It has been a long tradition, in China, the undergraduate and graduate petroleum engineering courses are taught in Chinese. As the globalization playing an important role in our lives, it has become more and more obvious, in many people's point of view, the education quality provided through those Chinese petroleum universities should also be matched with the international standards, such as ABET criteria. At 2014, the China University of Petroleum Beijing launched a program called the ABET accreditation preparation program. The primary goal of this program is to prepare the ABET accreditation through the transforming of the traditional Chinese-taught Petroleum Engineering courses into English-taught Petroleum Engineering courses to meet ABET standards. At phase 1 of this program, 2014-2015, only two courses (Reservoir Engineering course and Petrophysics course) were chosen to experiment the new concept. Upon the completion of phase 1, the two courses ranked top 5% among all the courses offered by the Petroleum Engineering Department in terms of its popularity among students. Based on the success of phase 1, at phase 2 (2016-now), additional 4 courses were added into this program. Those 4 courses are: Well Completion Design, Flow in Porous Media, Production Engineering, and Reservoir Simulation. This paper provides the lesson learned through the 5 years’ experience of setting up the new norm by fundamentally changing the ways of teaching in an environment where native language is not English. The specific details of "Know-how" through the execution of phase1 and phase 2 are presented, analyzed and discussed. The paper addressed the obstacles encountered within the program, the new teaching methods conducted in those classrooms and student's response to those brand-new English taught Petroleum Engineering courses. The experience obtained through the ABET preparation program at China University of Petroleum Beijing may provide some guidance for those who to pursuit the same goal of seeking international recognition and establishing an international learning environment for their Petroleum Engineering courses.
{"title":"Transforming Traditional Chinese-Taught Petroleum Engineering Courses into English-Taught Petroleum Engineering Courses to Meet ABET Standards","authors":"W. Qin, Ying Yuan, Fei Wang, Zhouyuan Zhu","doi":"10.2118/195826-ms","DOIUrl":"https://doi.org/10.2118/195826-ms","url":null,"abstract":"It has been a long tradition, in China, the undergraduate and graduate petroleum engineering courses are taught in Chinese. As the globalization playing an important role in our lives, it has become more and more obvious, in many people's point of view, the education quality provided through those Chinese petroleum universities should also be matched with the international standards, such as ABET criteria. At 2014, the China University of Petroleum Beijing launched a program called the ABET accreditation preparation program. The primary goal of this program is to prepare the ABET accreditation through the transforming of the traditional Chinese-taught Petroleum Engineering courses into English-taught Petroleum Engineering courses to meet ABET standards. At phase 1 of this program, 2014-2015, only two courses (Reservoir Engineering course and Petrophysics course) were chosen to experiment the new concept. Upon the completion of phase 1, the two courses ranked top 5% among all the courses offered by the Petroleum Engineering Department in terms of its popularity among students. Based on the success of phase 1, at phase 2 (2016-now), additional 4 courses were added into this program. Those 4 courses are: Well Completion Design, Flow in Porous Media, Production Engineering, and Reservoir Simulation. This paper provides the lesson learned through the 5 years’ experience of setting up the new norm by fundamentally changing the ways of teaching in an environment where native language is not English. The specific details of \"Know-how\" through the execution of phase1 and phase 2 are presented, analyzed and discussed. The paper addressed the obstacles encountered within the program, the new teaching methods conducted in those classrooms and student's response to those brand-new English taught Petroleum Engineering courses. The experience obtained through the ABET preparation program at China University of Petroleum Beijing may provide some guidance for those who to pursuit the same goal of seeking international recognition and establishing an international learning environment for their Petroleum Engineering courses.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81101005","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}
Shale reservoirs are estimated to account for approximately 10-30% of oil and gas worldwide, yet operators rarely produce more than 10% of the original hydrocarbons in place from them. These poor production numbers are a result of the assumption that the same pressure-volume-temperature (PVT) analysis procedures that are employed in conventional reservoirs are also applicable to shale and tight reservoirs. However, traditional PVT analysis does not account for the nanoporosity of the shale and, therefore, neglects the ability of nanopores to significantly alter the phase behavior of reservoir fluids. To quantify the effects of shale nanoporosity on the phase behavior of reservoir fluids, a novel gravimetric apparatus was developed. Unlike other gravimetric apparatuses in the literature, ours is compatible with both simple and complex experimental fluids and up to several hundred grams of unconsolidated or consolidated porous media at temperatures and pressures up to 232ᵒC and 5,000 psi, respectively. Furthermore, our apparatus does not require a buoyant force correction, which is one of the major shortcomings of most commercially available gravimetric apparatuses. These unique features allow us to study fluid phase behavior in shale and tight cores with high accuracy and efficiency. In the course of an exhaustive three-year research program, we have used this apparatus to measure the first capillary condensation isotherm for a fluid mixture with more than two components and discovered new phenomena of capillary condensed and supercritical fluids in the nanopores of shale rock and synthetic porous media. By reviewing the works produced over the course of this research, we are now able to answer longstanding questions as to when and how nanoconfinement-induced phase behavior occur in shale reservoirs and the implications that different types of phase behavior, including capillary condensation and nanoconfined supercriticality, have for oil and gas production.
{"title":"Capillary Condensation in Shale: A Narrative Review","authors":"E. Barsotti","doi":"10.2118/199768-stu","DOIUrl":"https://doi.org/10.2118/199768-stu","url":null,"abstract":"\u0000 Shale reservoirs are estimated to account for approximately 10-30% of oil and gas worldwide, yet operators rarely produce more than 10% of the original hydrocarbons in place from them. These poor production numbers are a result of the assumption that the same pressure-volume-temperature (PVT) analysis procedures that are employed in conventional reservoirs are also applicable to shale and tight reservoirs. However, traditional PVT analysis does not account for the nanoporosity of the shale and, therefore, neglects the ability of nanopores to significantly alter the phase behavior of reservoir fluids. To quantify the effects of shale nanoporosity on the phase behavior of reservoir fluids, a novel gravimetric apparatus was developed. Unlike other gravimetric apparatuses in the literature, ours is compatible with both simple and complex experimental fluids and up to several hundred grams of unconsolidated or consolidated porous media at temperatures and pressures up to 232ᵒC and 5,000 psi, respectively. Furthermore, our apparatus does not require a buoyant force correction, which is one of the major shortcomings of most commercially available gravimetric apparatuses. These unique features allow us to study fluid phase behavior in shale and tight cores with high accuracy and efficiency. In the course of an exhaustive three-year research program, we have used this apparatus to measure the first capillary condensation isotherm for a fluid mixture with more than two components and discovered new phenomena of capillary condensed and supercritical fluids in the nanopores of shale rock and synthetic porous media. By reviewing the works produced over the course of this research, we are now able to answer longstanding questions as to when and how nanoconfinement-induced phase behavior occur in shale reservoirs and the implications that different types of phase behavior, including capillary condensation and nanoconfined supercriticality, have for oil and gas production.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"2015 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83165442","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. Joshi, A. Eustes, J. Rostami, C. Gottschalk, C. Dreyer, Wenpeng Liu, Z. Zody, C. Bottini
Water is considered the ‘oil of space’ with applications ranging from fuel production to colony consumption. Recent findings suggested the presence of water-ice in the Permanently shadowed craters on Lunar poles. This water present on the Moon and other planetary bodies can significantly bring down the cost of space exploration, fueling the colonization of the solar system. With low-resolution orbital data available, the next step is to drill and analyze samples from the Moon. An extensive review of drilling systems designed by NASA was conducted focusing on the effect of different planetary environments on the drill design. Inspired by this and the drilling systems developed in the petroleum industry, an auger based rotary drilling rig was designed and fabricated with an extensive high-frequency data acquisition system, measuring all essential drilling parameters. Several analog rocks were cast with regolith simulant grout to replicate different subsurface geotechnical properties in the Lunar polar craters. The drill was tested on samples with different geotechnical properties to account for the varying properties expected in the Lunar poles. Application of the drilling engineering concepts has resulted in the development of a robust drilling system capable of replicating drilling process for different planetary environments like the Moon and Mars. Using the data acquisition system on the rig, an advanced machine learning algorithm capable of processing and analyzing the real-time high-frequency drilling data to estimate a sample's geotechnical properties and water content was created. The evolving algorithm was developed based on initial drilling tests on homogenous and heterogeneous analogs. It was tested on samples with varying heterogeneity to estimate the geotechnical properties and the water content accurately. With some modifications, this algorithm can be applied in the Lunar and Martian missions to estimate the geotechnical properties in real-time, without the need to analyze the subsurface samples on the surface. This can result in a cost-effective exploration of water-ice resources on the Moon and Mars, kickstarting the space resources industry and the human colonization on those planetary bodies. The expertise of the drilling engineers in designing and executing wells in extreme terrestrial environments can help create significantly effective drilling systems for extraterrestrial environments. This work details the design considerations to drill on the Moon and other planetary bodies focusing specifically on the application of drilling data to evaluate geotechnical properties and water content at Lunar polar conditions. The techniques developed here might pay a vital role in understanding the extent and composition of water-ice on the Moon, leading to efficient colonization of the solar system.
{"title":"How Can Drilling Engineers Help Revolutionize Space Transport and Colonize the Solar System: Focusing on Lunar Water-Ice","authors":"D. Joshi, A. Eustes, J. Rostami, C. Gottschalk, C. Dreyer, Wenpeng Liu, Z. Zody, C. Bottini","doi":"10.2118/195803-ms","DOIUrl":"https://doi.org/10.2118/195803-ms","url":null,"abstract":"\u0000 Water is considered the ‘oil of space’ with applications ranging from fuel production to colony consumption. Recent findings suggested the presence of water-ice in the Permanently shadowed craters on Lunar poles. This water present on the Moon and other planetary bodies can significantly bring down the cost of space exploration, fueling the colonization of the solar system. With low-resolution orbital data available, the next step is to drill and analyze samples from the Moon.\u0000 An extensive review of drilling systems designed by NASA was conducted focusing on the effect of different planetary environments on the drill design. Inspired by this and the drilling systems developed in the petroleum industry, an auger based rotary drilling rig was designed and fabricated with an extensive high-frequency data acquisition system, measuring all essential drilling parameters. Several analog rocks were cast with regolith simulant grout to replicate different subsurface geotechnical properties in the Lunar polar craters. The drill was tested on samples with different geotechnical properties to account for the varying properties expected in the Lunar poles.\u0000 Application of the drilling engineering concepts has resulted in the development of a robust drilling system capable of replicating drilling process for different planetary environments like the Moon and Mars. Using the data acquisition system on the rig, an advanced machine learning algorithm capable of processing and analyzing the real-time high-frequency drilling data to estimate a sample's geotechnical properties and water content was created. The evolving algorithm was developed based on initial drilling tests on homogenous and heterogeneous analogs. It was tested on samples with varying heterogeneity to estimate the geotechnical properties and the water content accurately. With some modifications, this algorithm can be applied in the Lunar and Martian missions to estimate the geotechnical properties in real-time, without the need to analyze the subsurface samples on the surface. This can result in a cost-effective exploration of water-ice resources on the Moon and Mars, kickstarting the space resources industry and the human colonization on those planetary bodies. The expertise of the drilling engineers in designing and executing wells in extreme terrestrial environments can help create significantly effective drilling systems for extraterrestrial environments.\u0000 This work details the design considerations to drill on the Moon and other planetary bodies focusing specifically on the application of drilling data to evaluate geotechnical properties and water content at Lunar polar conditions. The techniques developed here might pay a vital role in understanding the extent and composition of water-ice on the Moon, leading to efficient colonization of the solar system.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89535494","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 tackle an old problem – production forecast - using techniques that are relatively new to the reservoir engineer toolbox. The problem at hand consists of forecasting oil production in a mature onshore field simultaneously driven by water and steam injection. However, instead of turning to traditional methods, we deploy machine-learning algorithms which will feed on a plethora of historical data to extract hidden patterns and underlying relationships with a view to forecasting oil rate. No geological model and/or numerical reservoir simulators will be needed, only 3 sets of time-series: injection history, production history and number of producers. Two Machine-Learning algorithms are used: linear-regression and recurrent neural networks.
{"title":"Machine Learning Forecasts Oil Rate in Mature Onshore Field Jointly Driven by Water and Steam Injection","authors":"L. Kubota, Danilo Reinert","doi":"10.2118/196152-ms","DOIUrl":"https://doi.org/10.2118/196152-ms","url":null,"abstract":"\u0000 In this paper, we tackle an old problem – production forecast - using techniques that are relatively new to the reservoir engineer toolbox. The problem at hand consists of forecasting oil production in a mature onshore field simultaneously driven by water and steam injection. However, instead of turning to traditional methods, we deploy machine-learning algorithms which will feed on a plethora of historical data to extract hidden patterns and underlying relationships with a view to forecasting oil rate. No geological model and/or numerical reservoir simulators will be needed, only 3 sets of time-series: injection history, production history and number of producers. Two Machine-Learning algorithms are used: linear-regression and recurrent neural networks.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"87 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84041255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A set of different numerical algorithms for non-Darcy flow models is developed and compared to each other in order to estimate functionality of algorithms and their potential of embedding into existing reservoir simulation software. In addition, a question of using such updated software to study an applicability of various non-Darcy flow models for unconventional reservoirs is discussed. The approaches are based on generalization of a linear Darcy law in which a flow equation is modified by nonlinear expressions of a flow rate and other reservoir values, so various formulations of non-Darcy flows from different research papers can be described as particular cases of such a general formula. Next, this generalized flow equation is applied to the modified black-oil equations, but an exclusion of a flow rate as unknown is impossible due to properties of the generalization. A finite volume discretization and Newton linearization are performed, and several techniques of computationally efficient solution are observed. A prototype of reservoir simulation program based on obtained mathematical model is constructed. Several numerical experiments are performed in order to verify numerical solutions and applied algorithms. Convergence rates of calculations by different approaches to non-Darcy flows are studied. The most significant finding is an existence of common approaches to exclude discretized and linearized flow equations at each iteration of nonlinear solver. This is important due to a presence of different non-Darcy models derived from different prerequisites (such as Forchheimer quadratic law and power law for non-Newtonian fluid) which can be studied through general algorithm as a research framework. Equally important is that the developed approaches are practically efficient and could be implemented in previously developed software without significant rearrangement of their code and algorithms in order to immediately gain practically useful simulations of non-Darcy flows or to explore their applicability, which is still an issue to resolve. The novelty of the considered approaches is in ability to embed non-Darcy flow models into present reservoir simulation software keeping most of existing algorithms and data structures implemented. Taking into account that the algorithms are based on a generalized form of non-Darcy flows, it is possible to calculate a wide range of models preserving computational complexity.
{"title":"Numerical Simulation of Multiphase Non-Darcy Flows: Generalized Approach","authors":"M. Elizarev","doi":"10.2118/199769-stu","DOIUrl":"https://doi.org/10.2118/199769-stu","url":null,"abstract":"\u0000 A set of different numerical algorithms for non-Darcy flow models is developed and compared to each other in order to estimate functionality of algorithms and their potential of embedding into existing reservoir simulation software. In addition, a question of using such updated software to study an applicability of various non-Darcy flow models for unconventional reservoirs is discussed.\u0000 The approaches are based on generalization of a linear Darcy law in which a flow equation is modified by nonlinear expressions of a flow rate and other reservoir values, so various formulations of non-Darcy flows from different research papers can be described as particular cases of such a general formula. Next, this generalized flow equation is applied to the modified black-oil equations, but an exclusion of a flow rate as unknown is impossible due to properties of the generalization. A finite volume discretization and Newton linearization are performed, and several techniques of computationally efficient solution are observed.\u0000 A prototype of reservoir simulation program based on obtained mathematical model is constructed. Several numerical experiments are performed in order to verify numerical solutions and applied algorithms. Convergence rates of calculations by different approaches to non-Darcy flows are studied. The most significant finding is an existence of common approaches to exclude discretized and linearized flow equations at each iteration of nonlinear solver. This is important due to a presence of different non-Darcy models derived from different prerequisites (such as Forchheimer quadratic law and power law for non-Newtonian fluid) which can be studied through general algorithm as a research framework. Equally important is that the developed approaches are practically efficient and could be implemented in previously developed software without significant rearrangement of their code and algorithms in order to immediately gain practically useful simulations of non-Darcy flows or to explore their applicability, which is still an issue to resolve.\u0000 The novelty of the considered approaches is in ability to embed non-Darcy flow models into present reservoir simulation software keeping most of existing algorithms and data structures implemented. Taking into account that the algorithms are based on a generalized form of non-Darcy flows, it is possible to calculate a wide range of models preserving computational complexity.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86334572","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 paper provides a technical overview of an operator's Real-Time Drilling (RTD) ecosystem currently developed and deployed to all US Onshore and Deepwater Gulf of Mexico rigs. It also shares best practices with the industry through the journey of building the RTD solution: first designing and building the initial analytics system, then addressing significant challenges the system faces (these challenges should be common in drilling industry, especially for operators), next enhancing the system from lessons learned, and lastly, finalizing a fully integrated and functional ecosystem to provide a one-stop solution to end users. The RTD ecosystem consists of four subsystems as shown in architecture Figure 1. (I) The StreamBase RTD streaming system, which is the backbone of the ecosystem. It takes the real-time streaming log data as well as other contextual well data (for example, OpenWells), processes it through analytical models, generates results, and delivers them to the web-based user interface; (II) The analytics models, which include the Machine Learning (ML)/Deep Learning (DL) models, the physics-based models and the stream analytical/statistical models; (III) The digital transformation solution, which wasdesigned to address contextual well data digitization issues to enable real-time physics-based modeling. Contextual well data like bottom hole assemblies (BHAs) and casing programs are challenging to aggregate and deliver to models, as this data is often stored in locations across multiple systems and in various formats. The digital transformation applications are designed to fit into the drilling teams' workflows and collect this information during the course of normal engineering processes, enhancing both the engineering workflow and the data collection process; (IV) the cloud based ML pipeline, which streamlines the original ML workflows, as well as establishes an anomaly detection and re-training mechanism for ML models in production. Figure 1 RTD ecosystem architecture All of these subsystems are fully integrated and interact with each other to function as one system, providing a one-stop solution for real-time drilling optimization and monitoring. This RTD ecosystem has become a powerful decision support tool for the drilling operations team. While it was a significant effort, the long term operational and engineering benefits to operators designing such a real-time drilling analytics ecosystem far outweighs the cost and provides a solid foundation to continue pushing the historical limitations of drilling workflow and operational efficiency during this period of rapid digital transformation in the industry.
{"title":"Developing an Integrated Real-Time Drilling Ecosystem to Provide a One-Stop Solution for Drilling Monitoring and Optimization","authors":"Dingzhou Cao, Y. Ben, Chris James, Kate Ruddy","doi":"10.2118/196228-ms","DOIUrl":"https://doi.org/10.2118/196228-ms","url":null,"abstract":"\u0000 The paper provides a technical overview of an operator's Real-Time Drilling (RTD) ecosystem currently developed and deployed to all US Onshore and Deepwater Gulf of Mexico rigs. It also shares best practices with the industry through the journey of building the RTD solution: first designing and building the initial analytics system, then addressing significant challenges the system faces (these challenges should be common in drilling industry, especially for operators), next enhancing the system from lessons learned, and lastly, finalizing a fully integrated and functional ecosystem to provide a one-stop solution to end users.\u0000 The RTD ecosystem consists of four subsystems as shown in architecture Figure 1. (I) The StreamBase RTD streaming system, which is the backbone of the ecosystem. It takes the real-time streaming log data as well as other contextual well data (for example, OpenWells), processes it through analytical models, generates results, and delivers them to the web-based user interface; (II) The analytics models, which include the Machine Learning (ML)/Deep Learning (DL) models, the physics-based models and the stream analytical/statistical models; (III) The digital transformation solution, which wasdesigned to address contextual well data digitization issues to enable real-time physics-based modeling. Contextual well data like bottom hole assemblies (BHAs) and casing programs are challenging to aggregate and deliver to models, as this data is often stored in locations across multiple systems and in various formats. The digital transformation applications are designed to fit into the drilling teams' workflows and collect this information during the course of normal engineering processes, enhancing both the engineering workflow and the data collection process; (IV) the cloud based ML pipeline, which streamlines the original ML workflows, as well as establishes an anomaly detection and re-training mechanism for ML models in production.\u0000 Figure 1 RTD ecosystem architecture\u0000 All of these subsystems are fully integrated and interact with each other to function as one system, providing a one-stop solution for real-time drilling optimization and monitoring. This RTD ecosystem has become a powerful decision support tool for the drilling operations team. While it was a significant effort, the long term operational and engineering benefits to operators designing such a real-time drilling analytics ecosystem far outweighs the cost and provides a solid foundation to continue pushing the historical limitations of drilling workflow and operational efficiency during this period of rapid digital transformation in the industry.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86388467","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}
Yan Li, K. Zaki, Yunhui Tan, Ruiting Wu, Peggy Rijken
PI (Productivity Index) degradation is a common issue in many oil fields. To obtain a highly reliable production forecast, it is critical to include well and completion performance in the analysis. A new workflow is developed to assess and incorporate the damage mechanisms at the wellbore, fracture and reservoir into production forecasting. Currently, most reservoir models use a skin factor to represent the combined well damages mechanisms. The skin factor is adjusted based on the user's experience or data analysis instead of physical modeling. In this workflow, a detailed model is built to explicitly simulate the damage mechanisms, assess the dynamic performance of the well and completion with depletion, and generate a physics-based proxy function for reservoir modeling. The new workflow closes the modeling gap in production forecasting and provides insights into which damage mechanisms impact PI degradation. In the workflow, a detailed model is built, which includes an explicit wellbore, an explicit fracture and the reservoir. Subsurface rock and flow damage mechanisms are represented explicitly in the model. Running the model with an optimization tool, the damage mechanisms’ impact on productivity can be assessed separately or in a combination. A physics-based proxy is generated linking the change in productivity to typical well parameters such as cumulative production, drainage region depletion and drawdown. This proxy is then incorporated into a standard reservoir simulator through the utilization of scripts linking the PI evolution of the well to the typical well parameters stated above. The workflow increases the reliability of generated production forecasts by incorporating the best representation of the near wellbore flow patterns. By varying the damage mechanism inputs the workflow is capable of history matching and forecasting the observed field behavior. The workflow has been validated for a high permeability, over pressured deep-water reservoir. The history match, PI prediction and damage mechanism analysis are presented in this paper. The new workflow can help assets to: (1) history match and forecast well performance under varying operating conditions; (2) identify the key damage mechanisms which allows for potential mitigation and remediation solutions and; (3) set operational limits that reduce the likelihood of future PI degradation and maintain current performance.
{"title":"Productivity Decline: Improved Production Forecasting Through Accurate Representation of Well Damage","authors":"Yan Li, K. Zaki, Yunhui Tan, Ruiting Wu, Peggy Rijken","doi":"10.2118/196213-ms","DOIUrl":"https://doi.org/10.2118/196213-ms","url":null,"abstract":"\u0000 PI (Productivity Index) degradation is a common issue in many oil fields. To obtain a highly reliable production forecast, it is critical to include well and completion performance in the analysis. A new workflow is developed to assess and incorporate the damage mechanisms at the wellbore, fracture and reservoir into production forecasting. Currently, most reservoir models use a skin factor to represent the combined well damages mechanisms. The skin factor is adjusted based on the user's experience or data analysis instead of physical modeling. In this workflow, a detailed model is built to explicitly simulate the damage mechanisms, assess the dynamic performance of the well and completion with depletion, and generate a physics-based proxy function for reservoir modeling. The new workflow closes the modeling gap in production forecasting and provides insights into which damage mechanisms impact PI degradation.\u0000 In the workflow, a detailed model is built, which includes an explicit wellbore, an explicit fracture and the reservoir. Subsurface rock and flow damage mechanisms are represented explicitly in the model. Running the model with an optimization tool, the damage mechanisms’ impact on productivity can be assessed separately or in a combination. A physics-based proxy is generated linking the change in productivity to typical well parameters such as cumulative production, drainage region depletion and drawdown. This proxy is then incorporated into a standard reservoir simulator through the utilization of scripts linking the PI evolution of the well to the typical well parameters stated above. The workflow increases the reliability of generated production forecasts by incorporating the best representation of the near wellbore flow patterns.\u0000 By varying the damage mechanism inputs the workflow is capable of history matching and forecasting the observed field behavior. The workflow has been validated for a high permeability, over pressured deep-water reservoir. The history match, PI prediction and damage mechanism analysis are presented in this paper. The new workflow can help assets to: (1) history match and forecast well performance under varying operating conditions; (2) identify the key damage mechanisms which allows for potential mitigation and remediation solutions and; (3) set operational limits that reduce the likelihood of future PI degradation and maintain current performance.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91297897","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}
Permanent Reservoir Monitoring (PRM) in systems deep-water settings provide on-demand snapshots for hydrocarbon reservoirs at different times during their production history. Delays in the interpretation turn-around of 4D seismic data reduce some benefits of the PRM. These delays could adversely impact the decision making processes despite obtaining information on demand. Using fast-track approaches in 4D seismic interpretation can provide timely information for reservoir management. This work focuses on a fast-track 4D seismic qualitative interpretation in PRM environment, with the aim of choosing the best seismic amplitude attribute (4D) to use. Different seismic attributes are extracted and the one with high signal-to-noise ratio is selected to carry out the 4D qualitative interpretation. All 4D signals are juxtaposed with well production history data to increase confidence in our interpretation. The selected attribute can be interpreted and used for the foreseeable life of field. This workflow has been developed and applied on post-salt Brazilian offshore field to choose the best seismic attribute to conduct the 4D seismic qualitative interpretation.
{"title":"Fast-Track Qualitative Interpretation of Seismic Data in a Permanent Reservoir Monitoring PRM Setting for a Brazilian Field","authors":"M. Maleki, S. Danaei, A. Davolio, D. Schiozer","doi":"10.2118/196185-ms","DOIUrl":"https://doi.org/10.2118/196185-ms","url":null,"abstract":"Permanent Reservoir Monitoring (PRM) in systems deep-water settings provide on-demand snapshots for hydrocarbon reservoirs at different times during their production history. Delays in the interpretation turn-around of 4D seismic data reduce some benefits of the PRM. These delays could adversely impact the decision making processes despite obtaining information on demand. Using fast-track approaches in 4D seismic interpretation can provide timely information for reservoir management. This work focuses on a fast-track 4D seismic qualitative interpretation in PRM environment, with the aim of choosing the best seismic amplitude attribute (4D) to use. Different seismic attributes are extracted and the one with high signal-to-noise ratio is selected to carry out the 4D qualitative interpretation. All 4D signals are juxtaposed with well production history data to increase confidence in our interpretation. The selected attribute can be interpreted and used for the foreseeable life of field. This workflow has been developed and applied on post-salt Brazilian offshore field to choose the best seismic attribute to conduct the 4D seismic qualitative interpretation.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"36 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90448462","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}
Progressive Cavity Pumps (PCPs) are the predominant form of artificial lift method deployed by Australian operators in Coal Seam Gas (CSG) wells. With over five thousand CSG wells [1] operating in Queensland's Bowen and Surat Basins, managing and maintaining PCP supported production becomes a significant challenge for operators. Especially when these pumps face regular failures due to the production of coal fines. It is possible to gauge the holistic production performance of PCPs with the aid of real-time data, as this allows for pro-active and informed management of artificially lifted CSG wells. Based on data obtained from two (2) CSG operators, this paper will discuss in detail how features extracted from time series data can be converted to images, which can then aid in autonomously detecting abnormal PCP behavior.
{"title":"Converting Time Series Data into Images: An Innovative Approach to Detect Abnormal Behavior of Progressive Cavity Pumps Deployed in Coal Seam Gas Wells","authors":"Fahd Saghir, M. G. Perdomo, P. Behrenbruch","doi":"10.2118/195905-ms","DOIUrl":"https://doi.org/10.2118/195905-ms","url":null,"abstract":"\u0000 Progressive Cavity Pumps (PCPs) are the predominant form of artificial lift method deployed by Australian operators in Coal Seam Gas (CSG) wells. With over five thousand CSG wells [1] operating in Queensland's Bowen and Surat Basins, managing and maintaining PCP supported production becomes a significant challenge for operators. Especially when these pumps face regular failures due to the production of coal fines.\u0000 It is possible to gauge the holistic production performance of PCPs with the aid of real-time data, as this allows for pro-active and informed management of artificially lifted CSG wells. Based on data obtained from two (2) CSG operators, this paper will discuss in detail how features extracted from time series data can be converted to images, which can then aid in autonomously detecting abnormal PCP behavior.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90829838","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}
Accurate predictions of connectivity and heterogeneity pose important technical challenges for successful maturation of conventional and unconventional reservoirs. We present the success of a new reservoir management workflow that uses both artificial intelligence and classic models to define the impact of stratigraphic connectivity and heterogeneity on horizontal-well production performance in a mature heavy oil field. The data-driven model based on fuzzy logic was used to compute a new attribute named dynamic Reservoir Quality Index (dRQI). The classical models used the stratigraphic Lorenz Plots, Reservoir Quality Index (RQI) and Flow-Zone indicator (FZI). Workflows were validated through a lookback process on more than 400 wells used to predict the fine-scale stratigraphic and directional heterogeneities within intervals targeted by horizontal wells, and production performance. The workflow was successfully used to optimize the horizontal well placement for 2019-2020 drilling programs.
{"title":"Optimizing Horizontal Well Placement Through Stratigraphic Performance Prediction Using Artificial Intelligence","authors":"A. Popa, S. Connel","doi":"10.2118/195887-ms","DOIUrl":"https://doi.org/10.2118/195887-ms","url":null,"abstract":"\u0000 Accurate predictions of connectivity and heterogeneity pose important technical challenges for successful maturation of conventional and unconventional reservoirs. We present the success of a new reservoir management workflow that uses both artificial intelligence and classic models to define the impact of stratigraphic connectivity and heterogeneity on horizontal-well production performance in a mature heavy oil field. The data-driven model based on fuzzy logic was used to compute a new attribute named dynamic Reservoir Quality Index (dRQI). The classical models used the stratigraphic Lorenz Plots, Reservoir Quality Index (RQI) and Flow-Zone indicator (FZI). Workflows were validated through a lookback process on more than 400 wells used to predict the fine-scale stratigraphic and directional heterogeneities within intervals targeted by horizontal wells, and production performance. The workflow was successfully used to optimize the horizontal well placement for 2019-2020 drilling programs.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"381 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80671181","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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