P. Dhote, T. Al-Adwani, M. Al-Bahar, Ahmad Al-otaibi, S. Chakraborty, Slobodan Stojić
Subsurface petroleum industry is burdened with uncertainties in every aspect from exploration to production due to limitations of accessibility to reservoir and technology. The most important tools used to understand, quantify and mitigate the uncertainties are geostatistical static modeling and numerical dynamic simulation geomodels. Geomodels are widely used in the industry for characterizing the reservoir and planning favorable development strategy. It is vital instrument for maximizing asset value and optimize project economics. Static geomodels are foundation for all the advanced numerical and analytical solutions to solve the intricacies of reservoir performance. At the same time, it is where all the static and dynamic geological and engineering observations get integrated to develop common understanding of the reservoir for future studies. Understanding of the above observations and imaging of reservoir framework by individual is the basis for building static geomodels. Hence, at time, the process is highly subjective and proper QC'ing of the models to achieve the general and specific modeling objectives becomes imperative. Simple Questionaries’ based QC'ing and ranking methodologies are also controlled by subjectivity and individual preferences. In the present endeavor, quantitative ‘Key Performance Indicators (KPIs)’ based standard static geomodeling practices and QC'ing methodologies at corporate level are developed in specially designed "Process Implementation Project (PIP) – Hydrocarbon resource and Uncertainty Management"’ under the aegis of ‘Kuwait Oil Company (KOC) - Reservoir Management Best Practices Steering Committee'. The main objectives are to establish a practical modeling process, workflows and criteria to standardize modeling processes. A structured self-guidling modeling document has been developed with self-assemment guidelines and questionary. Finally, for each individual process a set of KPIs are specified as minimum standard to meet to obtain the approval of static model. The present efforts are important for any geologists, geomodelers and reservoir engineers dealing with geostatistical and numerical reservoir modeling and will provide the KPI's based general practices for quality assurance (QA) and QC'ing of the models.
{"title":"KPI Based Standardizing Static Geomodeling Practices for QA and QC of Models","authors":"P. Dhote, T. Al-Adwani, M. Al-Bahar, Ahmad Al-otaibi, S. Chakraborty, Slobodan Stojić","doi":"10.2523/IPTC-19123-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19123-MS","url":null,"abstract":"\u0000 Subsurface petroleum industry is burdened with uncertainties in every aspect from exploration to production due to limitations of accessibility to reservoir and technology. The most important tools used to understand, quantify and mitigate the uncertainties are geostatistical static modeling and numerical dynamic simulation geomodels. Geomodels are widely used in the industry for characterizing the reservoir and planning favorable development strategy. It is vital instrument for maximizing asset value and optimize project economics.\u0000 Static geomodels are foundation for all the advanced numerical and analytical solutions to solve the intricacies of reservoir performance. At the same time, it is where all the static and dynamic geological and engineering observations get integrated to develop common understanding of the reservoir for future studies. Understanding of the above observations and imaging of reservoir framework by individual is the basis for building static geomodels. Hence, at time, the process is highly subjective and proper QC'ing of the models to achieve the general and specific modeling objectives becomes imperative. Simple Questionaries’ based QC'ing and ranking methodologies are also controlled by subjectivity and individual preferences.\u0000 In the present endeavor, quantitative ‘Key Performance Indicators (KPIs)’ based standard static geomodeling practices and QC'ing methodologies at corporate level are developed in specially designed \"Process Implementation Project (PIP) – Hydrocarbon resource and Uncertainty Management\"’ under the aegis of ‘Kuwait Oil Company (KOC) - Reservoir Management Best Practices Steering Committee'.\u0000 The main objectives are to establish a practical modeling process, workflows and criteria to standardize modeling processes. A structured self-guidling modeling document has been developed with self-assemment guidelines and questionary. Finally, for each individual process a set of KPIs are specified as minimum standard to meet to obtain the approval of static model.\u0000 The present efforts are important for any geologists, geomodelers and reservoir engineers dealing with geostatistical and numerical reservoir modeling and will provide the KPI's based general practices for quality assurance (QA) and QC'ing of the models.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"344 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75120532","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 existence of elastic anisotropy in the reservoir is obtained through the equivalent media theory. An isotropic elastic theory fairly explains the reservoir modeling or characterization but its not well explain anisotropic characteristic fairly for reservoir characterization which is extremely challenging without considering a self-consistent theory of effective equivalent media theory. In this research, equivalent media theory has been explained and implemented on a producing well-log data with consistent Vp, Vs, density and other parameters. Instead of using Voigt averaging, equivalent media theory used to estimate the effective stiffness parameters and compare with Thomsen's parameters and finally used effective anisotropy parameters and compare with gamma log. Result shows the effectiveness of equivalent media theory for future application for developing reservoir modeling and characterization.
{"title":"An Equivalent Media Theory for Reservoir Characterization","authors":"S. Prajapati, D. Ghosh","doi":"10.2523/IPTC-19301-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19301-MS","url":null,"abstract":"\u0000 The existence of elastic anisotropy in the reservoir is obtained through the equivalent media theory. An isotropic elastic theory fairly explains the reservoir modeling or characterization but its not well explain anisotropic characteristic fairly for reservoir characterization which is extremely challenging without considering a self-consistent theory of effective equivalent media theory. In this research, equivalent media theory has been explained and implemented on a producing well-log data with consistent Vp, Vs, density and other parameters. Instead of using Voigt averaging, equivalent media theory used to estimate the effective stiffness parameters and compare with Thomsen's parameters and finally used effective anisotropy parameters and compare with gamma log. Result shows the effectiveness of equivalent media theory for future application for developing reservoir modeling and characterization.","PeriodicalId":11267,"journal":{"name":"Day 3 Thu, March 28, 2019","volume":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-03-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77006589","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}
Anindya Nandi, S. Sarkar, Chandreyi Chatterjee, Sourav Das, Sambit Pattanaik, Chandan Majumder, Bhopal Kumar Haldia, P. Chaturvedi, Siddharth Srivastava, M. Verma, S. Sarkar
Owing to the depleting reserves in the conventional reservoirs over the last few years, unconventional reservoirs have gained significant importance in the exploration of oil and gas. Basement rocks, though non-sedimentary in origin, is looked upto as one of the important unconventional reservoirs. Deccan volcanics in Kutch-Saurashtra is one such example from India. This study shows and validates a methodology of how acoustic log data can be integrated with borehole images to understand reservoir properties that governs flow. It has been noticed that presence of open fractures is not the single biggest driver contributing to production. Insitu stress plays a critical role in guiding fracture mobility. To understand and determine which fractures would contribute to flow, a geomechanical study of performing the fracture stability analysis has been carried out. This generates a Mohr circle plot that defines the Mohr-Colomb shear failure criteria using the stress and critical fracture angles. Combining these three-way approaches of acoustic, image log and geomechanics, a workflow has been established for this field to identify fractures and quantify the permeable zones. This workflow has been used for two nearby wells in this field and subsequent result emphasises the utility of this method to find out sweet spots of fluid flow in fractured basement.
{"title":"Integration of Electrical Micro-Image Log, Advance 3D Acoustic Anisotropy and Fracture Stability Analysis – A Complete Solution for Basement Reservoir","authors":"Anindya Nandi, S. Sarkar, Chandreyi Chatterjee, Sourav Das, Sambit Pattanaik, Chandan Majumder, Bhopal Kumar Haldia, P. Chaturvedi, Siddharth Srivastava, M. Verma, S. Sarkar","doi":"10.2523/IPTC-19355-MS","DOIUrl":"https://doi.org/10.2523/IPTC-19355-MS","url":null,"abstract":"\u0000 Owing to the depleting reserves in the conventional reservoirs over the last few years, unconventional reservoirs have gained significant importance in the exploration of oil and gas. Basement rocks, though non-sedimentary in origin, is looked upto as one of the important unconventional reservoirs. Deccan volcanics in Kutch-Saurashtra is one such example from India. This study shows and validates a methodology of how acoustic log data can be integrated with borehole images to understand reservoir properties that governs flow. It has been noticed that presence of open fractures is not the single biggest driver contributing to production. Insitu stress plays a critical role in guiding fracture mobility. To understand and determine which fractures would contribute to flow, a geomechanical study of performing the fracture stability analysis has been carried out. This generates a Mohr circle plot that defines the Mohr-Colomb shear failure criteria using the stress and critical fracture angles. Combining these three-way approaches of acoustic, image log and geomechanics, a workflow has been established for this field to identify fractures and quantify the permeable zones. This workflow has been used for two nearby wells in this field and subsequent result emphasises the utility of this method to find out sweet spots of fluid flow in fractured basement.","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":"74948728","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}
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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