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}
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}
M. Onur, M. Galvao, Davut Erdem Bircan, M. Carvalho, Abelardo Barreto
The objectives of this study are to (i) provide analytical transient coupled wellbore/reservoir model to interpret/analyze transient temperature drawdown/buildup data acquired at both the producing horizon (sandface) and a gauge depth above the producing horizon (wellbore) and (ii) delineate the information content of both transient sandface and wellbore temperature measurements. The analytical models consider flow of a slightly compressible, single-phase fluid in a homogeneous infinite-acting reservoir system and provide temperature-transient data for drawdown and buildup tests produced at constant rate at any gauge location along the wellbore including the sandface. The production in the wellbore is assumed to be from inside the production casing. The models account for Joule-Thomson (J-T), adiabatic fluid-expansion, conduction and convection effects as well as nearby wellbore damage effects. The well/reservoir system considered is a fully penetrating vertical well in a two-zone radial composite reservoir system. The inner zone may represent a damaged (skin) zone, and the outer (non-skin) zone represents an infinitely extended reservoir. The analytical solutions for the sandface transient temperatures are obtained by solving the decoupled isothermal (pressure) diffusivity and temperature differential equations for the inner and outer zones with the Boltzmann transformation, and the coupled wellbore differential equation is solved by Laplace transformation. The developed solution compares well with the results of a rigorous thermal numerical simulator and determines the information content of the sandface and wellbore temperature data including skin zone effects. The analytical models can be used as forward models for estimating the parameters of interest by nonlinear regression built on any gradient-based estimation method such as the maximum likelihood estimation (MLE).
{"title":"Analytical Models for Interpretation and Analysis of Transient Sandface and Wellbore Temperature Data","authors":"M. Onur, M. Galvao, Davut Erdem Bircan, M. Carvalho, Abelardo Barreto","doi":"10.2118/195991-ms","DOIUrl":"https://doi.org/10.2118/195991-ms","url":null,"abstract":"\u0000 The objectives of this study are to (i) provide analytical transient coupled wellbore/reservoir model to interpret/analyze transient temperature drawdown/buildup data acquired at both the producing horizon (sandface) and a gauge depth above the producing horizon (wellbore) and (ii) delineate the information content of both transient sandface and wellbore temperature measurements. The analytical models consider flow of a slightly compressible, single-phase fluid in a homogeneous infinite-acting reservoir system and provide temperature-transient data for drawdown and buildup tests produced at constant rate at any gauge location along the wellbore including the sandface. The production in the wellbore is assumed to be from inside the production casing. The models account for Joule-Thomson (J-T), adiabatic fluid-expansion, conduction and convection effects as well as nearby wellbore damage effects. The well/reservoir system considered is a fully penetrating vertical well in a two-zone radial composite reservoir system. The inner zone may represent a damaged (skin) zone, and the outer (non-skin) zone represents an infinitely extended reservoir. The analytical solutions for the sandface transient temperatures are obtained by solving the decoupled isothermal (pressure) diffusivity and temperature differential equations for the inner and outer zones with the Boltzmann transformation, and the coupled wellbore differential equation is solved by Laplace transformation. The developed solution compares well with the results of a rigorous thermal numerical simulator and determines the information content of the sandface and wellbore temperature data including skin zone effects. The analytical models can be used as forward models for estimating the parameters of interest by nonlinear regression built on any gradient-based estimation method such as the maximum likelihood estimation (MLE).","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88185370","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 quest for excellence and efficiency in academia and education is critical. Some of the best proven methods of assessing educational programs refer to the standards and approach developed by ABET (The Accreditation Board for Engineering and Technology). This institution which was initiated originally in the US is involved in the supervision and accreditation of more than 3750 educational programs spread internationally over 30 countries. The Bob L. Herd Department of Petroleum Engineering has been using these standards since its ABET accreditation 66 years ago. It is still striving for excellence, improvement and perfection since then. The goal is to produce the best petroleum engineers using the last Student's Outcome approach (SO) and assessment methods as developed by ABET. ABET approach uses various tools to assess educational programs, some of which are conventional such as: Examinations, quizzes, guided homework's, term projects etc. others are more involved. The data are translated into quantifiable parameters for analysis. In addition to all these tools, this study uses one more that has been proven to be valuable and added to the arsenal available at the Bob L. Herd DPE. It is the use of visualization labs. This consists of models built to simulate various forces, physical mechanisms and engineering concepts encountered in drilling, production and reservoir engineering. The present study is directed toward the use of petrophysical or scaled models to enhance the methods of education in petroleum engineering and more specifically in reservoir engineering. Scaled models are not new in the oil industry. They used to be the only way to study oil reservoirs and forecast their behavior until the advent of computers and numerical simulation in the 1970's. Several scaled models designed to simulate water-flooding processes for educational purpose are used by students to complete projects on reservoir development. Analysis of the results shows the following: After completion of the projects, students have a better understanding of the fundamental concepts and principles behind reservoir engineering, depletion mechanisms and other issues related to fluid displacement in porous media.The Metrix associated with the student outcome and assessment methods developed for ABET evaluation showed that a well-conceived visualization lab can be extremely effective in petroleum engineering education in general and more particularly in reservoir engineering.
在学术界和教育界追求卓越和效率是至关重要的。一些最好的经过验证的评估教育项目的方法参考了ABET(工程技术认证委员会)制定的标准和方法。这个最初在美国发起的机构参与了30多个国家的3750多个教育项目的监督和认证。Bob L. Herd石油工程系自66年前获得ABET认证以来一直使用这些标准。从那时起，它仍然在追求卓越，改进和完美。目标是使用ABET开发的最后一种学生结果方法(SO)和评估方法培养最优秀的石油工程师。ABET方法使用各种工具来评估教育项目，其中一些是传统的，如:考试、小测验、指导作业、学期项目等。数据被转换成可量化的参数进行分析。除了所有这些工具之外，本研究还使用了另一种已被证明是有价值的工具，并添加到Bob L. Herd DPE的可用库中。它是可视化实验室的使用。这包括模拟钻井、生产和油藏工程中遇到的各种力、物理机制和工程概念的模型。本研究的目的是利用岩石物理模型或比例模型来提高石油工程，特别是油藏工程的教学方法。比例模型在石油行业并不新鲜。在20世纪70年代计算机和数值模拟出现之前，它们曾经是研究油藏和预测其行为的唯一方法。几个为模拟水驱过程而设计的比例模型用于教学目的，学生们使用它们来完成油藏开发项目。结果分析表明:项目完成后，学生对储层工程的基本概念和原理、衰竭机制以及与多孔介质流体驱替有关的其他问题有了更好的理解。与ABET评估的学生成绩和评估方法相关的Metrix表明，一个精心设计的可视化实验室在石油工程教育中是非常有效的，尤其是在油藏工程教育中。
{"title":"Use of Visualization Labs to Enhance Petroleum Engineering Education","authors":"H. Menouar, L. Heinze, M. Watson, T. Gamadi","doi":"10.2118/195981-ms","DOIUrl":"https://doi.org/10.2118/195981-ms","url":null,"abstract":"\u0000 The quest for excellence and efficiency in academia and education is critical. Some of the best proven methods of assessing educational programs refer to the standards and approach developed by ABET (The Accreditation Board for Engineering and Technology). This institution which was initiated originally in the US is involved in the supervision and accreditation of more than 3750 educational programs spread internationally over 30 countries.\u0000 The Bob L. Herd Department of Petroleum Engineering has been using these standards since its ABET accreditation 66 years ago. It is still striving for excellence, improvement and perfection since then. The goal is to produce the best petroleum engineers using the last Student's Outcome approach (SO) and assessment methods as developed by ABET.\u0000 ABET approach uses various tools to assess educational programs, some of which are conventional such as: Examinations, quizzes, guided homework's, term projects etc. others are more involved. The data are translated into quantifiable parameters for analysis. In addition to all these tools, this study uses one more that has been proven to be valuable and added to the arsenal available at the Bob L. Herd DPE. It is the use of visualization labs. This consists of models built to simulate various forces, physical mechanisms and engineering concepts encountered in drilling, production and reservoir engineering. The present study is directed toward the use of petrophysical or scaled models to enhance the methods of education in petroleum engineering and more specifically in reservoir engineering. Scaled models are not new in the oil industry. They used to be the only way to study oil reservoirs and forecast their behavior until the advent of computers and numerical simulation in the 1970's.\u0000 Several scaled models designed to simulate water-flooding processes for educational purpose are used by students to complete projects on reservoir development. Analysis of the results shows the following: After completion of the projects, students have a better understanding of the fundamental concepts and principles behind reservoir engineering, depletion mechanisms and other issues related to fluid displacement in porous media.The Metrix associated with the student outcome and assessment methods developed for ABET evaluation showed that a well-conceived visualization lab can be extremely effective in petroleum engineering education in general and more particularly in reservoir engineering.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"11 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87693699","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 an overview of digital oilfield development experience gained by Nafta College [1] employing the complex PolyPlan asset simulator during a multi-year programme of PetroCup [2] interactive tournaments. During the last few years, professional multi-disciplinary teams of 8 to 10 people from petroleum organisations based in various countries carried out few-days exercises on production and development of synthetic assets. In total, more than 20 petroleum companies, 10 petroleum service companies and 10 academic and research institutions have taken part in this programme. PetroCup sessions had various team structures, digital reserves and regional economics to ensure realistic production conditions. Despite this variation, some statistical metrics highlight dominant trends in oilfield development strategies, including effective and ineffective ones. The results may attract interest from petroleum asset managers to assess the efficiency of corporate strategies and policies in field development planning and well and reservoir management, and eventually increase their performance. The provided statistics are useful for managers of petroleum companies to assess the range, perspectives and value of production-related services. The PetroCup statistics can also be used by training centres and universities as an indicator of upstream trends and for maintaining the right focus in petroleum engineering curricula.
{"title":"PetroCup Training and Skill Testing Facility in Field Development and Production Management","authors":"A. Aslanyan","doi":"10.2118/196095-ms","DOIUrl":"https://doi.org/10.2118/196095-ms","url":null,"abstract":"\u0000 The paper provides an overview of digital oilfield development experience gained by Nafta College [1] employing the complex PolyPlan asset simulator during a multi-year programme of PetroCup [2] interactive tournaments.\u0000 During the last few years, professional multi-disciplinary teams of 8 to 10 people from petroleum organisations based in various countries carried out few-days exercises on production and development of synthetic assets. In total, more than 20 petroleum companies, 10 petroleum service companies and 10 academic and research institutions have taken part in this programme.\u0000 PetroCup sessions had various team structures, digital reserves and regional economics to ensure realistic production conditions. Despite this variation, some statistical metrics highlight dominant trends in oilfield development strategies, including effective and ineffective ones. The results may attract interest from petroleum asset managers to assess the efficiency of corporate strategies and policies in field development planning and well and reservoir management, and eventually increase their performance.\u0000 The provided statistics are useful for managers of petroleum companies to assess the range, perspectives and value of production-related services. The PetroCup statistics can also be used by training centres and universities as an indicator of upstream trends and for maintaining the right focus in petroleum engineering curricula.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"211 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73064566","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}
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}
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}
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