installations exceeds the grid requirements, instead of stopping the energy generation, that power surplus can be used to produce hydrogen by electrolysis process.\u0000 Despite being a technically simple process to produce via electrolysis, fuel cost and equipment are the two most significant economical elements to consider as part of the LCOH equation and act as economical boundary conditions. Combining an in-depth analysis while applying the financial modeling toolbox, this project has evaluated specific conditions for solar PV installations in Morocco and Saudi Arabia markets in terms of a techno-economic analysis for a potential investment for green hydrogen production in 2021 as well as near future projections in 2023 and 2025.\u0000 The most potential application of green hydrogen production and usage is to decarbonize heavy industries (e.g., cement and steel) that cannot be electrified but this will require an extensive transport infrastructure with low-cost incidence for the green hydrogen to be an economically viable solution. Near future projects will require public funding in the form of grants or tax redemption to scale up to economical maturity.\u0000 After carrying out a detailed financial modeling and a discounted cash flow valuation model, the resulting LCOH for Morocco is $3,2695/kg while Saudi is $1,5757/kg as of the end of 2021 with a projected reduction to reach $2,3678/kg and $1,4417/kg respectively in 2025, which means that by 2025 both countries will be below the $1,5-2,5/kg green hydrogen threshold, on a competitive level with fossil fuels, enabling both countries to grasp unique commercial opportunities to lead the implementation of a green business models towards a hydrogen economy, and eventually a net zero world.\u0000 The paper will elaborate on the rational driving the need for green hydrogen, will elaborate on the geopolitical framework supporting this emerging business and dives in with the techno-economic analysis while creating a 2023-2025 look-ahead.","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"150 11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125883081","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}
A. Roostaei, E. Nikjoo, Ali Nadali, Ei Sheen Lau, V. Droppert
This paper describes how near real-time tracer data from the onsite tracer analysis enabled the operator in the Nova field to interactively optimize two well clean-ups to the rig. The tracers provided key information on the clean-up progress in different zones which enabled the operator to make informed and fast decisions to maximize well clean-up efficiency while minimising rig time and cost. Verification of well clean-up to increase zonal productivity and to eliminate the risk of damage to the surface production unit with minimum rig time is always a challenge during well start-up. The conventional wellbore clean-up practices are to monitor surface parameters including produced mud volume and basic sediment and water (BS&W) in the production fluids until a certain criterion for these parameters are met. However, this method cannot confirm that all the zones are cleaned up and are contributing to the production. Having the right monitoring technology to confirm well clean-up at the zonal level is therefore essential to successfully clean up the entire reservoir section. Inflow tracers with onsite analysis provide near real-time data on clean-up efficiency in different zones. Unique tracer molecules are embedded into the polymer systems and permanently installed in the lower completion. Oil and water tracers remain dormant until they come into contact with their target fluids. Once activated, the tracers are released into the target fluid for a certain designed life period and can be sampled when the well is opened. The collected samples can be analysed onsite or offsite at a laboratory. The onsite analysis can provide near real-time data and is preferred for a fast decision-making process such as during the clean-up to rig. The Nova drilling plan consisted of three oil producers (two horizontal and one slanted). The onsite tracer analysis with fast analysis turnaround time was used for the two horizontal wells. For the first horizontal well (X-3H), the tracer data results confirmed a strong heel clean-up efficiency from the very beginning and a weak toe clean-up efficiency. The middle and toe zone tracers appeared 8 and 12 hrs after opening the well respectively, therefore confirming oil contribution from all zones. Due to weak clean-up at the toe, the operator decided to prolong the clean-up at maximum drawdown to improve the clean-up of the toe section. For the second well (X-4 AHT2), the toe section exhibited effective clean-up from the very beginning while the heel zone showed a gradual clean-up and started to clean up 10 hrs after opening the well. Monitoring well performance at the zonal level without any intervention and in a cost-effective manner is a challenge, especially during the initial opening of the well to the rig. In this case, the inflow tracer technology was successfully utilized to provide near real-time validation of clean-up and flow contribution. This enabled the operator to understand his wells’ behaviour and make re
{"title":"Near Real-Time Tracer Data from the Onsite Tracer Analysis in Nova Field","authors":"A. Roostaei, E. Nikjoo, Ali Nadali, Ei Sheen Lau, V. Droppert","doi":"10.2118/214351-ms","DOIUrl":"https://doi.org/10.2118/214351-ms","url":null,"abstract":"\u0000 This paper describes how near real-time tracer data from the onsite tracer analysis enabled the operator in the Nova field to interactively optimize two well clean-ups to the rig. The tracers provided key information on the clean-up progress in different zones which enabled the operator to make informed and fast decisions to maximize well clean-up efficiency while minimising rig time and cost.\u0000 Verification of well clean-up to increase zonal productivity and to eliminate the risk of damage to the surface production unit with minimum rig time is always a challenge during well start-up. The conventional wellbore clean-up practices are to monitor surface parameters including produced mud volume and basic sediment and water (BS&W) in the production fluids until a certain criterion for these parameters are met. However, this method cannot confirm that all the zones are cleaned up and are contributing to the production. Having the right monitoring technology to confirm well clean-up at the zonal level is therefore essential to successfully clean up the entire reservoir section. Inflow tracers with onsite analysis provide near real-time data on clean-up efficiency in different zones. Unique tracer molecules are embedded into the polymer systems and permanently installed in the lower completion. Oil and water tracers remain dormant until they come into contact with their target fluids. Once activated, the tracers are released into the target fluid for a certain designed life period and can be sampled when the well is opened. The collected samples can be analysed onsite or offsite at a laboratory. The onsite analysis can provide near real-time data and is preferred for a fast decision-making process such as during the clean-up to rig.\u0000 The Nova drilling plan consisted of three oil producers (two horizontal and one slanted). The onsite tracer analysis with fast analysis turnaround time was used for the two horizontal wells. For the first horizontal well (X-3H), the tracer data results confirmed a strong heel clean-up efficiency from the very beginning and a weak toe clean-up efficiency. The middle and toe zone tracers appeared 8 and 12 hrs after opening the well respectively, therefore confirming oil contribution from all zones. Due to weak clean-up at the toe, the operator decided to prolong the clean-up at maximum drawdown to improve the clean-up of the toe section. For the second well (X-4 AHT2), the toe section exhibited effective clean-up from the very beginning while the heel zone showed a gradual clean-up and started to clean up 10 hrs after opening the well.\u0000 Monitoring well performance at the zonal level without any intervention and in a cost-effective manner is a challenge, especially during the initial opening of the well to the rig. In this case, the inflow tracer technology was successfully utilized to provide near real-time validation of clean-up and flow contribution. This enabled the operator to understand his wells’ behaviour and make re","PeriodicalId":306106,"journal":{"name":"Day 4 Thu, June 08, 2023","volume":"108 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128198302","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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