The saturation history dependent relative permeability (kr) data have been reported frequently in the laboratory investigations. Accurate estimation of kr data with hysteresis effects is crucial, specifically in Water Alternating Gas (WAG) injection which involves a sequence of drainage and imbibition cycles. Although there are a few methods to model the hysteresis effects in three-phase systems, the predicted values are still not adequate to simulate the hysteresis observed in experiments. In this study, a generalized three-phase hysteresis model was developed to simulate the observed hysteresis in the WAG experiments performed at Heriot-Watt University. It is discussed that the use of Land trapping coefficient in the hysteresis models is doubtful since it originates from the observed behaviour in two-phase systems which reach residual saturations. Hence, the new hysteresis model is developed based on innovative techniques to predict the oil and water saturation at the end of each injection cycle. Moreover, in the developed model, the formulations for estimation of hysteresis in water and gas kr data are updated to capture the observed behaviors in WAG experiments. The suggested hysteresis model was evaluated by comparing the simulation results with the available experimental data. The results showed that the developed model is able to simulate oil, water and gas production more accurately. Based on the results, the model can simulate the pressure behaviours observed in the experiments with dominated hysteresis. In addition, the developed model can predict the oil, water and gas saturations at the end of each cycle with higher accuracy compared to the available methods in the literature. The significant impacts of the hysteresis phenomenon on designing the best WAG injection scenario require a reliable hysteresis model for performing accurate reservoir simulations. The use of the suggested model elevates the accuracy of any feasibility analysis performed to evaluate the WAG injection scenario.
{"title":"Accurate Modeling of Relative Permeability Hysteresis in Water Alternating Gas Experiments","authors":"S. Aghabozorgi, M. Sohrabi","doi":"10.2118/197615-ms","DOIUrl":"https://doi.org/10.2118/197615-ms","url":null,"abstract":"\u0000 The saturation history dependent relative permeability (kr) data have been reported frequently in the laboratory investigations. Accurate estimation of kr data with hysteresis effects is crucial, specifically in Water Alternating Gas (WAG) injection which involves a sequence of drainage and imbibition cycles. Although there are a few methods to model the hysteresis effects in three-phase systems, the predicted values are still not adequate to simulate the hysteresis observed in experiments.\u0000 In this study, a generalized three-phase hysteresis model was developed to simulate the observed hysteresis in the WAG experiments performed at Heriot-Watt University. It is discussed that the use of Land trapping coefficient in the hysteresis models is doubtful since it originates from the observed behaviour in two-phase systems which reach residual saturations. Hence, the new hysteresis model is developed based on innovative techniques to predict the oil and water saturation at the end of each injection cycle. Moreover, in the developed model, the formulations for estimation of hysteresis in water and gas kr data are updated to capture the observed behaviors in WAG experiments.\u0000 The suggested hysteresis model was evaluated by comparing the simulation results with the available experimental data. The results showed that the developed model is able to simulate oil, water and gas production more accurately. Based on the results, the model can simulate the pressure behaviours observed in the experiments with dominated hysteresis. In addition, the developed model can predict the oil, water and gas saturations at the end of each cycle with higher accuracy compared to the available methods in the literature.\u0000 The significant impacts of the hysteresis phenomenon on designing the best WAG injection scenario require a reliable hysteresis model for performing accurate reservoir simulations. The use of the suggested model elevates the accuracy of any feasibility analysis performed to evaluate the WAG injection scenario.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90399074","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}
Cunliang Chen, Ming Yang, Xiaodong Han, Jianbo Zhang
Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies have been widely used with a long run time and too much information to solve this problem. Therefore, it is urgent to form a fast intelligent prediction method, especially with the development of large data processing and artificial intelligence methods. This paper proposed a new method to predict water flooding performance using big data and artificial intelligence algorithms. The method regards layered reservoir as a vertical superposition of a series of single layer reservoirs. An injection-production analysis model is established in each single layer reservoir respectively. And then a superposition model is established only by production data and logging tools data. Finally, the least square principle and the particle swarm optimization algorithm are used to optimize the model and predict water flooding performance. This method has been tested for different synthetic reservoir case studies. The results are in good agreement in comparison with the numerical simulation results. The average relative error is 4.59%, but the calculation time is only 1/10 of that of numerical simulation by using artificial intelligence method. It showed that this technique has capability to predict water flooding performance. These examples showed that the use of artificial intelligence method not only greatly shortens the working time, but also has a higher accuracy. By this paper, it is possible to predict the water flooding performance easily and accurately in reservoirs. It has an important role in the field development, increasing or decreasing investment, drilling new wells and future injection schedule.
{"title":"Water Flooding Performance Prediction in Layered Reservoir Using Big Data and Artificial Intelligence Algorithms","authors":"Cunliang Chen, Ming Yang, Xiaodong Han, Jianbo Zhang","doi":"10.2118/197585-ms","DOIUrl":"https://doi.org/10.2118/197585-ms","url":null,"abstract":"\u0000 Managing oil production from reservoirs to maximize the future economic return of the asset is an important issue in petroleum engineering. One of the most important problems is the prediction of water flooding performance. Traditional strategies have been widely used with a long run time and too much information to solve this problem. Therefore, it is urgent to form a fast intelligent prediction method, especially with the development of large data processing and artificial intelligence methods.\u0000 This paper proposed a new method to predict water flooding performance using big data and artificial intelligence algorithms. The method regards layered reservoir as a vertical superposition of a series of single layer reservoirs. An injection-production analysis model is established in each single layer reservoir respectively. And then a superposition model is established only by production data and logging tools data. Finally, the least square principle and the particle swarm optimization algorithm are used to optimize the model and predict water flooding performance.\u0000 This method has been tested for different synthetic reservoir case studies. The results are in good agreement in comparison with the numerical simulation results. The average relative error is 4.59%, but the calculation time is only 1/10 of that of numerical simulation by using artificial intelligence method. It showed that this technique has capability to predict water flooding performance. These examples showed that the use of artificial intelligence method not only greatly shortens the working time, but also has a higher accuracy.\u0000 By this paper, it is possible to predict the water flooding performance easily and accurately in reservoirs. It has an important role in the field development, increasing or decreasing investment, drilling new wells and future injection schedule.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74786997","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}
Downhole vibration remains a major challenge for drillers. Today, there is technology to look at the problem from a unique perspective. A novel look at the problem focuses on evaluation of machine learning algorithms to predict downhole vibrations. Prediction is the first step in a longer road map. The goal would be to find an optimal combination of revolutions per minute (RPM) and weight-on-bit (WOB) to remedy drilling vibration in real-time, hence closing the loop. Drilling mechanics data for thousands of wells, acquired over more than ten years was analyzed. Some preparation of the drilling mechanics data was required. Data cleaning was first performed. This included corrections for time-dependent nature of the data. Data imputing for missing values and handling of outliers and anomalies was also performed in this stage. This was followed by feature engineering which included adding variables based on company-wide drilling domain expertise. Variables to capture data patterns and variables for better capturing the time-series dependencies were also created in this stage. This paper will discuss methodologies and general rules that were tested for preparing unstructured drilling data. A few of the machine learning algorithms used as building blocks of our full solution are gradient boosting and random forest. Deep learning models were also tested and the value of these are compared. The results were compiled to decide the best algorithm which could further be used to fine-tune optimum performance. The time series aspect of the data is captured in a moving window. As the window increases, the performance of each algorithm also varied. Also, evaluation of the benefits and drawbacks of each algorithm for the drilling predictions is detailed. Ways to improve the accuracy of prediction for downhole vibrations is also suggested with reference to the results showing the logic behind all recommendations. There will be a summary of the details of each finding and a short discussion on the way forward for the industry.
{"title":"Machine Learning Lessons Learnt in Stick-Slip Prediction","authors":"Soumya Gupta, Crispin Chatar, J. Celaya","doi":"10.2118/197584-ms","DOIUrl":"https://doi.org/10.2118/197584-ms","url":null,"abstract":"\u0000 Downhole vibration remains a major challenge for drillers. Today, there is technology to look at the problem from a unique perspective. A novel look at the problem focuses on evaluation of machine learning algorithms to predict downhole vibrations. Prediction is the first step in a longer road map. The goal would be to find an optimal combination of revolutions per minute (RPM) and weight-on-bit (WOB) to remedy drilling vibration in real-time, hence closing the loop. Drilling mechanics data for thousands of wells, acquired over more than ten years was analyzed. Some preparation of the drilling mechanics data was required. Data cleaning was first performed. This included corrections for time-dependent nature of the data. Data imputing for missing values and handling of outliers and anomalies was also performed in this stage. This was followed by feature engineering which included adding variables based on company-wide drilling domain expertise. Variables to capture data patterns and variables for better capturing the time-series dependencies were also created in this stage.\u0000 This paper will discuss methodologies and general rules that were tested for preparing unstructured drilling data. A few of the machine learning algorithms used as building blocks of our full solution are gradient boosting and random forest. Deep learning models were also tested and the value of these are compared. The results were compiled to decide the best algorithm which could further be used to fine-tune optimum performance. The time series aspect of the data is captured in a moving window. As the window increases, the performance of each algorithm also varied. Also, evaluation of the benefits and drawbacks of each algorithm for the drilling predictions is detailed. Ways to improve the accuracy of prediction for downhole vibrations is also suggested with reference to the results showing the logic behind all recommendations. There will be a summary of the details of each finding and a short discussion on the way forward for the industry.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"229 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77588303","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}
Estimating the lateral heterogeneity of geochemical properties of organic rich mudrocks is important for unconventional resource plays. Mature regions can rely on abundant well data to build empirical relationships and on traditional geostatistical methods to estimate properties between wells. However, well penetration in emerging plays are sparse and so these methods will not yield good results. In this case, quantitative seismic interpretation (QSI) might be helpful in estimating the desired properties. In this study, we use QSI based on a rock physics template in estimating the uncertainty of the geochemical properties of organic mudrocks of the Shublik Formation, North Slope, Alaska. A rock physics template incorporating lithology, pore fraction, kerogen fraction, and thermal maturity is constructed and validated using well data. The template clearly shows that the inversion problem is non-unique. Inverted impedances cubes are estimated from three seismic angle gathers (near with angles between 0° and 15°, mid with angle gathers between 15° and 30°, and far with angle gathers between 30° and 45°). The inversion is done using a model-based implementation with an initial earth model derived from the seismic velocity model used in the processing phase. By combining the rock physics template and the results of seismic inversion, multiple realizations of total organic content (TOC), matrix porosity, and brittleness index are generated. These parameters can be used for sweet spot detection. Lithological results can also be used as an input for basin and petroleum system modeling.
{"title":"Quantitative Seismic Interpretation Workflow for Sweet Spot Identification in Organic-Rich Mudrocks","authors":"M. A. Ibrahim, T. Mukerji, A. Scheirer","doi":"10.2118/197290-ms","DOIUrl":"https://doi.org/10.2118/197290-ms","url":null,"abstract":"\u0000 Estimating the lateral heterogeneity of geochemical properties of organic rich mudrocks is important for unconventional resource plays. Mature regions can rely on abundant well data to build empirical relationships and on traditional geostatistical methods to estimate properties between wells. However, well penetration in emerging plays are sparse and so these methods will not yield good results. In this case, quantitative seismic interpretation (QSI) might be helpful in estimating the desired properties. In this study, we use QSI based on a rock physics template in estimating the uncertainty of the geochemical properties of organic mudrocks of the Shublik Formation, North Slope, Alaska. A rock physics template incorporating lithology, pore fraction, kerogen fraction, and thermal maturity is constructed and validated using well data. The template clearly shows that the inversion problem is non-unique. Inverted impedances cubes are estimated from three seismic angle gathers (near with angles between 0° and 15°, mid with angle gathers between 15° and 30°, and far with angle gathers between 30° and 45°). The inversion is done using a model-based implementation with an initial earth model derived from the seismic velocity model used in the processing phase. By combining the rock physics template and the results of seismic inversion, multiple realizations of total organic content (TOC), matrix porosity, and brittleness index are generated. These parameters can be used for sweet spot detection. Lithological results can also be used as an input for basin and petroleum system modeling.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"53 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77213664","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 Pengerang Integrated Complex (PIC) is a US$27b multi package fast-tracked mega project with execution period of 60 months to ‘Ready for Start-up’ in Q1 2019. One of the largest project of its kind in the world constructed at a single time and location, PIC consists of a Refinery and Petrochemical Integrated Development (RAPID) and supporting facilities. A successful integrated system schedule was established towards achieving on-time project completion by early 2019. The key challenges lie in the immense complexity of the project with involvement of over 140 organizations including more than 40 EPCC packages, supported by 13 licensors and over 200 suppliers and Contractors, around 250k EPCC schedule line items and alignment of 117 Integrated Networks across over 30 main process units. To drive a successful system schedule integration, three key guiding principles were followed: a pre-emptive schedule management guideline for systemization and start-up sequence, integrated critical path schedule alignment engagements with EPCC Contractors and a lean task force collaboration by project planning and operation on prioritized critical utilities delivery for EPCC commissioning activities. As a result of the successful system schedule alignment and integration, the critical utilities i.e. power, steam, cooling water, fuel gas and fire water, were successfully delivered in quick successions according to the respective EPCC packages needs, allowing commissioning activities within the packages and the Integrated Networks crossing over multiple EPCC packages. The cohesive pre-commissioning, commissioning and start-up schedule between all EPCC packages propelled PIC project to a momentous "Finish Strong". The critical success factor comprising of early creation of special task force driven by project planning and operation personnel in managing system schedule prioritization and strong collaboration with EPCC Contractors, maybe the answer to future projects looking for integrated smart solutions.
{"title":"Successful System Schedule Integration for a Complex Multi Package Mega Project","authors":"Sahaiza Saat","doi":"10.2118/197376-ms","DOIUrl":"https://doi.org/10.2118/197376-ms","url":null,"abstract":"\u0000 The Pengerang Integrated Complex (PIC) is a US$27b multi package fast-tracked mega project with execution period of 60 months to ‘Ready for Start-up’ in Q1 2019. One of the largest project of its kind in the world constructed at a single time and location, PIC consists of a Refinery and Petrochemical Integrated Development (RAPID) and supporting facilities. A successful integrated system schedule was established towards achieving on-time project completion by early 2019.\u0000 The key challenges lie in the immense complexity of the project with involvement of over 140 organizations including more than 40 EPCC packages, supported by 13 licensors and over 200 suppliers and Contractors, around 250k EPCC schedule line items and alignment of 117 Integrated Networks across over 30 main process units. To drive a successful system schedule integration, three key guiding principles were followed: a pre-emptive schedule management guideline for systemization and start-up sequence, integrated critical path schedule alignment engagements with EPCC Contractors and a lean task force collaboration by project planning and operation on prioritized critical utilities delivery for EPCC commissioning activities.\u0000 As a result of the successful system schedule alignment and integration, the critical utilities i.e. power, steam, cooling water, fuel gas and fire water, were successfully delivered in quick successions according to the respective EPCC packages needs, allowing commissioning activities within the packages and the Integrated Networks crossing over multiple EPCC packages. The cohesive pre-commissioning, commissioning and start-up schedule between all EPCC packages propelled PIC project to a momentous \"Finish Strong\".\u0000 The critical success factor comprising of early creation of special task force driven by project planning and operation personnel in managing system schedule prioritization and strong collaboration with EPCC Contractors, maybe the answer to future projects looking for integrated smart solutions.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76047625","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}
Shaikha Al Jenaibi, Tasnim Al Mzaini, L. Saputelli, H. Hafez, Carlos Mata, R. Narayanan, K. Mogensen, R. Mohan, Frank Charles, Z. Mammadov, Alvaro Escorcia, G. Mijares, J. Rodriguez, Cristina Hernandez
Meeting energy demands and generating profit to shareholders is a continuous quest for oil and gas companies. Production and business planning in integrated oil and gas operating companies is a complex process involving numerous organizations, historic data collection, modeling, prediction, and forecasting. Integrated business planning complexity intensifies due to the uncertain nature of past facts and future conditions. We propose a framework for integrating upstream and downstream production planning processes using data-driven models representing the upstream capacities, downstream processes, and a countrywide profit model. The upstream production model forecasts optimum capacity scenarios of the reservoir fluids with their compositional characteristics and hydraulic performance of the surface facilities while honoring business rules, and based on the various long-term expenditure scenarios, downtime requirements, and downstream demand schedules. An integrated optimization model for value chain has the potential to protect profitability for oil and gas companies in times of unbalanced market forces.
{"title":"Value Chain Optimization in Oil & Gas Companies – Integrated Workflows","authors":"Shaikha Al Jenaibi, Tasnim Al Mzaini, L. Saputelli, H. Hafez, Carlos Mata, R. Narayanan, K. Mogensen, R. Mohan, Frank Charles, Z. Mammadov, Alvaro Escorcia, G. Mijares, J. Rodriguez, Cristina Hernandez","doi":"10.2118/197925-ms","DOIUrl":"https://doi.org/10.2118/197925-ms","url":null,"abstract":"\u0000 Meeting energy demands and generating profit to shareholders is a continuous quest for oil and gas companies. Production and business planning in integrated oil and gas operating companies is a complex process involving numerous organizations, historic data collection, modeling, prediction, and forecasting. Integrated business planning complexity intensifies due to the uncertain nature of past facts and future conditions.\u0000 We propose a framework for integrating upstream and downstream production planning processes using data-driven models representing the upstream capacities, downstream processes, and a countrywide profit model.\u0000 The upstream production model forecasts optimum capacity scenarios of the reservoir fluids with their compositional characteristics and hydraulic performance of the surface facilities while honoring business rules, and based on the various long-term expenditure scenarios, downtime requirements, and downstream demand schedules.\u0000 An integrated optimization model for value chain has the potential to protect profitability for oil and gas companies in times of unbalanced market forces.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79741886","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}
E. Spelta, V. Caronni, G. Carrasquero, M. Catanzaro, M. Rossi, R. L. Tagliamonte, A. Valdisturlo
A robust and detailed reservoir model is an essential requirement when a fast track approach drives the development of a green field. Such a tool can only be developed through the orchestration of Geological and Geophysical (G&G) and Reservoir Engineering disciplines. This integration effort is, first of all, aimed at identifying the key characteristics of the reservoir most impacting its dynamic behavior at different scale and, eventually, at capturing them with the proper modelling approach. This paper decribes such approach to the case of a complex deep-water reservoir belonging to slope-toe of slope environment. A 3D integrated static model was built by incorporating core and log data, their petrophysical interpretation, a description of the depositional and architectural elements, a quantitative seismic reservoir characterization and the few dynamic information available at this early development stage. The implemented geomodeling workflow focused on heterogenetiy that could affect reservoir performance such as structural-stratigraphic discontinuities that could act as hydraulic barriers. Facies in the interwell space were distributed by applying seismic-derived 3D trends. Facies distribution eventually provided the framework within which petrophysical properties modelling was performed. During the implementation of this integrated G&G and Reservoir workflow, continuous crosschecks of consistency and robustness of the model led to elaborate the final product. The resulting reservoir model captured critical uncertainties (e.g. degree of reservoir heterogeneity including stratigraphic discontinuities) leading to an optimized development scheme, that allowed to minimize risks, despite the few data available.
{"title":"When Effective Integration Drives the Development: A Successful Case History","authors":"E. Spelta, V. Caronni, G. Carrasquero, M. Catanzaro, M. Rossi, R. L. Tagliamonte, A. Valdisturlo","doi":"10.2118/197900-ms","DOIUrl":"https://doi.org/10.2118/197900-ms","url":null,"abstract":"\u0000 A robust and detailed reservoir model is an essential requirement when a fast track approach drives the development of a green field. Such a tool can only be developed through the orchestration of Geological and Geophysical (G&G) and Reservoir Engineering disciplines. This integration effort is, first of all, aimed at identifying the key characteristics of the reservoir most impacting its dynamic behavior at different scale and, eventually, at capturing them with the proper modelling approach.\u0000 This paper decribes such approach to the case of a complex deep-water reservoir belonging to slope-toe of slope environment. A 3D integrated static model was built by incorporating core and log data, their petrophysical interpretation, a description of the depositional and architectural elements, a quantitative seismic reservoir characterization and the few dynamic information available at this early development stage.\u0000 The implemented geomodeling workflow focused on heterogenetiy that could affect reservoir performance such as structural-stratigraphic discontinuities that could act as hydraulic barriers. Facies in the interwell space were distributed by applying seismic-derived 3D trends. Facies distribution eventually provided the framework within which petrophysical properties modelling was performed. During the implementation of this integrated G&G and Reservoir workflow, continuous crosschecks of consistency and robustness of the model led to elaborate the final product.\u0000 The resulting reservoir model captured critical uncertainties (e.g. degree of reservoir heterogeneity including stratigraphic discontinuities) leading to an optimized development scheme, that allowed to minimize risks, despite the few data available.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81191781","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}
Optimizing resources and pushing the drilling limits to tap into deeper reservoirs at minimal cost has always been the primary objective of many operators worldwide. Moreover, the prolonged current market conditions are pressurizing every stakeholder involved within the well-delivery process to reduce time and the associated costs like never before. This paper deals with an Offshore Artificial Island project where the drilling limits were constantly challenged by adopting new technologies and practices in an extended-reach drilling (ERD) campaign. The complexity of these extended-reach wells was managed effectively with excellent planning and execution. Implementation of new and existing technologies and the adoption of revamped operational practices has managed the challenges of equipment capabilities, torque and drag, ECD, wellbore stability, hole cleaning and stuck pipe avoidance to name a few. The project drilled longer wells at less costs. This approach has resulted in drilling and completion of wells comfortably within the equipment-rating envelope. Additional technological means such as newly developed lubricant and mechanical drill pipe torque reducer subs helped reduce the friction factor and eliminate drill string buckling. Existing technology in the bottom-hole assembly (BHA) minimized the tortuosity in the wellbore, along with transmitted real-time downhole drilling data (Torque, Weight on Bit, Mechanical Specific Energy, and Equivalent Circulating Density) which helped in active drilling parameters optimization for efficient drilling. Similar technologies and practices were used in landing the completion string. The geo-mechanical studies undertaken at the concept stage and later revised against the offset well information helped in drilling the troublesome shale formations with no associated events. Specific importance was given to maximizing the hole cleaning by having the right tools in the BHA that could accommodate higher flow rates while using a tandem drill string for lower hydraulics. In addition, the newly formulated field / formation specific drilling and reaming practices minimized the stuck pipe, saving approximately 10% in overall well costs. This paper discusses the successful drilling of a number of offshore ERD wells with various complexities and tailored solutions with minimal downhole problems and within continuously revising planned times and budgets. The lessons learned and techniques associated with drilling of extended-reach wells at lower costs will be detailed in this paper. This information would give insights and considerations to all stakeholders who intend to drill extended reach wells or challenge their current limiters. This proven successful methodology and its results are considered a benchmark for the nearby fields in the region.
{"title":"Technologies and Practices to Push The Extended Reach Drilling Envelope within The Existing Constraints","authors":"Phalgun Paila, R. Singh, Kashif Abid","doi":"10.2118/197123-ms","DOIUrl":"https://doi.org/10.2118/197123-ms","url":null,"abstract":"\u0000 Optimizing resources and pushing the drilling limits to tap into deeper reservoirs at minimal cost has always been the primary objective of many operators worldwide. Moreover, the prolonged current market conditions are pressurizing every stakeholder involved within the well-delivery process to reduce time and the associated costs like never before. This paper deals with an Offshore Artificial Island project where the drilling limits were constantly challenged by adopting new technologies and practices in an extended-reach drilling (ERD) campaign.\u0000 The complexity of these extended-reach wells was managed effectively with excellent planning and execution. Implementation of new and existing technologies and the adoption of revamped operational practices has managed the challenges of equipment capabilities, torque and drag, ECD, wellbore stability, hole cleaning and stuck pipe avoidance to name a few. The project drilled longer wells at less costs. This approach has resulted in drilling and completion of wells comfortably within the equipment-rating envelope. Additional technological means such as newly developed lubricant and mechanical drill pipe torque reducer subs helped reduce the friction factor and eliminate drill string buckling. Existing technology in the bottom-hole assembly (BHA) minimized the tortuosity in the wellbore, along with transmitted real-time downhole drilling data (Torque, Weight on Bit, Mechanical Specific Energy, and Equivalent Circulating Density) which helped in active drilling parameters optimization for efficient drilling. Similar technologies and practices were used in landing the completion string.\u0000 The geo-mechanical studies undertaken at the concept stage and later revised against the offset well information helped in drilling the troublesome shale formations with no associated events. Specific importance was given to maximizing the hole cleaning by having the right tools in the BHA that could accommodate higher flow rates while using a tandem drill string for lower hydraulics. In addition, the newly formulated field / formation specific drilling and reaming practices minimized the stuck pipe, saving approximately 10% in overall well costs.\u0000 This paper discusses the successful drilling of a number of offshore ERD wells with various complexities and tailored solutions with minimal downhole problems and within continuously revising planned times and budgets. The lessons learned and techniques associated with drilling of extended-reach wells at lower costs will be detailed in this paper. This information would give insights and considerations to all stakeholders who intend to drill extended reach wells or challenge their current limiters. This proven successful methodology and its results are considered a benchmark for the nearby fields in the region.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86542950","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}
Xingcai Wu, Yongli Wang, A. Naabi, Hanbing Xu, Ibrahim S. Al Sinani, K. Busaidi, S. A. Jabri, S. Dhahab, Jianli Zhang, C. Xiong, Ye Yinzhu, X. Tian, Xu Jia, Jing Lv
The field under study is located in the northern part of Oman where most of the fields have a tight carbonate oil reservoirs. Initially the field was produced under natural depletion for almost 15 years until 2005 when a line drive water flood development with horizontal wells took place and was deployed in the whole field. After more than 10 years of water injection, the water cut reached an average of 75% in the major producing blocks. The reservoir has a light oil with viscosity of 0.8 mPa.s, a downhole temperature of 87°C and average permeability of 10 mD. The calcium and magnesium concentration in formation water is high, about 4000 mg/L. Reservoir heterogeneity in tight carbonate reservoirs causes uneven water flood sweep efficiency and hence resulted in a lot of bypassed oil. The initial EOR methods screening in the field under study didn't recommend to use the conventional polymer flooding due to low reservoir permeability and hence injectivity challenge. However, a new unique nano-ploymer was recently developed in the market to be a potential EOR method for such tight formation reservoirs. Extensive laboratory experiments using the core and fluid samples from the studied reservoir followed by numerical simulation modeling work proved the technical feasibility for this new polymer. This was then followed by field testing pilot in one of the matured water flood sector and the performance is currently under monitoring. The new polymer is a particle-type and comes with various nanometer-micrometer sizes. This polymer has a low apparent viscosity of 1-4 mPa and when it is mixed with the injection water, the particles disperse in the water and the resultant mixture has a low viscosity making it easily to be injected. In addition, this nano-polymer has a high tolerance for both temperature and salinity. While the particles move into formation, they temporarily plug the preferential existing water paths and divert the injection water into the relatively small pores/throats and displace the remaining bypassed oil. The polymer particle has high deformation capacity, so it can deform and pass through the throat under certain pressure to plug even deeper parts of the formation. The process is repeated continuously so that it can inhibit water production and enhance oil production. For the lab experiments, 12 core plugs from the associated reservoir were collected, based on which, a series of experiments were conducted including: core thin section analysis, injectivity test for the nano-polymer and core flooding experiments on single plug and parallel double plugs. Subsequently, the lab results were utilized for numerical simulation and that was followed by economic evaluation. Based on the lab test results, a conceptual simulation model for the studiedfield's sector was used to estimate the incremental oil gain at different pore volume (PV) injection. The incremental oil gain was determined at different SMG PV injection starting from 0.05PV to
{"title":"A New Polymer Flooding Technology for Improving Low Permeability Carbonate Reservoir Recovery--From Lab Study to Pilot Test--Case Study from Oman","authors":"Xingcai Wu, Yongli Wang, A. Naabi, Hanbing Xu, Ibrahim S. Al Sinani, K. Busaidi, S. A. Jabri, S. Dhahab, Jianli Zhang, C. Xiong, Ye Yinzhu, X. Tian, Xu Jia, Jing Lv","doi":"10.2118/197912-ms","DOIUrl":"https://doi.org/10.2118/197912-ms","url":null,"abstract":"\u0000 The field under study is located in the northern part of Oman where most of the fields have a tight carbonate oil reservoirs. Initially the field was produced under natural depletion for almost 15 years until 2005 when a line drive water flood development with horizontal wells took place and was deployed in the whole field. After more than 10 years of water injection, the water cut reached an average of 75% in the major producing blocks. The reservoir has a light oil with viscosity of 0.8 mPa.s, a downhole temperature of 87°C and average permeability of 10 mD. The calcium and magnesium concentration in formation water is high, about 4000 mg/L.\u0000 Reservoir heterogeneity in tight carbonate reservoirs causes uneven water flood sweep efficiency and hence resulted in a lot of bypassed oil. The initial EOR methods screening in the field under study didn't recommend to use the conventional polymer flooding due to low reservoir permeability and hence injectivity challenge. However, a new unique nano-ploymer was recently developed in the market to be a potential EOR method for such tight formation reservoirs. Extensive laboratory experiments using the core and fluid samples from the studied reservoir followed by numerical simulation modeling work proved the technical feasibility for this new polymer. This was then followed by field testing pilot in one of the matured water flood sector and the performance is currently under monitoring.\u0000 The new polymer is a particle-type and comes with various nanometer-micrometer sizes. This polymer has a low apparent viscosity of 1-4 mPa and when it is mixed with the injection water, the particles disperse in the water and the resultant mixture has a low viscosity making it easily to be injected. In addition, this nano-polymer has a high tolerance for both temperature and salinity. While the particles move into formation, they temporarily plug the preferential existing water paths and divert the injection water into the relatively small pores/throats and displace the remaining bypassed oil. The polymer particle has high deformation capacity, so it can deform and pass through the throat under certain pressure to plug even deeper parts of the formation. The process is repeated continuously so that it can inhibit water production and enhance oil production.\u0000 For the lab experiments, 12 core plugs from the associated reservoir were collected, based on which, a series of experiments were conducted including: core thin section analysis, injectivity test for the nano-polymer and core flooding experiments on single plug and parallel double plugs. Subsequently, the lab results were utilized for numerical simulation and that was followed by economic evaluation.\u0000 Based on the lab test results, a conceptual simulation model for the studiedfield's sector was used to estimate the incremental oil gain at different pore volume (PV) injection. The incremental oil gain was determined at different SMG PV injection starting from 0.05PV to","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89091461","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}
Luisa Caluri, Mirela Jianu, Paolo Cerioli, G. Silvestri
The purpose of this paper is to describe the process employed by Eni to enable digital innovation across the organization. In particular, it focuses on illustrating the reinforcing role played by the unit dedicated to the open innovation initiatives in identifying the most promising digital solutions and innovative providers. The present paper analyzes the case of Eni, one of the biggest oil companies in the world and among the first esatblishers of a Digital Unit that reports directly to the CEO, based on an internal study using the qualitative methodology. A description of processes for development / execution of open innovation initiatives is provided, alongside a view on the main stakeholders involved that make this approach a value creation driver. The paper tackles also elements of novelty and efficiency regarding both the organizational (structural) aspects and the work, as well as information flows within the company and across the departments involved. Ultimately, this case study is meant to illustrate the main features and success factors that made Eni Digital Open Innovation Unit effective in promoting an organizational and cultural development and by this supporting the digital transformation and encouraging corporate dynamism. Valuable insights can be therefore drawn on how organizational and procedural decisions, adoption of new instruments and ways of working through digital empowerment and open innovation philosophy dissiminated by the Digital Department can boost the attainable results throughout the organization.
{"title":"Open Innovation as Enabling Paradigm to Empower Digital Transformation in Oil & Gas Organizations","authors":"Luisa Caluri, Mirela Jianu, Paolo Cerioli, G. Silvestri","doi":"10.2118/197904-ms","DOIUrl":"https://doi.org/10.2118/197904-ms","url":null,"abstract":"\u0000 The purpose of this paper is to describe the process employed by Eni to enable digital innovation across the organization. In particular, it focuses on illustrating the reinforcing role played by the unit dedicated to the open innovation initiatives in identifying the most promising digital solutions and innovative providers.\u0000 The present paper analyzes the case of Eni, one of the biggest oil companies in the world and among the first esatblishers of a Digital Unit that reports directly to the CEO, based on an internal study using the qualitative methodology.\u0000 A description of processes for development / execution of open innovation initiatives is provided, alongside a view on the main stakeholders involved that make this approach a value creation driver. The paper tackles also elements of novelty and efficiency regarding both the organizational (structural) aspects and the work, as well as information flows within the company and across the departments involved.\u0000 Ultimately, this case study is meant to illustrate the main features and success factors that made Eni Digital Open Innovation Unit effective in promoting an organizational and cultural development and by this supporting the digital transformation and encouraging corporate dynamism. Valuable insights can be therefore drawn on how organizational and procedural decisions, adoption of new instruments and ways of working through digital empowerment and open innovation philosophy dissiminated by the Digital Department can boost the attainable results throughout the organization.","PeriodicalId":11091,"journal":{"name":"Day 3 Wed, November 13, 2019","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-11-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89644696","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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