Substantial computational time is typically a bottleneck for coupled flow-geomechanics simulation in realistic problems despite increasing importance in reservoir geomechanics. This paper presents a new, rapid, coupled flow-geomechanics simulator using the Fast Marching Method (FMM-Geo). The simulator incorporates Diffusive Time-of-Flight (DTOF), which represents the arrival time of the propagating pressure front, as a 1-D spatial coordinate to transform original multi-dimensional model into equivalent 1-D model. DTOF can be obtained by efficiently solving the Eikonal equation using the Fast Marching Method (FMM). FMM-Geo is verified for 2-D models against a benchmark simulator and has achieved order-of-magnitude faster computation while it preserved reasonable accuracy. Finally, the simulator is applied to an assisted history matching example using surface subsidence data to illustrate its computational efficiency and applicability.
尽管油藏地质力学在实际问题中越来越重要，但大量的计算时间通常是流体-地质力学耦合模拟的瓶颈。本文提出了一种基于快速推进法(FMM-Geo)的新型、快速、耦合流动-地质力学模拟器。仿真器将表示传播压力锋到达时间的扩散飞行时间(diffusion time -of- flight, DTOF)作为一维空间坐标，将原来的多维模型转化为等效的一维模型。利用快速推进法(FMM)求解Eikonal方程，可以得到dof。FMM-Geo在二维模型上进行了基准模拟器验证，在保持合理精度的同时，计算速度提高了数量级。最后，以地面沉降数据辅助历史匹配为例，说明了该仿真器的计算效率和适用性。
{"title":"Rapid Coupled Flow and Geomechanics Simulation using the Fast Marching Method","authors":"K. Terada","doi":"10.2118/199785-stu","DOIUrl":"https://doi.org/10.2118/199785-stu","url":null,"abstract":"\u0000 Substantial computational time is typically a bottleneck for coupled flow-geomechanics simulation in realistic problems despite increasing importance in reservoir geomechanics. This paper presents a new, rapid, coupled flow-geomechanics simulator using the Fast Marching Method (FMM-Geo). The simulator incorporates Diffusive Time-of-Flight (DTOF), which represents the arrival time of the propagating pressure front, as a 1-D spatial coordinate to transform original multi-dimensional model into equivalent 1-D model. DTOF can be obtained by efficiently solving the Eikonal equation using the Fast Marching Method (FMM). FMM-Geo is verified for 2-D models against a benchmark simulator and has achieved order-of-magnitude faster computation while it preserved reasonable accuracy. Finally, the simulator is applied to an assisted history matching example using surface subsidence data to illustrate its computational efficiency and applicability.","PeriodicalId":10909,"journal":{"name":"Day 2 Tue, October 01, 2019","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2019-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73562136","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}
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}
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}
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}
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}
Ramin Dabirian, Mobina Mohammadikharkeshi, R. Mohan, O. Shoham
Sand transport in multiphase flow has recently gained keen attention of the oil and gas industry owing to the negative effects associated with it. These include partial pipe blockage, pipe corrosion, excessive pressure drop and production decline. To date, no comprehensive literature review and models evaluation have been published, which compare the experimental data collected for the prediction of the critical sand deposition velocity under intermittent flow with the related model predictions. This study can be used by engineers and researchers to determine the conditions under which the developed models perform the best. The intermittent flow critical sand deposition velocity data acquired by Najmi (2015) are presented in detail. Next, the effects of important parameters such as phase velocities, liquid viscosity as well as particle size and concentration on the critical velocity are investigated. The collected data are utilized to evaluate the performance of the models developed by Salama (1998), Hill (2011), Stevenson et al. (2001) and Danielson (2007), in order to determine the best model for the prediction of the sand critical velocity. The experimental data of Najmi (2015) indicate that higher critical velocities are required with increasing the liquid viscosity, particle size and particle concentration. However, the predictions of the models of Salama (1998), Stevenson et al. (2001) and Danielson (2007) demonstrate that these models do not take into account the effect of particle concentration. Depending on the liquid viscosity, Stevenson et al. (2001) model significantly over-predicts or under-predicts the critical velocity over different ranges of the phase velocities, while Salama (1998) model under-predicts the critical velocity under all experimental conditions. An overall comparison of the data with the published model predictions confirms that the Hill (2011) model has the best performance capturing the physical phenomena, including the effects of phase velocities, particle size, particle concentration and liquid viscosity.
{"title":"Critical Sand Deposition Velocity in Intermittent Flow – Models Evaluation","authors":"Ramin Dabirian, Mobina Mohammadikharkeshi, R. Mohan, O. Shoham","doi":"10.2118/196085-ms","DOIUrl":"https://doi.org/10.2118/196085-ms","url":null,"abstract":"\u0000 Sand transport in multiphase flow has recently gained keen attention of the oil and gas industry owing to the negative effects associated with it. These include partial pipe blockage, pipe corrosion, excessive pressure drop and production decline. To date, no comprehensive literature review and models evaluation have been published, which compare the experimental data collected for the prediction of the critical sand deposition velocity under intermittent flow with the related model predictions. This study can be used by engineers and researchers to determine the conditions under which the developed models perform the best.\u0000 The intermittent flow critical sand deposition velocity data acquired by Najmi (2015) are presented in detail. Next, the effects of important parameters such as phase velocities, liquid viscosity as well as particle size and concentration on the critical velocity are investigated. The collected data are utilized to evaluate the performance of the models developed by Salama (1998), Hill (2011), Stevenson et al. (2001) and Danielson (2007), in order to determine the best model for the prediction of the sand critical velocity.\u0000 The experimental data of Najmi (2015) indicate that higher critical velocities are required with increasing the liquid viscosity, particle size and particle concentration. However, the predictions of the models of Salama (1998), Stevenson et al. (2001) and Danielson (2007) demonstrate that these models do not take into account the effect of particle concentration. Depending on the liquid viscosity, Stevenson et al. (2001) model significantly over-predicts or under-predicts the critical velocity over different ranges of the phase velocities, while Salama (1998) model under-predicts the critical velocity under all experimental conditions. An overall comparison of the data with the published model predictions confirms that the Hill (2011) model has the best performance capturing the physical phenomena, including the effects of phase velocities, particle size, particle concentration and liquid viscosity.","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":"76551872","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}
U. Prasad, Ashabikash Roy Chowdhury, Mark Anderson
Operators face the continuing challenge to improve drilling efficiency for cost containment, especially in deepwater drilling environments where drilling costs are significantly higher. Innovative drilling technologies have been developed and implemented continuously to support the initiative. In many areas of the world, including the Gulf of Mexico (GOM), hydrocarbon reservoirs exist below thick non-porous and impermeable sequences of salt that are considered a perfect cap rock. However, salt poses varied levels of drilling challenges due to its unique mechanical properties. At ambient conditions, the unconfined compressive strength (UCS) of salt varies between 3,000 to 5,000 psi; however, the strain at failure for salt can be an order of magnitude higher when compared to other rocks. Consequently, during drilling salt's viscoelastic behavior requires that its must be broken with an inter-crystalline or trans-crystalline grain boundary breakage. When compared to other rock types, the unique isotropic nature of salt results in a level of strain that is much higher for the given elastic moduli. This strain level makes salt failure mechanics different from other rock types that are prevalent in the GOM. Hybrid bits combine roller-cone and polycrystalline diamond compact (PDC) cutting elements to perform a simultaneous on-bottom crushing / gouging and shearing action. Two divergent cutting mechanics pre-stresses the rock and apply high strain for deformation and displacement, resulting in highly efficient cutting mechanics. To meet the drilling objectives, different hybrid designs have been implemented to combine stability and aggressiveness for improved drilling efficiency. An operator, while drilling salt sections at record penetration rates, has successfully used this innovative process of rock failure utilizing the dual-cutting mechanics of hybrid bits. This has resulted in significant value additions for the operator. This paper analyzes field-drilling data from successful GOM wells and attempts to correlate salt failure mechanics and provide insight into dual-cutting mechanics and its correlation with salt failure. The paper also reviews the drilling mechanics of hybrid bits in salt and highlights importance of dual-cutting mechanics for achieving higher penetration rates in salt through improved drilling efficiency.
{"title":"Drilling Mechanics Analysis of Record Hybrid Drill Bit Runs in Gulf of Mexico Salt Formation and its Correlation with Rock-Mechanical Properties of Salt","authors":"U. Prasad, Ashabikash Roy Chowdhury, Mark Anderson","doi":"10.2118/195860-ms","DOIUrl":"https://doi.org/10.2118/195860-ms","url":null,"abstract":"\u0000 Operators face the continuing challenge to improve drilling efficiency for cost containment, especially in deepwater drilling environments where drilling costs are significantly higher. Innovative drilling technologies have been developed and implemented continuously to support the initiative. In many areas of the world, including the Gulf of Mexico (GOM), hydrocarbon reservoirs exist below thick non-porous and impermeable sequences of salt that are considered a perfect cap rock. However, salt poses varied levels of drilling challenges due to its unique mechanical properties.\u0000 At ambient conditions, the unconfined compressive strength (UCS) of salt varies between 3,000 to 5,000 psi; however, the strain at failure for salt can be an order of magnitude higher when compared to other rocks. Consequently, during drilling salt's viscoelastic behavior requires that its must be broken with an inter-crystalline or trans-crystalline grain boundary breakage. When compared to other rock types, the unique isotropic nature of salt results in a level of strain that is much higher for the given elastic moduli. This strain level makes salt failure mechanics different from other rock types that are prevalent in the GOM.\u0000 Hybrid bits combine roller-cone and polycrystalline diamond compact (PDC) cutting elements to perform a simultaneous on-bottom crushing / gouging and shearing action. Two divergent cutting mechanics pre-stresses the rock and apply high strain for deformation and displacement, resulting in highly efficient cutting mechanics. To meet the drilling objectives, different hybrid designs have been implemented to combine stability and aggressiveness for improved drilling efficiency. An operator, while drilling salt sections at record penetration rates, has successfully used this innovative process of rock failure utilizing the dual-cutting mechanics of hybrid bits. This has resulted in significant value additions for the operator.\u0000 This paper analyzes field-drilling data from successful GOM wells and attempts to correlate salt failure mechanics and provide insight into dual-cutting mechanics and its correlation with salt failure. The paper also reviews the drilling mechanics of hybrid bits in salt and highlights importance of dual-cutting mechanics for achieving higher penetration rates in salt through improved drilling efficiency.","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":"76285502","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}
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