S. Mulyani, Zammy Sarmiento, V. Chandra, R. Hendry, S. Nasution, R. Hidayat, Jhonny Jhonny, P. Sari, Dedi Juandi
Understanding the reservoir conditions through 3D subsurface modeling is the key to optimize the exploration stage in geothermal field. A calibrated reservoir model based on updated data can be very important for this process. The main challenge of reservoir characterization in a geothermal field is the lack of subsurface data, therefore surface data are useful for reservoir modeling. This study utilized Sorik Marapi geothermal field data as a reference for reservoir modeling. This field is one of the geothermal fields in Indonesia that has been recently drilled, with results indicating the existence of a high temperature-neutral acidity resource. Initial reservoir model has been built from the previous study to create conceptual 3D subsurface model which includes structural, lithology, resistivity, and temperature distribution from surface exploration data, including surface mapping, remote sensing image interpretation, the magnetotelluric method, and subsurface data from six wells data. The objective of this paper is to calibrate the initial reservoir model with information from an additional ten new wells data to improve delineation for updated reservoir area in the field. Software that allowed multidisciplinary data integration from surface to subsurface information was used for the calibration of the initial 3D model. The workflow to calibrate the model started with data loading and quality control, preparing the old 3D model and comparing it to new well data, analyzing the comparison, and updating the 3D model. Finally, the new delineation of reservoir zone can be determined. The result of this study is an updated 3D subsurface static model defining the vertical and lateral reservoir boundaries, as well as the prime resource areas, which would be the basis for designing future well targets, and parameters for a dynamic reservoir model. The same model can be expanded to construct the numerical model to match the natural state condition of the field and make forecasts of the future reservoir behavior at different operating conditions. The main properties of the updated 3D model are lithology and temperature, which are important in geothermal reservoir delineation.
{"title":"Calibrated Natural State Model in Sorik Marapi Geothermal Field, Indonesia","authors":"S. Mulyani, Zammy Sarmiento, V. Chandra, R. Hendry, S. Nasution, R. Hidayat, Jhonny Jhonny, P. Sari, Dedi Juandi","doi":"10.2523/IPTC-19221-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19221-MS","url":null,"abstract":"\u0000 Understanding the reservoir conditions through 3D subsurface modeling is the key to optimize the exploration stage in geothermal field. A calibrated reservoir model based on updated data can be very important for this process. The main challenge of reservoir characterization in a geothermal field is the lack of subsurface data, therefore surface data are useful for reservoir modeling. This study utilized Sorik Marapi geothermal field data as a reference for reservoir modeling. This field is one of the geothermal fields in Indonesia that has been recently drilled, with results indicating the existence of a high temperature-neutral acidity resource. Initial reservoir model has been built from the previous study to create conceptual 3D subsurface model which includes structural, lithology, resistivity, and temperature distribution from surface exploration data, including surface mapping, remote sensing image interpretation, the magnetotelluric method, and subsurface data from six wells data.\u0000 The objective of this paper is to calibrate the initial reservoir model with information from an additional ten new wells data to improve delineation for updated reservoir area in the field. Software that allowed multidisciplinary data integration from surface to subsurface information was used for the calibration of the initial 3D model. The workflow to calibrate the model started with data loading and quality control, preparing the old 3D model and comparing it to new well data, analyzing the comparison, and updating the 3D model. Finally, the new delineation of reservoir zone can be determined.\u0000 The result of this study is an updated 3D subsurface static model defining the vertical and lateral reservoir boundaries, as well as the prime resource areas, which would be the basis for designing future well targets, and parameters for a dynamic reservoir model. The same model can be expanded to construct the numerical model to match the natural state condition of the field and make forecasts of the future reservoir behavior at different operating conditions. The main properties of the updated 3D model are lithology and temperature, which are important in geothermal reservoir delineation.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81262249","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}
J. Scarborough, Leonardo Mega-Franca, Mohamed Farouk Ibrahim
Process upsets in high oil production facilities can hinder optimal plant performance and result in system shut-ins. Based on several successful demulsifier chemical trials, scientists and engineers have developed a guideline on how to optimize production throughout the chemical trial period. Factors such as chemical injection rate, export crude oil monitoring (basic sediment and water (BS&W) and salt), discharge water quality(from the water-oil separator (WOSEP)), and transformer voltage fluctuation (dehydrator and desalter) plays an important role in minimizing the system upset. Prior to chemical trial, scientists and engineers analyze the process system to understand individual vessel functions and limitations. Incumbent chemical program provides baselines and key performance indicators (KPIs) set minimum oil specifications before exporting oil to refineries. Demulsifier injection rates are reduced based on the chemical program optimization proposal until it reaches the dosage limit while maintaining stable process throughout the trial. Therefore, scientists and engineers may evaluate the demulsifier’s performance based on the KPIs set with no system upset. Fast fluid separation in the High Pressure Production Traps (HPPTs) is an important strategy in order to improve process system’s performance. High volume oil production systems typically have two HPPTs in parallel for initial water separation. Downstream of the HPPTs is the Low Pressure Production Trap (LPPT), which is mainly used for gas separation. Oil continues to the dehydrator to finish the dehydration to meet the pipeline BS&W requirement. The dehydrator is where the transformer is located for the electrostatic grid and high amounts of water separation can cause fluid levels to fluctuate and trip the transformers. Throughout several field trial experiences, demulsifier rates can be optimized (reduced) further when it shows increased water separation at HPPT vessels. Clear water from HPPTs discharge, valves in water leg HPPTs open more (%), stable voltage grid (dehydrator/desalter), and less than 0.2% BS&W with less than 10ptb salt recorded at the export oil gives a good indication that the process is stable. Thus reduced the risk for system upset. This paper summaries the best approach to optimize chemical rates in high volume oil production systems, analyzes qualitative and quantitative system checks to verify stable operations, and discusses potential risks involved when reaching lower limits of effective chemical rates.
{"title":"Minimise System Upsets in High Oil Production Facility throughout Demulsifier Chemical Trial","authors":"J. Scarborough, Leonardo Mega-Franca, Mohamed Farouk Ibrahim","doi":"10.2523/IPTC-19496-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19496-MS","url":null,"abstract":"\u0000 Process upsets in high oil production facilities can hinder optimal plant performance and result in system shut-ins. Based on several successful demulsifier chemical trials, scientists and engineers have developed a guideline on how to optimize production throughout the chemical trial period. Factors such as chemical injection rate, export crude oil monitoring (basic sediment and water (BS&W) and salt), discharge water quality(from the water-oil separator (WOSEP)), and transformer voltage fluctuation (dehydrator and desalter) plays an important role in minimizing the system upset.\u0000 Prior to chemical trial, scientists and engineers analyze the process system to understand individual vessel functions and limitations. Incumbent chemical program provides baselines and key performance indicators (KPIs) set minimum oil specifications before exporting oil to refineries. Demulsifier injection rates are reduced based on the chemical program optimization proposal until it reaches the dosage limit while maintaining stable process throughout the trial. Therefore, scientists and engineers may evaluate the demulsifier’s performance based on the KPIs set with no system upset. Fast fluid separation in the High Pressure Production Traps (HPPTs) is an important strategy in order to improve process system’s performance.\u0000 High volume oil production systems typically have two HPPTs in parallel for initial water separation. Downstream of the HPPTs is the Low Pressure Production Trap (LPPT), which is mainly used for gas separation. Oil continues to the dehydrator to finish the dehydration to meet the pipeline BS&W requirement. The dehydrator is where the transformer is located for the electrostatic grid and high amounts of water separation can cause fluid levels to fluctuate and trip the transformers.\u0000 Throughout several field trial experiences, demulsifier rates can be optimized (reduced) further when it shows increased water separation at HPPT vessels. Clear water from HPPTs discharge, valves in water leg HPPTs open more (%), stable voltage grid (dehydrator/desalter), and less than 0.2% BS&W with less than 10ptb salt recorded at the export oil gives a good indication that the process is stable. Thus reduced the risk for system upset.\u0000 This paper summaries the best approach to optimize chemical rates in high volume oil production systems, analyzes qualitative and quantitative system checks to verify stable operations, and discusses potential risks involved when reaching lower limits of effective chemical rates.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84407700","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}
D-X oilfield implemented an infill adjustment from 2013 to 2015, adding 101 development wells, two central processing platforms and two wellhead platforms. After the adjustment, the oil field greatly improved the engineering processing capacity, but it had also entered a period of rapid decline. In order to mitigate the decline and improve the recovery rate, 65 adjustment wells were implemented in this oilfield after the first adjustment. Based on the new drilling data and production data, earlier inefficient well management was turned a more efficient well management, where the engineering processing space is fully utilized, the decline of oil field is alleviated, and the development effect is greatly improved. This this paper expounds how to excavate the potential again after the redevelopment project in large fluvial facies oilfield in Bohai bay, taking this oilfield as an example.
{"title":"Tap the Potentials and Reduce the Geological Uncertainties of Mature Complex Fluvial Reservoir: Case Study Redevelopment Plan of D-X Oilfield in Bohai Bay","authors":"Yifan He","doi":"10.2523/IPTC-19415-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19415-MS","url":null,"abstract":"\u0000 D-X oilfield implemented an infill adjustment from 2013 to 2015, adding 101 development wells, two central processing platforms and two wellhead platforms.\u0000 After the adjustment, the oil field greatly improved the engineering processing capacity, but it had also entered a period of rapid decline. In order to mitigate the decline and improve the recovery rate, 65 adjustment wells were implemented in this oilfield after the first adjustment. Based on the new drilling data and production data, earlier inefficient well management was turned a more efficient well management, where the engineering processing space is fully utilized, the decline of oil field is alleviated, and the development effect is greatly improved.\u0000 This this paper expounds how to excavate the potential again after the redevelopment project in large fluvial facies oilfield in Bohai bay, taking this oilfield as an example.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82514119","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 drilling of shale gas development wells in the Sichuan Basin has been problematic, with stuck pipe and fluid loss events reported in many wells. These events hindered the development efforts as operators whose aim is to reduce drilling costs and increase drilling speed are faced with developing drilling strategies for more cost effective well delivery. As such, it becomes critical to understand the key mechanisms of borehole failures and fluid losses during drilling of the laterals so that proper mud weights and mud designs can be formulated to overcome the drilling challenges. To optimize the entire drilling process from different angles, an integrated approach is required to combine the knowledge and expertise from different disciplines. A robust geomechanical model and detailed diagnostics of the borehole instability related problems are key and form the basis for the drilling risk management. To build a robust geomechanical model, several representative areas in the Sichuan Shale Gas play were reviewed using a consistent approach. A general understanding of the in-situ stress conditions and rock mechanical properties of the Longmaxi Shale Gas reservoir was developed by combing data and knowledge in the different areas. Using the geomechanical model, a series of newly drilled horizontal wells was also reviewed so that the main causes of stuck pipes and fluid losses can be determined. Based on the geomechanical model and drilling experiences review, risks that could potentially cause non-productive time (NPT) during drilling of the planned wells were postulated and listed, and each risk was then assessed in detail so that a thorough understanding of the risk factors can be achieved from different drilling perspectives. Consequently, a multi-disciplinary mitigating solution was proposed in order to help with reducing the occurrence of any borehole instability-related problems. The integrated drilling optimization plan was executed successfully during the drilling of the horizontal wells in the Sichuan Basin. The increase in understanding of the geomechanical issues in the Sichuan Shale Gas drilling indicated that common trends in shale characteristics are present although it is widely accepted that shale types and stress conditions are different in each field. With the geomechanical models being calibrated from information in different areas, uncertainties are reduced and the robust models in turn provide solid foundations for risk identification and mitigation, leading to successful and economical drilling of the long laterals. However, it is a long and continuous learning process in order to apply this integrated approach effectively. To achieve continuous improvement, the geomechanical model and mitigating solutions need to be refined regularly following the drilling process while further information and knowledge are being acquired in the Sichuan Shale Gas area.
{"title":"Understanding the Geomechanical Challenges and Risk Mitigation in Sichuan Shale Gas Drilling, China","authors":"F. Gui, Shanshan Wang, S. Bordoloi, S. Ong","doi":"10.2523/IPTC-19387-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19387-MS","url":null,"abstract":"\u0000 The drilling of shale gas development wells in the Sichuan Basin has been problematic, with stuck pipe and fluid loss events reported in many wells. These events hindered the development efforts as operators whose aim is to reduce drilling costs and increase drilling speed are faced with developing drilling strategies for more cost effective well delivery. As such, it becomes critical to understand the key mechanisms of borehole failures and fluid losses during drilling of the laterals so that proper mud weights and mud designs can be formulated to overcome the drilling challenges.\u0000 To optimize the entire drilling process from different angles, an integrated approach is required to combine the knowledge and expertise from different disciplines. A robust geomechanical model and detailed diagnostics of the borehole instability related problems are key and form the basis for the drilling risk management. To build a robust geomechanical model, several representative areas in the Sichuan Shale Gas play were reviewed using a consistent approach. A general understanding of the in-situ stress conditions and rock mechanical properties of the Longmaxi Shale Gas reservoir was developed by combing data and knowledge in the different areas. Using the geomechanical model, a series of newly drilled horizontal wells was also reviewed so that the main causes of stuck pipes and fluid losses can be determined.\u0000 Based on the geomechanical model and drilling experiences review, risks that could potentially cause non-productive time (NPT) during drilling of the planned wells were postulated and listed, and each risk was then assessed in detail so that a thorough understanding of the risk factors can be achieved from different drilling perspectives. Consequently, a multi-disciplinary mitigating solution was proposed in order to help with reducing the occurrence of any borehole instability-related problems. The integrated drilling optimization plan was executed successfully during the drilling of the horizontal wells in the Sichuan Basin.\u0000 The increase in understanding of the geomechanical issues in the Sichuan Shale Gas drilling indicated that common trends in shale characteristics are present although it is widely accepted that shale types and stress conditions are different in each field. With the geomechanical models being calibrated from information in different areas, uncertainties are reduced and the robust models in turn provide solid foundations for risk identification and mitigation, leading to successful and economical drilling of the long laterals. However, it is a long and continuous learning process in order to apply this integrated approach effectively. To achieve continuous improvement, the geomechanical model and mitigating solutions need to be refined regularly following the drilling process while further information and knowledge are being acquired in the Sichuan Shale Gas area.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"14 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91021873","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 order to understand the large scattering of elastic properties of carbonate rocks, two datasets were chosen in two different geological formations (non-tropical carbonate from Australia and actual continental carbonate from Turkey). Three statistical methods that aim to quantify the influence of Geological depositional environment and dominant pore type, that highlight similarities and differences on petro-elastic and petrophysic behaviors. Geological depositional environment information would be main reason for Vp variation as shown by Study 1, while in study 2 pore-type could have a strong link with P-wave velocity. To understand the origin of those similarities and differences, and to identify common information hidden inside the geological meanings, several simulation tests have been performed by digital rocks, in order to quantify the influences of the pore volume fraction, pore size and pore shape of carbonate microstructure. The numerical simulation shows that the pores size has statistically no influence on the elastic response; the pore shape is one of the main impacting parameter of the elastic properties. The future work consists on the understanding of influence factor for petrophysic parameter by more simulation results. The ultimate objective of this study is to identify factors that influence seismic velocity and then use it to better interpret the petrophysic parameters from seismic inversion.
{"title":"Factors Influencing Elastic Properties on Carbonate Rocks, Lessons Learnt from Two Case Studies and from Simulation Results","authors":"F. Hong, F. Bastide, O. Zerhouni, C. Planteblat","doi":"10.2523/IPTC-19560-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19560-MS","url":null,"abstract":"\u0000 In order to understand the large scattering of elastic properties of carbonate rocks, two datasets were chosen in two different geological formations (non-tropical carbonate from Australia and actual continental carbonate from Turkey). Three statistical methods that aim to quantify the influence of Geological depositional environment and dominant pore type, that highlight similarities and differences on petro-elastic and petrophysic behaviors. Geological depositional environment information would be main reason for Vp variation as shown by Study 1, while in study 2 pore-type could have a strong link with P-wave velocity. To understand the origin of those similarities and differences, and to identify common information hidden inside the geological meanings, several simulation tests have been performed by digital rocks, in order to quantify the influences of the pore volume fraction, pore size and pore shape of carbonate microstructure. The numerical simulation shows that the pores size has statistically no influence on the elastic response; the pore shape is one of the main impacting parameter of the elastic properties. The future work consists on the understanding of influence factor for petrophysic parameter by more simulation results. The ultimate objective of this study is to identify factors that influence seismic velocity and then use it to better interpret the petrophysic parameters from seismic inversion.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78552173","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}
Xunsheng Du, Yuchen Jin, Xuqing Wu, Yu Liu, Xianping Wu, Omar Awan, Joey Roth, K. C. See, Nicolas Tognini, Jiefu Chen, Zhu Han
A deep learning framework is proposed and implemented for monitoring the cutting volumes of a shaker in real-time on a deep-water drilling rig. The framework aims at performing classification and quantification with a live video streaming. Compared to the traditional video analytics method that is time-consuming, the proposed framework is more efficient and can be implemented for a real-time video analysis application. The real-time deep learning video analysis model consists of two parts for processing. The first part is a multi-thread video processing engine. A modularized service named Rig-Site Virtual Presence (RSVP) provides real-time video streaming from the rig. The multi-thread video processing engine implements real-time decoding, preprocessing and encoding of the video stream. The second part is a customized deep classification model. Based on the deep neural network (DNN), we implement the following adaptations: 1) Applied whitening and instance normalization to video frames; 2) Optimized the number of convolutional layers and the number of nodes in fully-connected layers; 3) Applied L2-norm regularization. The customized model is embedded in the multi-thread video processing engine, which ensures the capability for the real-time inference. The deep learning model categorizes every video frame into "ExtraHeavy", "Heavy", "Light" or "None". The model also outputs the corresponding numerical probabilities of each outcome. The training of the model is accomplished on a Nvidia GeForce 1070 GPU using the video stream with 137Kbps bitrate, 5.84 frames/s, and a frame size of 720×486. With only a common CPU support, the inference of the pre-trained model can be conducted in real-time. Both labeled frames and numerical results will be saved for later examination. Compared to the manual labeling results, the proposed deep learning framework achieves very promising results for analyzing video streaming in real-time.
{"title":"The Embedded VGG-Net Video Stream Processing Framework for Real-Time Classification of Cutting Volume at Shale Shaker","authors":"Xunsheng Du, Yuchen Jin, Xuqing Wu, Yu Liu, Xianping Wu, Omar Awan, Joey Roth, K. C. See, Nicolas Tognini, Jiefu Chen, Zhu Han","doi":"10.2523/IPTC-19312-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19312-MS","url":null,"abstract":"\u0000 A deep learning framework is proposed and implemented for monitoring the cutting volumes of a shaker in real-time on a deep-water drilling rig. The framework aims at performing classification and quantification with a live video streaming. Compared to the traditional video analytics method that is time-consuming, the proposed framework is more efficient and can be implemented for a real-time video analysis application. The real-time deep learning video analysis model consists of two parts for processing. The first part is a multi-thread video processing engine. A modularized service named Rig-Site Virtual Presence (RSVP) provides real-time video streaming from the rig. The multi-thread video processing engine implements real-time decoding, preprocessing and encoding of the video stream. The second part is a customized deep classification model. Based on the deep neural network (DNN), we implement the following adaptations: 1) Applied whitening and instance normalization to video frames; 2) Optimized the number of convolutional layers and the number of nodes in fully-connected layers; 3) Applied L2-norm regularization. The customized model is embedded in the multi-thread video processing engine, which ensures the capability for the real-time inference. The deep learning model categorizes every video frame into \"ExtraHeavy\", \"Heavy\", \"Light\" or \"None\". The model also outputs the corresponding numerical probabilities of each outcome. The training of the model is accomplished on a Nvidia GeForce 1070 GPU using the video stream with 137Kbps bitrate, 5.84 frames/s, and a frame size of 720×486. With only a common CPU support, the inference of the pre-trained model can be conducted in real-time. Both labeled frames and numerical results will be saved for later examination. Compared to the manual labeling results, the proposed deep learning framework achieves very promising results for analyzing video streaming in real-time.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90897622","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 this paper, we present for the first time, a classification system for naturally-occurring gas hydrate deposits existing in the permafrost and marine environment. This classification is relatively simple but highlights the salient features of a gas hydrate deposit which are important for their exploration and production such as location, porosity system, gas origin and migration path. We then show how this classification can be used to describe eight well-studied gas hydrate deposits in permafrost and marine environment. Potential implications of this classification are also discussed.
{"title":"A Comparison of Gas Hydrate Petroleum Systems in Marine and Permafrost Regions","authors":"H. Lau, Ming Zhang, Jinjie Wang, Yin Huai","doi":"10.2523/IPTC-19164-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19164-MS","url":null,"abstract":"\u0000 In this paper, we present for the first time, a classification system for naturally-occurring gas hydrate deposits existing in the permafrost and marine environment. This classification is relatively simple but highlights the salient features of a gas hydrate deposit which are important for their exploration and production such as location, porosity system, gas origin and migration path. We then show how this classification can be used to describe eight well-studied gas hydrate deposits in permafrost and marine environment. Potential implications of this classification are also discussed.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87155110","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}
Bingjie Wang, Changgui Xu, Kui Wu, Rucai Zhang, Jun Deng, Naichuan Guo
A new oil property identification parameter (Pw) is derived which represents the total hydrocarbon generation and pyrolysis hydrocarbon. Using continuous measurement data (Pw) and a series of sample-based attributes from 3D seismic, a strong linear trend is observed. This trend linear is used to calculate heavy oil property data in 3D volume. At the same time, we have got the relationship between reservoir physical and oil properties. Based on this, the core data and geochemical data are used to study the charging of crude oil.
{"title":"Insights into the Extra Heavy Oil Property and Oil Charging Combing Rock Pyrolysis Paramenters, Geochemistry Data and Seismic Multi-Attribute Transformation","authors":"Bingjie Wang, Changgui Xu, Kui Wu, Rucai Zhang, Jun Deng, Naichuan Guo","doi":"10.2523/IPTC-19196-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19196-MS","url":null,"abstract":"\u0000 A new oil property identification parameter (Pw) is derived which represents the total hydrocarbon generation and pyrolysis hydrocarbon. Using continuous measurement data (Pw) and a series of sample-based attributes from 3D seismic, a strong linear trend is observed. This trend linear is used to calculate heavy oil property data in 3D volume. At the same time, we have got the relationship between reservoir physical and oil properties. Based on this, the core data and geochemical data are used to study the charging of crude oil.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"27 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87984417","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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