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}
M. Carlsen, C. H. Whitson, A. Alavian, S. Martinsen, S. Mydland, Kameshwar Singh, Bilal Younus, Ilina Yusra
In this paper we emphasize the duality of fluid sampling: (1) fluid characterization; to collect samples and measure pressure/volume/temperature (PVT) data that can be used to build and tune an equation of state (EOS) model, and (2) fluid initialization; to collect samples to estimate in-situ fluid compositions. It is hard, if not impossible, to obtain truly in-situ representative fluid samples in multi-fractured horizontal wells (MFHW). This paper explains why fluids measured in the lab may be significantly different from in-situ representative fluid samples, even if the fluid samples are collected shortly after the well is put online. The paper also suggests that practically all samples, in-situ representative or not, can and should be used to build a reliable EOS model. To make a comprehensive assessment of fluid sampling in tight unconventionals, reservoir fluids ranging from black oils to gas condensates have been studied. For a wide range of fluid systems, a compositional reservoir simulator has been used to assess two main scenarios: (1) an initially undersaturated (single-phase) fluid system, and (2) initially saturated (two-phase) fluid system. To quantify how collected surface samples change with time, three properties are studied as functions of time: (1) saturation pressure and type (dewpoint | bubblepoint), (2) producing gas/oil ratio (GOR), and (3) stock-tank oil (STO) API. Observations of how these three properties change with time is used to help explain why elevated saturation pressures, greater than the initial reservoir pressure, often can be observed. Rapid decline of the flowing bottomhole pressure (BHP | pwf), together with shut-in periods, makes it difficult to obtain in-situ representative samples in MFHW. For slightly undersaturated reservoirs, and saturated reservoirs, it may be impossible to obtain in-situ representative fluid samples because of the near-wellbore multiphase behavior. However, samples which are not in-situ representative can still be used to estimate original in-situ fluids using equilibrium contact mixing (ECM) procedures. In this paper, we propose two ECM methods that can either be carried out by physical measurements in a PVT lab or can be computed with a properly tuned EOS model.
{"title":"Fluid Sampling in Tight Unconventionals","authors":"M. Carlsen, C. H. Whitson, A. Alavian, S. Martinsen, S. Mydland, Kameshwar Singh, Bilal Younus, Ilina Yusra","doi":"10.2118/196056-ms","DOIUrl":"https://doi.org/10.2118/196056-ms","url":null,"abstract":"\u0000 In this paper we emphasize the duality of fluid sampling: (1) fluid characterization; to collect samples and measure pressure/volume/temperature (PVT) data that can be used to build and tune an equation of state (EOS) model, and (2) fluid initialization; to collect samples to estimate in-situ fluid compositions. It is hard, if not impossible, to obtain truly in-situ representative fluid samples in multi-fractured horizontal wells (MFHW). This paper explains why fluids measured in the lab may be significantly different from in-situ representative fluid samples, even if the fluid samples are collected shortly after the well is put online. The paper also suggests that practically all samples, in-situ representative or not, can and should be used to build a reliable EOS model.\u0000 To make a comprehensive assessment of fluid sampling in tight unconventionals, reservoir fluids ranging from black oils to gas condensates have been studied. For a wide range of fluid systems, a compositional reservoir simulator has been used to assess two main scenarios: (1) an initially undersaturated (single-phase) fluid system, and (2) initially saturated (two-phase) fluid system. To quantify how collected surface samples change with time, three properties are studied as functions of time: (1) saturation pressure and type (dewpoint | bubblepoint), (2) producing gas/oil ratio (GOR), and (3) stock-tank oil (STO) API. Observations of how these three properties change with time is used to help explain why elevated saturation pressures, greater than the initial reservoir pressure, often can be observed.\u0000 Rapid decline of the flowing bottomhole pressure (BHP | pwf), together with shut-in periods, makes it difficult to obtain in-situ representative samples in MFHW. For slightly undersaturated reservoirs, and saturated reservoirs, it may be impossible to obtain in-situ representative fluid samples because of the near-wellbore multiphase behavior. However, samples which are not in-situ representative can still be used to estimate original in-situ fluids using equilibrium contact mixing (ECM) procedures. In this paper, we propose two ECM methods that can either be carried out by physical measurements in a PVT lab or can be computed with a properly tuned EOS model.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77752204","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}
Xu Zhou, M. Tyagi, Guoyin Zhang, Hao Yu, Yangkang Chen
With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations. 3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey. In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.
{"title":"Data Driven Modeling and Prediction for Reservoir Characterization Using Seismic Attribute Analyses and Big Data Analytics","authors":"Xu Zhou, M. Tyagi, Guoyin Zhang, Hao Yu, Yangkang Chen","doi":"10.2118/195856-ms","DOIUrl":"https://doi.org/10.2118/195856-ms","url":null,"abstract":"With recent developments in data acquisition and storage techniques, there exists huge amount of available data for data-driven decision making in the Oil & Gas industry. This study explores an application of using Big Data Analytics to establish the statistical relationships between seismic attribute values from a 3D seismic survey and petrophysical properties from well logs. Such relationships and models can be further used for the optimization of exploration and production operations.\u0000 3D seismic data can be used to extract various seismic attribute values at all locations within the seismic survey. Well logs provide accurate constraints on the petrophysical values along the wellbore. Big Data Analytics methods are utilized to establish the statistical relationships between seismic attributes and petrophysical data. Since seismic data are at the reservoir scale and are available at every sample cell of the seismic survey, these relationships can be used to estimate the petrophysical properties at all locations inside the seismic survey.\u0000 In this study, the Teapot dome 3D seismic survey is selected to extract seismic attribute values. A set of instantaneous seismic attributes, including curvature, instantaneous phase, and trace envelope, are extracted from the 3D seismic volume. Deep Learning Neural Network models are created to establish the relationships between the input seismic attribute values from the seismic survey and petrophysical properties from well logs. Results show that a Deep Learning Neural Network model with multi-hidden layers is capable of predicting porosity values using extracted seismic attribute values from 3D seismic volumes. Ultilization of a subset of seismic attributes improves the model performance in predicting porosity values from seismic data.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"107 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80902161","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}
This paper discusses career development essentials for young E&P technical professionals to realize and use for career planning. By dividing the professional life of the E&P professional into the early-career, mid-career and late-career stages, each spanning about twelve years, the author discusses career development essentials and their benefits in each stage. In the early-career stage, essentials include understanding the corporate culture, developing technical depth and breadth and developing good interpersonal team skills. In the mid-career stage, essentials include developing leadership skills, moving out of one's comfort zone, mastering cross discipline competency and developing a strong professional network. In the late-career stage, essential include anticipating future trends, leveraging one's strength and experience, developing others and leaving a legacy.
{"title":"Career Development Essentials for Young E&P Technical Professionals","authors":"H. Lau","doi":"10.2118/196027-ms","DOIUrl":"https://doi.org/10.2118/196027-ms","url":null,"abstract":"\u0000 This paper discusses career development essentials for young E&P technical professionals to realize and use for career planning. By dividing the professional life of the E&P professional into the early-career, mid-career and late-career stages, each spanning about twelve years, the author discusses career development essentials and their benefits in each stage. In the early-career stage, essentials include understanding the corporate culture, developing technical depth and breadth and developing good interpersonal team skills. In the mid-career stage, essentials include developing leadership skills, moving out of one's comfort zone, mastering cross discipline competency and developing a strong professional network. In the late-career stage, essential include anticipating future trends, leveraging one's strength and experience, developing others and leaving a legacy.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"110 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77585542","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
E. Stueland, Alf M. Øverland, M. Persaud, D. D. Leonardis, F. Sanfilippo, F. J. Santarelli
Reservoirs in the Barents Sea are several times shallower than in other parts of the NCS, essentially due to recent uplift and erosion of younger sediments. A proper understanding of their geomechanics is considered paramount for their successful development. In turn, the lack of any available analogue makes the proper in situ measurement of key parameters compulsory. The paper describes the planning and execution of an appraisal well solely dedicated to the purpose of geomechanics data acquisition in the shallowest oil reservoir on the NCS – i.e. coring, logging, XLOT and injection testing. It focuses on the operations conducted in the oil reservoir itself, which included an entirely novel multi-cycle injection test aimed at estimating the large-scale thermal stress coefficient of the formations around the well – i.e. the impact of the injection temperature on the fracture pressure of the formations. Every operation in the well was challenging due to the sea depth being about twice that of the overburden thickness and to the formations being quite consolidated, which was met by careful iterative multidisciplinary-planning. The equipment was often taken to its limit and sometimes extended beyond its standard use – e.g. the metering systems. The injection test itself could not be performed traditionally – i.e. use of surface data and downhole memory gauge. Instead, the downhole gauge data were sampled, pumped out and transferred to a remote site where real time advanced analytics was used to ensure that safety criteria were always met throughout the operation in terms of vertical fracture propagation and lack of reservoir compartmentalisation. In addition, this allowed adjusting the planned injection schedule to the exact formation's response, which could not be fully quantified ahead of time. All the targets of the appraisal well were met. The injection test – i.e. the shallowest on the NCS and perhaps worldwide in an offshore environment – was performed successfully. Its main results are considered essential for a possible future field development – e.g. the injectivity is confirmed and, in addition, a significant thermal effect is proven. The series of novel technologies deployed in the extreme environment presented in the paper can easily and beneficially be extended to more traditional reservoirs. This concerns performing multi-cycle injection tests on appraisal wells on a systematic basis to prepare and optimise the development plan, real-time monitoring through advanced analytics and adjustment of these tests, start-up of injection wells during field development, monitoring and optimisation of water injection schemes, etc.
{"title":"Advanced Real-Time Analytics Allow Performing the Shallowest Injection Test Ever on the Norwegian Continental Shelf NCS – Rational, Planning, Execution and Results","authors":"E. Stueland, Alf M. Øverland, M. Persaud, D. D. Leonardis, F. Sanfilippo, F. J. Santarelli","doi":"10.2118/196111-ms","DOIUrl":"https://doi.org/10.2118/196111-ms","url":null,"abstract":"\u0000 Reservoirs in the Barents Sea are several times shallower than in other parts of the NCS, essentially due to recent uplift and erosion of younger sediments. A proper understanding of their geomechanics is considered paramount for their successful development. In turn, the lack of any available analogue makes the proper in situ measurement of key parameters compulsory.\u0000 The paper describes the planning and execution of an appraisal well solely dedicated to the purpose of geomechanics data acquisition in the shallowest oil reservoir on the NCS – i.e. coring, logging, XLOT and injection testing. It focuses on the operations conducted in the oil reservoir itself, which included an entirely novel multi-cycle injection test aimed at estimating the large-scale thermal stress coefficient of the formations around the well – i.e. the impact of the injection temperature on the fracture pressure of the formations.\u0000 Every operation in the well was challenging due to the sea depth being about twice that of the overburden thickness and to the formations being quite consolidated, which was met by careful iterative multidisciplinary-planning. The equipment was often taken to its limit and sometimes extended beyond its standard use – e.g. the metering systems.\u0000 The injection test itself could not be performed traditionally – i.e. use of surface data and downhole memory gauge. Instead, the downhole gauge data were sampled, pumped out and transferred to a remote site where real time advanced analytics was used to ensure that safety criteria were always met throughout the operation in terms of vertical fracture propagation and lack of reservoir compartmentalisation. In addition, this allowed adjusting the planned injection schedule to the exact formation's response, which could not be fully quantified ahead of time.\u0000 All the targets of the appraisal well were met. The injection test – i.e. the shallowest on the NCS and perhaps worldwide in an offshore environment – was performed successfully. Its main results are considered essential for a possible future field development – e.g. the injectivity is confirmed and, in addition, a significant thermal effect is proven.\u0000 The series of novel technologies deployed in the extreme environment presented in the paper can easily and beneficially be extended to more traditional reservoirs. This concerns performing multi-cycle injection tests on appraisal wells on a systematic basis to prepare and optimise the development plan, real-time monitoring through advanced analytics and adjustment of these tests, start-up of injection wells during field development, monitoring and optimisation of water injection schemes, etc.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79066187","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. Bakay, J. Caers, T. Mukerji, P. Miller, Cheryl Cartier, A. Briceno
The focus of this paper is on Duvernay shale formation in Alberta, Canada. The objective is to provide, based on existing data of production, completion and geological parameters, an automated machine- learning approach to determine the spatial variation in decline type curves for gas production. This model will enable the prediction and uncertainty quantification of production profiles for new target wells or areas in the basin. The project is based on publicly available monthly production data from most of the producing wells of the Duvernay formation. We use k-means to cluster 273 wells, using geological parameters (thickness, porosity, etc.), completion parameters (horizontal section length, proppant volume, etc.), spatial location, fluid window, and production curves. Based on the clustering results, a machine learning classification is used to draw distinct geographic regions, within which the combination of geological, completion, and production factors is fairly similar. A support vector machine approach is used to create maps of clusters and quantify its uncertainty. In addition, functional classification and regression trees (CART) is used to indicate the most important/sensitive factors that should be used for clustering. The results show that the unsupervised method, k-means, performs equally as well as the supervised CART method. The methodology is flexible and allows for quick changes in the variables used in clustering; the transfer to another dataset or basin is straightforward.
{"title":"Machine Learning of Spatially Varying Decline Curves for the Duvernay Formation","authors":"A. Bakay, J. Caers, T. Mukerji, P. Miller, Cheryl Cartier, A. Briceno","doi":"10.2118/196110-ms","DOIUrl":"https://doi.org/10.2118/196110-ms","url":null,"abstract":"\u0000 The focus of this paper is on Duvernay shale formation in Alberta, Canada. The objective is to provide, based on existing data of production, completion and geological parameters, an automated machine- learning approach to determine the spatial variation in decline type curves for gas production. This model will enable the prediction and uncertainty quantification of production profiles for new target wells or areas in the basin.\u0000 The project is based on publicly available monthly production data from most of the producing wells of the Duvernay formation. We use k-means to cluster 273 wells, using geological parameters (thickness, porosity, etc.), completion parameters (horizontal section length, proppant volume, etc.), spatial location, fluid window, and production curves. Based on the clustering results, a machine learning classification is used to draw distinct geographic regions, within which the combination of geological, completion, and production factors is fairly similar. A support vector machine approach is used to create maps of clusters and quantify its uncertainty.\u0000 In addition, functional classification and regression trees (CART) is used to indicate the most important/sensitive factors that should be used for clustering.\u0000 The results show that the unsupervised method, k-means, performs equally as well as the supervised CART method. The methodology is flexible and allows for quick changes in the variables used in clustering; the transfer to another dataset or basin is straightforward.","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":"75139538","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Downhole fluid sampling is ubiquitous during exploration and appraisal because formation fluid properties have a strong impact on field development decisions. Efficient planning of sampling operations and interpretation of obtained data require a model-based approach. We present a framework for forward and inverse modeling of filtrate contamination cleanup during fluid sampling. The framework consists of a deep learning (DL) proxy forward model coupled with a Markov Chain Monte Carlo (MCMC) approach for the inverse model. The DL forward model is trained using precomputed numerical simulations of immiscible filtrate cleanup over a wide range of in situ conditions. The forward model consists of a multilayer neural network with both recurrent and linear layers, where inputs are defined by a combination of reservoir and fluid properties. A model training and selection process is presented, including network depth and layer size impact assessment. The inverse framework consists of an MCMC algorithm that stochastically explores the solution space using the likelihood of the observed data computed as the mismatch between the observations and the model predictions. The developed DL forward model achieved up to 50% increased accuracy compared with prior proxy models based on Gaussian process regression. Additionally, the new approach reduced the memory footprint by a factor of ten. The same model architecture and training process proved applicable to multiple sampling probe geometries without compromising performance. These attributes, combined with the speed of the model, enabled its use in real-time inversion applications. Furthermore, the DL forward model is amendable to incremental improvements if new training data becomes available. Flowline measurements acquired during cleanup and sampling hold valuable information about formation and fluid properties that may be uncovered through an inversion process. Using measurements of water cut and pressure, the MCMC inverse model achieved 93% less calls to the forward model compared to conventional gradient-based optimization along with comparable quality of history matches. Moreover, by obtaining estimates of the full posterior parameter distributions, the presented model enables more robust uncertainty quantification.
{"title":"Deep Learning and Bayesian Inversion for Planning and Interpretation of Downhole Fluid Sampling","authors":"Dante Orta Alemán, M. Kristensen, N. Chugunov","doi":"10.2118/195800-ms","DOIUrl":"https://doi.org/10.2118/195800-ms","url":null,"abstract":"\u0000 Downhole fluid sampling is ubiquitous during exploration and appraisal because formation fluid properties have a strong impact on field development decisions. Efficient planning of sampling operations and interpretation of obtained data require a model-based approach. We present a framework for forward and inverse modeling of filtrate contamination cleanup during fluid sampling. The framework consists of a deep learning (DL) proxy forward model coupled with a Markov Chain Monte Carlo (MCMC) approach for the inverse model.\u0000 The DL forward model is trained using precomputed numerical simulations of immiscible filtrate cleanup over a wide range of in situ conditions. The forward model consists of a multilayer neural network with both recurrent and linear layers, where inputs are defined by a combination of reservoir and fluid properties. A model training and selection process is presented, including network depth and layer size impact assessment. The inverse framework consists of an MCMC algorithm that stochastically explores the solution space using the likelihood of the observed data computed as the mismatch between the observations and the model predictions.\u0000 The developed DL forward model achieved up to 50% increased accuracy compared with prior proxy models based on Gaussian process regression. Additionally, the new approach reduced the memory footprint by a factor of ten. The same model architecture and training process proved applicable to multiple sampling probe geometries without compromising performance. These attributes, combined with the speed of the model, enabled its use in real-time inversion applications. Furthermore, the DL forward model is amendable to incremental improvements if new training data becomes available.\u0000 Flowline measurements acquired during cleanup and sampling hold valuable information about formation and fluid properties that may be uncovered through an inversion process. Using measurements of water cut and pressure, the MCMC inverse model achieved 93% less calls to the forward model compared to conventional gradient-based optimization along with comparable quality of history matches. Moreover, by obtaining estimates of the full posterior parameter distributions, the presented model enables more robust uncertainty quantification.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"10 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75347678","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}
Jinze Xu, Jin Wang, Hossein Aghabarati, A. Zamani, K. Cheung
Suncor's Firebag Project is one of the largest steam-assisted gravity drainage (SAGD) projects in the world. As a powerful tool for decision-making in the field, the Firebag SAGD reservoir simulation platform is based on an in-depth understanding of physics that controls thermal recovery process and meets the need for a practical solution. In this platform, standardized inputs and workflows are developed, and a good agreement with field data is achieved for all Firebag SAGD operating pads with production history. The Firebag SAGD reservoir simulation platform promotes the capacity to address existing Firebag SAGD challenges, capture unique Firebag reservoir features, and support reservoir management and future pad development.
{"title":"Development and Application of Firebag SAGD Reservoir Simulation Platform","authors":"Jinze Xu, Jin Wang, Hossein Aghabarati, A. Zamani, K. Cheung","doi":"10.2118/196233-ms","DOIUrl":"https://doi.org/10.2118/196233-ms","url":null,"abstract":"\u0000 Suncor's Firebag Project is one of the largest steam-assisted gravity drainage (SAGD) projects in the world. As a powerful tool for decision-making in the field, the Firebag SAGD reservoir simulation platform is based on an in-depth understanding of physics that controls thermal recovery process and meets the need for a practical solution. In this platform, standardized inputs and workflows are developed, and a good agreement with field data is achieved for all Firebag SAGD operating pads with production history. The Firebag SAGD reservoir simulation platform promotes the capacity to address existing Firebag SAGD challenges, capture unique Firebag reservoir features, and support reservoir management and future pad development.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75800601","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}
Zhihua Wang, Chaoliang Zhu, Yuhua Lou, Q. Cheng, Yang Liu, Xinyu Wang
Wax crystals can aggregate and precipitate when the oil temperature decreases to below the wax appearance temperature (WAT) of waxy crude oil, which has undesirable effects on the transportation of crude oil in pipelines. Thermodynamic models considering the molecular diffusion, shearing dispersion, and shear stripping as well as hydrodynamic models have been developed for predicting the wax deposition in crude oil pipelines. However, the aggregation behavior of wax crystals during crude oil production and transportation is not well understood. The microscopic rheological parameters have not been related to the bulk flow parameters in the shearing field, and the prediction of the wax deposition behavior under complex conditions is restricted by the vector characteristics of the shearing stress and flow rate. A set of microscopic experiments was performed in this study to obtain the basic information from images of wax crystals in shearing fields. A novel method of fractal dimensional analysis was introduced to elucidate the aggregation behavior of wax crystals in different shear flow fields. The fractal methodology for characterizing wax crystal aggregation was then developed, and a blanket algorithm was introduced to compute the fractal dimension of the aggregated wax crystals. The flow characteristics of waxy crude oil in a pipeline were correlated with the shearing stress work, and a wax deposition model focusing on shearing energy analysis was established. The results indicate that a quantitative interpretation of the wax crystal aggregation behavior can be realized using the fractal methodology. The aggregation behavior of the wax crystals is closely related to the temperature and shearing experienced by the waxy crude oil. The aggregation behavior will be intensified with decreasing temperature and shearing effect, and a wider fractal dimension distribution appears at lower temperatures when the same shear rate range is employed. The lower the fractal dimensions obtained at high temperature and strong shear action, the weaker will be the nonlinear characteristics of the wax crystal aggregation structure, and thus, the potential wax deposition will be inhibited during waxy crude oil production and transportation. Furthermore, the improved model provides a method for discussing the effects of the operating conditions on wax deposition. The average relative deviation between the improved model prediction results and experimental results from the literature is 3.01%–5.32%. The fractal methodology developed in this study and the improvement in wax deposition modeling are beneficial for understanding and optimizing flow assurance operations in the pipeline transportation of waxy crude oils, and the results are expected to facilitate a better understanding of the wax crystallization and deposition mechanism.
{"title":"Method for Characterizing the Aggregation of Wax Crystals and Improving the Wax Deposition Model","authors":"Zhihua Wang, Chaoliang Zhu, Yuhua Lou, Q. Cheng, Yang Liu, Xinyu Wang","doi":"10.2118/195936-ms","DOIUrl":"https://doi.org/10.2118/195936-ms","url":null,"abstract":"\u0000 Wax crystals can aggregate and precipitate when the oil temperature decreases to below the wax appearance temperature (WAT) of waxy crude oil, which has undesirable effects on the transportation of crude oil in pipelines. Thermodynamic models considering the molecular diffusion, shearing dispersion, and shear stripping as well as hydrodynamic models have been developed for predicting the wax deposition in crude oil pipelines. However, the aggregation behavior of wax crystals during crude oil production and transportation is not well understood. The microscopic rheological parameters have not been related to the bulk flow parameters in the shearing field, and the prediction of the wax deposition behavior under complex conditions is restricted by the vector characteristics of the shearing stress and flow rate. A set of microscopic experiments was performed in this study to obtain the basic information from images of wax crystals in shearing fields. A novel method of fractal dimensional analysis was introduced to elucidate the aggregation behavior of wax crystals in different shear flow fields. The fractal methodology for characterizing wax crystal aggregation was then developed, and a blanket algorithm was introduced to compute the fractal dimension of the aggregated wax crystals. The flow characteristics of waxy crude oil in a pipeline were correlated with the shearing stress work, and a wax deposition model focusing on shearing energy analysis was established. The results indicate that a quantitative interpretation of the wax crystal aggregation behavior can be realized using the fractal methodology. The aggregation behavior of the wax crystals is closely related to the temperature and shearing experienced by the waxy crude oil. The aggregation behavior will be intensified with decreasing temperature and shearing effect, and a wider fractal dimension distribution appears at lower temperatures when the same shear rate range is employed. The lower the fractal dimensions obtained at high temperature and strong shear action, the weaker will be the nonlinear characteristics of the wax crystal aggregation structure, and thus, the potential wax deposition will be inhibited during waxy crude oil production and transportation. Furthermore, the improved model provides a method for discussing the effects of the operating conditions on wax deposition. The average relative deviation between the improved model prediction results and experimental results from the literature is 3.01%–5.32%. The fractal methodology developed in this study and the improvement in wax deposition modeling are beneficial for understanding and optimizing flow assurance operations in the pipeline transportation of waxy crude oils, and the results are expected to facilitate a better understanding of the wax crystallization and deposition mechanism.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73214095","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}
Surfactant-based EOR has thus far been demonstrated to be a potentially effective solution to improve the hydrocarbon recovery from Unconventional Oil Reservoirs (UORs). The most discussed functions of a surfactant are either Interfacial Tension (IFT) reduction or Wettability (WTA) Alteration. However, studies of the accountable effects for the enhanced production are inadequate because of the peculiar properties of shale matrix, such as the extremely low permeability and initial wetness. In addition, the current studies mainly focused on the spontaneous imbibition (SI) because of the long experimental period and limited pressure applicability with the existing experimental apparatus. This work is to study the process of shale oil EOR by adding surfactant additives with high confining pressures applied to an in-house designed set-up. The applied pressure was as high as 3000 psi and the surfactant was selected with a spectrum of IFT values. Two operational schemes were conducted: Forced Imbibition (FI) and Cyclic Injection (CI). For the forced imbibition study, constant pressure was applied to the experimental set-up throughout the whole experimental period. The final recovery was recorded at the end of each test. The cyclic injection is also referred to as ‘huff-n-puff’ technique. The pressure is applied and released with a periodic schedule and the recoveries were recorded after each cycle by volume. The results were compared with that of regular SI experiments. It is noticed that oil productions through the CI technique is mostly effective and efficient. In addition, WTB-alteration is the dominating mechanism in both pressurized and atmospheric pressure cases, while surprisingly, IFT-reduction could be detrimental for the recovery enhancement due to the low capillary pressure. The results gave indicative suggestions on the selection of surfactant and engineering application design for a surfactant based EOR project in shale oil reservoirs.
{"title":"Study of Surfactant-Based Shale Oil EOR Under High Confining Pressure Conditions","authors":"Jiawei Tu","doi":"10.2118/199774-stu","DOIUrl":"https://doi.org/10.2118/199774-stu","url":null,"abstract":"\u0000 Surfactant-based EOR has thus far been demonstrated to be a potentially effective solution to improve the hydrocarbon recovery from Unconventional Oil Reservoirs (UORs). The most discussed functions of a surfactant are either Interfacial Tension (IFT) reduction or Wettability (WTA) Alteration. However, studies of the accountable effects for the enhanced production are inadequate because of the peculiar properties of shale matrix, such as the extremely low permeability and initial wetness. In addition, the current studies mainly focused on the spontaneous imbibition (SI) because of the long experimental period and limited pressure applicability with the existing experimental apparatus.\u0000 This work is to study the process of shale oil EOR by adding surfactant additives with high confining pressures applied to an in-house designed set-up. The applied pressure was as high as 3000 psi and the surfactant was selected with a spectrum of IFT values. Two operational schemes were conducted: Forced Imbibition (FI) and Cyclic Injection (CI). For the forced imbibition study, constant pressure was applied to the experimental set-up throughout the whole experimental period. The final recovery was recorded at the end of each test. The cyclic injection is also referred to as ‘huff-n-puff’ technique. The pressure is applied and released with a periodic schedule and the recoveries were recorded after each cycle by volume.\u0000 The results were compared with that of regular SI experiments. It is noticed that oil productions through the CI technique is mostly effective and efficient. In addition, WTB-alteration is the dominating mechanism in both pressurized and atmospheric pressure cases, while surprisingly, IFT-reduction could be detrimental for the recovery enhancement due to the low capillary pressure. The results gave indicative suggestions on the selection of surfactant and engineering application design for a surfactant based EOR project in shale oil reservoirs.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"65 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73284998","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：