E. Illarionov, Pavel Temirchev, D. Voloskov, A. Gubanova, D. Koroteev, M. Simonov, A. Akhmetov, A. Margarit
In adaptation of reservoir models a direct gradient backpropagation through the forward model is often intractable or requires enormous computational costs. Thus one have to construct separate models that simulate them implicitly, e.g. via stochastic sampling or solving of adjoint systems. We demonstrate that if the forward model is a neural network, gradient backpropagation becomes naturally involved both in model training and adaptation. In our research we compare 3 adaptation strategies: variation of reservoir model variables, neural network adaptation and latent space adaptation and discuss to what extent they preserve the geological content. We exploit a real-world reservoir model to investigate the problem in practical case. The numerical experiments demonstrate that the latent space adaptation provides the most stable and accurate results.
{"title":"3D Reservoir Model History Matching Based on Machine Learning Technology","authors":"E. Illarionov, Pavel Temirchev, D. Voloskov, A. Gubanova, D. Koroteev, M. Simonov, A. Akhmetov, A. Margarit","doi":"10.2118/201924-ms","DOIUrl":"https://doi.org/10.2118/201924-ms","url":null,"abstract":"\u0000 In adaptation of reservoir models a direct gradient backpropagation through the forward model is often intractable or requires enormous computational costs. Thus one have to construct separate models that simulate them implicitly, e.g. via stochastic sampling or solving of adjoint systems. We demonstrate that if the forward model is a neural network, gradient backpropagation becomes naturally involved both in model training and adaptation. In our research we compare 3 adaptation strategies: variation of reservoir model variables, neural network adaptation and latent space adaptation and discuss to what extent they preserve the geological content. We exploit a real-world reservoir model to investigate the problem in practical case. The numerical experiments demonstrate that the latent space adaptation provides the most stable and accurate results.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127143516","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}
Fang Zhao, Changfeng Xi, Xialin Zhang, Xiaorong Shi, Fengxiang Yang, Hetaer Mu, Wenlong Guan, Youwei Jiang, Hongzhuang Wang, T. Babadagli, H. Li
We evaluated the performance of a field-wide post-steam in-situ combustion (ISC) project conducted in a complex heavy oil reservoir in China using laboratory one-dimensional combustion experiments, reservoir simulation outputs, and data collected from the field application. The commercial ISC project showed vastly different production performances in different regions of the field and two types of representative well groups were identified. Type I group has a low oil viscosity (<8000 mPa.s) and a high steam-flooded recovery factor (>30%); after ISC treatment, these producers show a high initial water cut, while some experience channeling issues and hence produce a large quantity of flue gas. Type II group has a high oil viscosity (>20000 mPa.s) and a low cyclic steam stimulation (CSS) recovery factor (15-20%); these producers have a high air injection pressure exceeding the fracture pressure. Corresponding remedial methods were designed and applied to these two well groups. Presently, the evaluation methods described in this paper are being applied in the field, and initial results have been acquired.
{"title":"Evaluation of a Field-Wide Post-Steam In-Situ Combustion Performance in a Heavy Oil Reservoir in China","authors":"Fang Zhao, Changfeng Xi, Xialin Zhang, Xiaorong Shi, Fengxiang Yang, Hetaer Mu, Wenlong Guan, Youwei Jiang, Hongzhuang Wang, T. Babadagli, H. Li","doi":"10.2118/201815-ms","DOIUrl":"https://doi.org/10.2118/201815-ms","url":null,"abstract":"\u0000 We evaluated the performance of a field-wide post-steam in-situ combustion (ISC) project conducted in a complex heavy oil reservoir in China using laboratory one-dimensional combustion experiments, reservoir simulation outputs, and data collected from the field application. The commercial ISC project showed vastly different production performances in different regions of the field and two types of representative well groups were identified. Type I group has a low oil viscosity (<8000 mPa.s) and a high steam-flooded recovery factor (>30%); after ISC treatment, these producers show a high initial water cut, while some experience channeling issues and hence produce a large quantity of flue gas. Type II group has a high oil viscosity (>20000 mPa.s) and a low cyclic steam stimulation (CSS) recovery factor (15-20%); these producers have a high air injection pressure exceeding the fracture pressure. Corresponding remedial methods were designed and applied to these two well groups. Presently, the evaluation methods described in this paper are being applied in the field, and initial results have been acquired.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122662666","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}
I. Denisenko, I. Kuvaev, I. Uvarov, Oleg Evgenievich Kushmantzev, Artem Igorevich Toporov
Today's oil & gas industry faces a number of different challenges. Drilling activities are ramping up due to an increase in hydrocarbon demand combined with a reduction of easy-to-recover reserves. Horizontal drilling is growing and has become an integral part of field development. The geology is becoming more and more complex requiring drilling through dense layers targeting thin-layered reservoirs with lateral changes and anisotropy. In recent years, companies have been looking at the ways of optimizing drilling costs by increasing efficiency and process automation. This has been a driver for many companies to stay profitable and efficient in the market. One of the areas of interest for process automation has been a geosteering. Geosteering is the real-time adjustment well trajectory while drilling to maximize effective footage in the target zone. In this paper, innovative new approaches to automation of the geosteering process will be discussed. This approach has been successfully tested and deployed in several leading O&G companies. The main objective of automated geosteering is to optimize horizontal well placement while freeing up time operational geologists had spent doing routine work in order to focus on complex and more intense tasks as well as the reduction of operational errors related to human factors. This paper will provide details on several automated geosteering algorithms. They have been tested successfully on large numbers of wells. The results of automated geosteering were as close as 90% to the manual interpretations done by geologists. When the results diverged, the geologists often "agreed" with the interpretation proposed by the algorithm.
{"title":"Automated Geosteering While Drilling Using Machine Learning. Case Studies","authors":"I. Denisenko, I. Kuvaev, I. Uvarov, Oleg Evgenievich Kushmantzev, Artem Igorevich Toporov","doi":"10.2118/202046-ms","DOIUrl":"https://doi.org/10.2118/202046-ms","url":null,"abstract":"\u0000 Today's oil & gas industry faces a number of different challenges. Drilling activities are ramping up due to an increase in hydrocarbon demand combined with a reduction of easy-to-recover reserves. Horizontal drilling is growing and has become an integral part of field development. The geology is becoming more and more complex requiring drilling through dense layers targeting thin-layered reservoirs with lateral changes and anisotropy. In recent years, companies have been looking at the ways of optimizing drilling costs by increasing efficiency and process automation. This has been a driver for many companies to stay profitable and efficient in the market.\u0000 One of the areas of interest for process automation has been a geosteering. Geosteering is the real-time adjustment well trajectory while drilling to maximize effective footage in the target zone. In this paper, innovative new approaches to automation of the geosteering process will be discussed. This approach has been successfully tested and deployed in several leading O&G companies.\u0000 The main objective of automated geosteering is to optimize horizontal well placement while freeing up time operational geologists had spent doing routine work in order to focus on complex and more intense tasks as well as the reduction of operational errors related to human factors. This paper will provide details on several automated geosteering algorithms. They have been tested successfully on large numbers of wells. The results of automated geosteering were as close as 90% to the manual interpretations done by geologists. When the results diverged, the geologists often \"agreed\" with the interpretation proposed by the algorithm.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"90 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121932950","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. Yadav, D. Davudov, Y. Danişman, A. Malkov, E. Omara, A. Venkatraman, A. El-Hawari
The uncertainties associated with oil and gas field reduces with time. When oil fields mature, there is a potential to better understand the field due to the availability of historic production and injection data. In this research, a novel approach is presented which uses data analytics techniques to optimize waterflooding in a Gulf of Suez field. A combination of qualitative and quantitative techniques has been applied to develop a new workflow for analyzing and optimizing waterflood. The presented technique involves combining qualitative analysis (random forest) and quantitative analysis (capacitance resistance model, CRM) to obtain a waterflood strategy for the producing field. The Random forest algorithm (machine learning technique) is used to compare two time series signals – production data and injection data from producer/injector wells. The data from each injector and surrounding producers are used for random forest analysis to identify the most effective and ineffective injector-producer pairs. Next, the qualitative analysis using the capacitance resistance model (CRM) is used to determine gain values between each injector-producer pair and to also obtain new injection rates for increasing oil recovery. Results obtained from the random forest model helps reduce the number of unknowns and further validate results in CRM. The production and injection data reveal the most effective and ineffective injector-producer pairs that are the result of changes occurring in the reservoir during waterflood. Accordingly, the use of data analytics technique of random forest analysis and CRM on production injection data helps improve reservoir characterization. This combined analysis for the presented field uniquely helps identify effective and ineffective injector-producer pairs to determine the efficiency of waterflooding. The results from this novel analytical technique are presented for the Gulf of Suez field. These results compare well with the streamline approach presented for the same Gulf of Suez field. In summary, a new method for reservoir surveillance using data analytics technique of random forest in combination with the capacitance resistance model is presented. The novel combination of the qualitative and quantitative methods presented also helps adapt the specific characteristic of this field – the presence of water drive (pseudo injector). The modeling of water drive as an additional injector (pseudo injector) improves the gain coefficient obtained from the CRM. The comparison with streamlines helps benchmark the model results especially in cases where such secondary data is not available. The model presented can be adapted to similar mature fields under waterfloods. This new approach can be used to optimize water injection more frequently using operations data being gathered for implementing digitization strategies for oil and gas companies.
{"title":"A New Data Analytics Based Method to Characterize Waterflood Strategy in Geologically Challenging Mature Oil Field","authors":"A. Yadav, D. Davudov, Y. Danişman, A. Malkov, E. Omara, A. Venkatraman, A. El-Hawari","doi":"10.2118/201929-ms","DOIUrl":"https://doi.org/10.2118/201929-ms","url":null,"abstract":"\u0000 The uncertainties associated with oil and gas field reduces with time. When oil fields mature, there is a potential to better understand the field due to the availability of historic production and injection data. In this research, a novel approach is presented which uses data analytics techniques to optimize waterflooding in a Gulf of Suez field. A combination of qualitative and quantitative techniques has been applied to develop a new workflow for analyzing and optimizing waterflood.\u0000 The presented technique involves combining qualitative analysis (random forest) and quantitative analysis (capacitance resistance model, CRM) to obtain a waterflood strategy for the producing field. The Random forest algorithm (machine learning technique) is used to compare two time series signals – production data and injection data from producer/injector wells. The data from each injector and surrounding producers are used for random forest analysis to identify the most effective and ineffective injector-producer pairs. Next, the qualitative analysis using the capacitance resistance model (CRM) is used to determine gain values between each injector-producer pair and to also obtain new injection rates for increasing oil recovery. Results obtained from the random forest model helps reduce the number of unknowns and further validate results in CRM.\u0000 The production and injection data reveal the most effective and ineffective injector-producer pairs that are the result of changes occurring in the reservoir during waterflood. Accordingly, the use of data analytics technique of random forest analysis and CRM on production injection data helps improve reservoir characterization. This combined analysis for the presented field uniquely helps identify effective and ineffective injector-producer pairs to determine the efficiency of waterflooding. The results from this novel analytical technique are presented for the Gulf of Suez field. These results compare well with the streamline approach presented for the same Gulf of Suez field.\u0000 In summary, a new method for reservoir surveillance using data analytics technique of random forest in combination with the capacitance resistance model is presented. The novel combination of the qualitative and quantitative methods presented also helps adapt the specific characteristic of this field – the presence of water drive (pseudo injector). The modeling of water drive as an additional injector (pseudo injector) improves the gain coefficient obtained from the CRM. The comparison with streamlines helps benchmark the model results especially in cases where such secondary data is not available. The model presented can be adapted to similar mature fields under waterfloods. This new approach can be used to optimize water injection more frequently using operations data being gathered for implementing digitization strategies for oil and gas companies.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134565109","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. Rylance, Y. Tuzov, S. Aliyev, A. Gorbov, I. Galitskiy, D. Makhmutov, V. Grinchenko, R. Sultanov, I. Levanov
The TAAS-Yurakh Neftegazodobycha Company (TYNGD) have been ramping up the drilling activity in the Srednebotuobinskoye oil field in Eastern Siberia, beating new records and delivering audacious targets in multilateral well designs. Drilling activity has ramped-up from 2 to 10 drilling rigs since formation of the JV business in Early 2015. As development drilling has progressed, a number of existing and newly identified challenges have arisen. Development of the Srednebotuobinskoye oil field is from the Bt. formation which is a thin oil rim with a massive gas cap. As the horizontal permeability is more than 350 mD this leads to severe challenges in increasing the oil production while restricting the production of associated gas and in order to deal with these challenges, a multilateral project was established to apply the best that this technology can offer. The multilateral well concept was a key approach to successfully develop the field, based on the in-situ risks and required well economics. Many of these issues have been addressed, in a stepwise and logical fashion, and this paper describes how this has been achieved and the progress that has been made to date. Economic challenges required that the well construction process be improved in efficiency, increased the individual well productivity and enhanced economic delivery. In order to deliver these efficiencies, the TYNGD Team prioritised a number of initiatives, such as a reduction in the Non-Productive Time (NPT) that is associated with the drilling and well construction process. From vuggy-losses in the overburden, to managing increasing losses and differential sticking within the reservoir; a number of key challenges have been identified; many have been addressed, others are in action and a few remain opportunities. In order to manage limited resources as efficiently as possible, and while building a meaningful and sufficiently populated drilling wells database; a simplistic roadmap of the wellbore construction process was created from best practice. This approach allowed TYNGD to prioritize targeting of trials, pilots and techniques to those areas that were most impactful to the overall field development at this stage. Close integration between the drilling and sub-surface teams allowed such ranking/prioritisation to be highly effective. This began with ensuring that the cement integrity was being achieved through the gas-cap region, to ensure that productivity, in the open-hole was assured. Major losses in the overburden were also targeted and prioritised, and a range of options were developed and deployed in order to help minimise issues. In parallel with this, formation damage and well productivity behaviour was also addressed, which has led to the planning and implementation of a number of multilateral field trials. Development of the Srednebotuobinskoye oilfield is underway and in order to deliver the most efficient development, drilling optimisation and continuous improvemen
{"title":"Fishbones, Wishbones and Birch-Leaves, Multilateral Well Design on the Srednebotuobinskoye Field in Eastern Siberia","authors":"M. Rylance, Y. Tuzov, S. Aliyev, A. Gorbov, I. Galitskiy, D. Makhmutov, V. Grinchenko, R. Sultanov, I. Levanov","doi":"10.2118/201849-ms","DOIUrl":"https://doi.org/10.2118/201849-ms","url":null,"abstract":"\u0000 The TAAS-Yurakh Neftegazodobycha Company (TYNGD) have been ramping up the drilling activity in the Srednebotuobinskoye oil field in Eastern Siberia, beating new records and delivering audacious targets in multilateral well designs. Drilling activity has ramped-up from 2 to 10 drilling rigs since formation of the JV business in Early 2015. As development drilling has progressed, a number of existing and newly identified challenges have arisen.\u0000 Development of the Srednebotuobinskoye oil field is from the Bt. formation which is a thin oil rim with a massive gas cap. As the horizontal permeability is more than 350 mD this leads to severe challenges in increasing the oil production while restricting the production of associated gas and in order to deal with these challenges, a multilateral project was established to apply the best that this technology can offer. The multilateral well concept was a key approach to successfully develop the field, based on the in-situ risks and required well economics. Many of these issues have been addressed, in a stepwise and logical fashion, and this paper describes how this has been achieved and the progress that has been made to date.\u0000 Economic challenges required that the well construction process be improved in efficiency, increased the individual well productivity and enhanced economic delivery. In order to deliver these efficiencies, the TYNGD Team prioritised a number of initiatives, such as a reduction in the Non-Productive Time (NPT) that is associated with the drilling and well construction process. From vuggy-losses in the overburden, to managing increasing losses and differential sticking within the reservoir; a number of key challenges have been identified; many have been addressed, others are in action and a few remain opportunities.\u0000 In order to manage limited resources as efficiently as possible, and while building a meaningful and sufficiently populated drilling wells database; a simplistic roadmap of the wellbore construction process was created from best practice. This approach allowed TYNGD to prioritize targeting of trials, pilots and techniques to those areas that were most impactful to the overall field development at this stage. Close integration between the drilling and sub-surface teams allowed such ranking/prioritisation to be highly effective. This began with ensuring that the cement integrity was being achieved through the gas-cap region, to ensure that productivity, in the open-hole was assured. Major losses in the overburden were also targeted and prioritised, and a range of options were developed and deployed in order to help minimise issues. In parallel with this, formation damage and well productivity behaviour was also addressed, which has led to the planning and implementation of a number of multilateral field trials.\u0000 Development of the Srednebotuobinskoye oilfield is underway and in order to deliver the most efficient development, drilling optimisation and continuous improvemen","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130556469","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}
In recent years, microbial enhanced oil recovery methods have been systematically applied in the Daqing oilfield in order to stimulate and increase oil production, remaining difficult to be recovered after polymer flooding. In this paper, a selection method of activator for indigenous energized microorganisms in Daqing oilfield with low permeability reservoirs was proposed. Firstly, the number of microorganisms in samples taken from production and injection wells in the target block was measured. Based on the obtained results, the condition for the use of MEOR method in the studied well system was determined. Secondly, laboratory experiments were conducted on the culture of indigenous microorganisms and the optimal parameters of the activator for indigenous microorganisms was determined by the obtained experiment results. Thirdly, the concentration of indigenous microorganisms, the pH value, interfacial tension, the viscosity change of the crude oil and the amount of generated gas after the hydrocarbons metabolization were measured. Based on the values of the crude oil viscosity and the amount of generated gas, the optimal ratio of nutrients (glucose and corn steep liquor) that are part of the activator for indigenous microorganisms was determined. The experimental results showed that when the ratio of nutrients (glucose and corn liquor) is equal to 1:2, the viscosity of the crude oil after the activation is minimal and the amount of generated gas after the hydrocarbons metabolization reaches the maximum value. The selected activator composition was used to culture indigenous microorganisms in samples taken from production wells in the studied well system. The pressure increase was observed due to the occurrence of biogas during the process of hydrocarbon degradation by microorganisms. In the further development of the Daqing field with low permeability reservoirs using microbiological methods, it is recommended to use the selected activator composition for the culture of indigenous microorganisms.
{"title":"Selection Method of Activator for Indigenous Energized Microorganisms in Daqing Oilfield with Low Permeability Reservoirs","authors":"Xiaofeng Zhou, A. Gayubov, S. Le, Ying Yang","doi":"10.2118/201835-ms","DOIUrl":"https://doi.org/10.2118/201835-ms","url":null,"abstract":"\u0000 In recent years, microbial enhanced oil recovery methods have been systematically applied in the Daqing oilfield in order to stimulate and increase oil production, remaining difficult to be recovered after polymer flooding. In this paper, a selection method of activator for indigenous energized microorganisms in Daqing oilfield with low permeability reservoirs was proposed.\u0000 Firstly, the number of microorganisms in samples taken from production and injection wells in the target block was measured. Based on the obtained results, the condition for the use of MEOR method in the studied well system was determined. Secondly, laboratory experiments were conducted on the culture of indigenous microorganisms and the optimal parameters of the activator for indigenous microorganisms was determined by the obtained experiment results. Thirdly, the concentration of indigenous microorganisms, the pH value, interfacial tension, the viscosity change of the crude oil and the amount of generated gas after the hydrocarbons metabolization were measured. Based on the values of the crude oil viscosity and the amount of generated gas, the optimal ratio of nutrients (glucose and corn steep liquor) that are part of the activator for indigenous microorganisms was determined.\u0000 The experimental results showed that when the ratio of nutrients (glucose and corn liquor) is equal to 1:2, the viscosity of the crude oil after the activation is minimal and the amount of generated gas after the hydrocarbons metabolization reaches the maximum value. The selected activator composition was used to culture indigenous microorganisms in samples taken from production wells in the studied well system. The pressure increase was observed due to the occurrence of biogas during the process of hydrocarbon degradation by microorganisms. In the further development of the Daqing field with low permeability reservoirs using microbiological methods, it is recommended to use the selected activator composition for the culture of indigenous microorganisms.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125399929","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. T. Litvin, A. A. Terentiyev, Denis Anatolevich Gornov, V. N. Kozhin, K. Pchela, I. I. Kireyev, S. V. Demin, A. V. Nikitin, Pavel Valeryevich Roschin
The paper presents various modified technologies to enhance oil production and to stimulate the inflow of high-viscosity oil, based on a combination of the traditional approach and the use of solvents. The results of laboratory experiments to study the effects of 29 solvents reagents on high-viscosity oil samples from 5 different objects are presented. Based on the analysis of the obtained laboratory data, basic recommendations are formulated to increase the efficiency of extraction of high-viscosity oil using solvents. In order to determine the potential for modifying oil recovery enhancement and well stimulation methods by adding solvents to their process, a comparative hydrodynamic modeling of 3 treatment options for the bottom-hole zone and subsequent assessment of their economic effect were carried out.
{"title":"Selection of Effective Solvents – Universal Modification of Presently Available Enhanced Oil Recovery Methods and Oil Production Stimulation Processes","authors":"A. T. Litvin, A. A. Terentiyev, Denis Anatolevich Gornov, V. N. Kozhin, K. Pchela, I. I. Kireyev, S. V. Demin, A. V. Nikitin, Pavel Valeryevich Roschin","doi":"10.2118/201831-ms","DOIUrl":"https://doi.org/10.2118/201831-ms","url":null,"abstract":"\u0000 The paper presents various modified technologies to enhance oil production and to stimulate the inflow of high-viscosity oil, based on a combination of the traditional approach and the use of solvents.\u0000 The results of laboratory experiments to study the effects of 29 solvents reagents on high-viscosity oil samples from 5 different objects are presented. Based on the analysis of the obtained laboratory data, basic recommendations are formulated to increase the efficiency of extraction of high-viscosity oil using solvents.\u0000 In order to determine the potential for modifying oil recovery enhancement and well stimulation methods by adding solvents to their process, a comparative hydrodynamic modeling of 3 treatment options for the bottom-hole zone and subsequent assessment of their economic effect were carried out.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"624 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123275670","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. Zakirov, Daniil Yurievich Kartinen, A. I. Madyarov
The components of casing drilling technology are evolving and the application area is expanding. Drilling engineers today have more flexibility than ever before to redesign wells and can combine different technologies, materials and equipment to achieve new records in well construction efficiency. In recent years, PJSC Gazpromneft has been constantly using casing drilling technologies. The increasing complexity of geological conditions and the need to reduce well construction costs lead to more efficient solutions. The article presents the actual experience of using the following variations of the casing drilling technology: Non-directional drilling on 426mm casing together with 630mm thermal case at Yamburgskoye field Non-directional drilling on 324mm casing at the Vostochny Section of the Orenburg oil and gas condensate field (VUONGKM) Run in hole with rotation on 245mm casing at VUONGKM. Directional drilling with 245mm casing with a retrievable BHA at VUONGKF. The use of casing drilling technology has not in all the cases under consideration increased the efficiency of well construction, but at the same time the results obtained allow us to predict an additional increase in the efficiency of the application of these technological solutions. At the end of the article, a tree of casing drilling technologies was built and segments with development prospects were highlighted.
{"title":"Experience of Existing Casing Drilling Technologies in Various Conditions and Search of the New Solutions for Actual Challenges","authors":"A. Zakirov, Daniil Yurievich Kartinen, A. I. Madyarov","doi":"10.2118/201854-ms","DOIUrl":"https://doi.org/10.2118/201854-ms","url":null,"abstract":"\u0000 The components of casing drilling technology are evolving and the application area is expanding. Drilling engineers today have more flexibility than ever before to redesign wells and can combine different technologies, materials and equipment to achieve new records in well construction efficiency. In recent years, PJSC Gazpromneft has been constantly using casing drilling technologies. The increasing complexity of geological conditions and the need to reduce well construction costs lead to more efficient solutions.\u0000 The article presents the actual experience of using the following variations of the casing drilling technology:\u0000 Non-directional drilling on 426mm casing together with 630mm thermal case at Yamburgskoye field Non-directional drilling on 324mm casing at the Vostochny Section of the Orenburg oil and gas condensate field (VUONGKM) Run in hole with rotation on 245mm casing at VUONGKM. Directional drilling with 245mm casing with a retrievable BHA at VUONGKF.\u0000 The use of casing drilling technology has not in all the cases under consideration increased the efficiency of well construction, but at the same time the results obtained allow us to predict an additional increase in the efficiency of the application of these technological solutions.\u0000 At the end of the article, a tree of casing drilling technologies was built and segments with development prospects were highlighted.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121077522","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Khadum horizon is of interest for discovery of shale oil, which deposits were discovered within the boundaries of Zhuravskoye, Vorobyevskoye and other fields. Within the boundaries of the Buynak depression the Khadum horizon is characterized by a manyfold increase in thickness, and the results of the field and laboratory studies show a high potential for oil-bearing capacity of these deposits. Regional study of the structure of the Khadum deposits was conducted. Criteria for oil-bearing capacity prospects were identified and a priority site for development was identified.
{"title":"Prospects for Oil-and Gas-Bearing Capacity of the Khadum Shale Deposits in the Eastern Fore-Caucasus","authors":"V. V. Kalabin","doi":"10.2118/201809-ms","DOIUrl":"https://doi.org/10.2118/201809-ms","url":null,"abstract":"\u0000 The Khadum horizon is of interest for discovery of shale oil, which deposits were discovered within the boundaries of Zhuravskoye, Vorobyevskoye and other fields. Within the boundaries of the Buynak depression the Khadum horizon is characterized by a manyfold increase in thickness, and the results of the field and laboratory studies show a high potential for oil-bearing capacity of these deposits. Regional study of the structure of the Khadum deposits was conducted. Criteria for oil-bearing capacity prospects were identified and a priority site for development was identified.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"561 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133546280","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. Fedorov, A. Povalyaev, B. Suleymanov, I. R. Dilmuhametov, A. Sergeychev
The aim of this work is to develop an approach to multivariate optimization of development systems for tight oil reservoirs of the Achimov formation, where large volumes of drilling of RN-Yuganskneftegaz LLC are currently concentrated on. The approach described in the paper is an integral part of the corporate module "Decision Support System for drilling out new sections of tight oil reservoirs", which allows making quick design decisions for new drilling sites of target objects. This work discusses the main parts of the integrated solution of this system that will be embedded into corporate software. Also, the description of the global approach and obtained results are presented. The main idea of this project is based on automatic assignment of the prospective development zone to an existing cluster-analog, based on well logs response in exploration wells. Following this interpretation, the potential performance of various development systems is evaluated and the optimal one is selected. Within the framework of these projects the following tasks were solved: Wells clustering in Achimov deposits and their analogs. The geological heterogeneity and reservoir connectivity were characterized and a special algorithm for wells assignments to an existing cluster was developed, that is done by: Wells clustering depending on their petrophysical properties derived from well logs interpretation via k-means algorithm. Wells classification with a use of neural network. Multivariate 3D dynamic modeling and creation of surrogate models to provide predictions of reservoir simulation results. Development of the software package with all mentioned functionality being implemented.
{"title":"Decision Support System for Tight Oil Fields Development Achimov Deposits and Their Analogues Using Machine Learning Algorithms","authors":"A. Fedorov, A. Povalyaev, B. Suleymanov, I. R. Dilmuhametov, A. Sergeychev","doi":"10.2118/201921-ms","DOIUrl":"https://doi.org/10.2118/201921-ms","url":null,"abstract":"\u0000 The aim of this work is to develop an approach to multivariate optimization of development systems for tight oil reservoirs of the Achimov formation, where large volumes of drilling of RN-Yuganskneftegaz LLC are currently concentrated on. The approach described in the paper is an integral part of the corporate module \"Decision Support System for drilling out new sections of tight oil reservoirs\", which allows making quick design decisions for new drilling sites of target objects.\u0000 This work discusses the main parts of the integrated solution of this system that will be embedded into corporate software.\u0000 Also, the description of the global approach and obtained results are presented. The main idea of this project is based on automatic assignment of the prospective development zone to an existing cluster-analog, based on well logs response in exploration wells. Following this interpretation, the potential performance of various development systems is evaluated and the optimal one is selected.\u0000 Within the framework of these projects the following tasks were solved:\u0000 Wells clustering in Achimov deposits and their analogs. The geological heterogeneity and reservoir connectivity were characterized and a special algorithm for wells assignments to an existing cluster was developed, that is done by: Wells clustering depending on their petrophysical properties derived from well logs interpretation via k-means algorithm. Wells classification with a use of neural network. Multivariate 3D dynamic modeling and creation of surrogate models to provide predictions of reservoir simulation results. Development of the software package with all mentioned functionality being implemented.","PeriodicalId":359083,"journal":{"name":"Day 2 Tue, October 27, 2020","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-10-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133991507","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：