Tao Xu, G. Lindsay, Wei Zheng, Q. Yan, K. Patron, Farhan Alimahomed, M. Panjaitan, R. Malpani
Since early 2016, commodity prices have been gradually increasing, and the Permian Basin has become the most active basin for unconventional horizontal well development. As the plays in the basin are developed, new infill wells are drilled near pre-existing wells (known as "parent wells"). The impact of pressure depletion caused by adjacent existing producers may have a larger role in the performance of these new infill wells. How the various well spacing impact with the degree of reservoir pressure depletion from parent well is more important than ever for operators to optimize the completion design. Through data analytics and comprehensive fracture/reservoir modeling this paper studies how changes in well spacing and proppant volume in the Spraberry, a main formation in the Permian Basin, will impact new infill well performance. The studies in this paper are focused on the Midland Basin. A public database was used to identify the number of parent and child wells in the Midland basin. Data analysis of production normalized by total proppant and lateral length shows that parent wells outperform infill, or child, wells. To further understand the relationship between parent and child wells, a reservoir dataset for the Spraberry formation was used to build a hydraulic fracture and reservoir simulation model for both the parent well and a two-well infill pad. After production history matching a P50 type well as the parent well, three periods of production depletion were modeled (6 months, 3 years and 5 years) to understand the timing impact on the infill well production. A geomechanical finite-element model (FEM) was then used to quantify the changes to the magnitude and azimuth of the in-situ stresses from the various reservoir depletion scenarios. A two-well infill pad was then simulated into the altered stress field next to the parent well at various spacings between the parent and child wells. A sensitivity was then performed with different stimulation job sizes to understand the volume impact on created complex fracture propagation and total system recovery. This study can help operators understand how well spacing, reservoir depletion, and completion job size impact the infill well performance so they can optimize their infill well completion strategy.
{"title":"Advanced Modeling of Production Induced Pressure Depletion and Well Spacing Impact on Infill Wells in Spraberry, Permian Basin","authors":"Tao Xu, G. Lindsay, Wei Zheng, Q. Yan, K. Patron, Farhan Alimahomed, M. Panjaitan, R. Malpani","doi":"10.2118/191696-MS","DOIUrl":"https://doi.org/10.2118/191696-MS","url":null,"abstract":"\u0000 Since early 2016, commodity prices have been gradually increasing, and the Permian Basin has become the most active basin for unconventional horizontal well development. As the plays in the basin are developed, new infill wells are drilled near pre-existing wells (known as \"parent wells\"). The impact of pressure depletion caused by adjacent existing producers may have a larger role in the performance of these new infill wells. How the various well spacing impact with the degree of reservoir pressure depletion from parent well is more important than ever for operators to optimize the completion design. Through data analytics and comprehensive fracture/reservoir modeling this paper studies how changes in well spacing and proppant volume in the Spraberry, a main formation in the Permian Basin, will impact new infill well performance. The studies in this paper are focused on the Midland Basin.\u0000 A public database was used to identify the number of parent and child wells in the Midland basin. Data analysis of production normalized by total proppant and lateral length shows that parent wells outperform infill, or child, wells. To further understand the relationship between parent and child wells, a reservoir dataset for the Spraberry formation was used to build a hydraulic fracture and reservoir simulation model for both the parent well and a two-well infill pad. After production history matching a P50 type well as the parent well, three periods of production depletion were modeled (6 months, 3 years and 5 years) to understand the timing impact on the infill well production. A geomechanical finite-element model (FEM) was then used to quantify the changes to the magnitude and azimuth of the in-situ stresses from the various reservoir depletion scenarios. A two-well infill pad was then simulated into the altered stress field next to the parent well at various spacings between the parent and child wells. A sensitivity was then performed with different stimulation job sizes to understand the volume impact on created complex fracture propagation and total system recovery.\u0000 This study can help operators understand how well spacing, reservoir depletion, and completion job size impact the infill well performance so they can optimize their infill well completion strategy.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"94 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74973798","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}
T. Hoeink, D. Cotrell, Elijah Odusina, Sachin Ghorpade
A paradigm shift in dealing with subsurface uncertainty in hydraulic fracturing treatments is introduced. The mathematically rigorous application of uncertainty and sensitivity analyses for a proposed stimulation of a lateral well within an unconventional reservoir in the Marcellus with limited formation data delivers the ability to identify the optimum treatment parameters and to quantify its probability of success. Selection of the optimum reservoir stimulation treatment is achieved by systematically investigating thousands of hydraulic fracture simulations over a large parameter space covering formation properties with inherent uncertainties (e.g., stress gradients, leak-off coefficients) and tunable treatment parameters (e.g. pumping rates, fluid and proppant properties, perforation spacing), and computing an objective function. Operators commonly select objectives based on technical (e.g., propped fracture length, fracture height containment), operational and investment considerations. Here, the average fracture conductivity at closure is selected as the primary technical objective to be maximized. A subsequent uncertainty analysis of the optimum treatment plan that expressly includes the limits of formation property knowledge quantifies the probability of success. Production forecasts of specific cases illustrate the range of possible outcomes. Results from more than 12,000 hydraulic stimulation simulations demonstrate a wide distribution of results in terms of average fracture conductivity. Surprisingly, only a small, isolated fraction (< 5%) of the design space returns clearly superior results compared to the majority of investigated scenarios. The optimum treatment designs in this study are associated with relatively low volumes of a gel treatment pumped at relatively high rates. Production simulations illustrate that the best 10% of cases significantly outperform production over the first two years by approximately 50%. Collectively, the approach presented here illustrates the application of uncertainty and sensitivity analyses on several thousand simulations that cover a large, realistic parameter space. Embracing uncertainty, this approach enables identification of the best treatment plan and quantification of the probability of success given limited formation data. In addition, this methodology offers input for risk assessment and return on investment decisions.
{"title":"Quantifying the Probability of Success of Stimulation Treatments When Information is Limited","authors":"T. Hoeink, D. Cotrell, Elijah Odusina, Sachin Ghorpade","doi":"10.2118/191753-MS","DOIUrl":"https://doi.org/10.2118/191753-MS","url":null,"abstract":"\u0000 A paradigm shift in dealing with subsurface uncertainty in hydraulic fracturing treatments is introduced. The mathematically rigorous application of uncertainty and sensitivity analyses for a proposed stimulation of a lateral well within an unconventional reservoir in the Marcellus with limited formation data delivers the ability to identify the optimum treatment parameters and to quantify its probability of success. Selection of the optimum reservoir stimulation treatment is achieved by systematically investigating thousands of hydraulic fracture simulations over a large parameter space covering formation properties with inherent uncertainties (e.g., stress gradients, leak-off coefficients) and tunable treatment parameters (e.g. pumping rates, fluid and proppant properties, perforation spacing), and computing an objective function. Operators commonly select objectives based on technical (e.g., propped fracture length, fracture height containment), operational and investment considerations. Here, the average fracture conductivity at closure is selected as the primary technical objective to be maximized. A subsequent uncertainty analysis of the optimum treatment plan that expressly includes the limits of formation property knowledge quantifies the probability of success. Production forecasts of specific cases illustrate the range of possible outcomes. Results from more than 12,000 hydraulic stimulation simulations demonstrate a wide distribution of results in terms of average fracture conductivity. Surprisingly, only a small, isolated fraction (< 5%) of the design space returns clearly superior results compared to the majority of investigated scenarios. The optimum treatment designs in this study are associated with relatively low volumes of a gel treatment pumped at relatively high rates. Production simulations illustrate that the best 10% of cases significantly outperform production over the first two years by approximately 50%. Collectively, the approach presented here illustrates the application of uncertainty and sensitivity analyses on several thousand simulations that cover a large, realistic parameter space. Embracing uncertainty, this approach enables identification of the best treatment plan and quantification of the probability of success given limited formation data. In addition, this methodology offers input for risk assessment and return on investment decisions.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"42 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85549365","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. T. Al-Murayri, Dawood S. Kamal, J. G. Garcia, N. Al-Tameemi, J. Driver, Richard Hernandez, R. Fortenberry, Christopher Britton
There are many oil reservoirs worldwide with substantial amount of H2S but otherwise very favorable conditions for polymer flooding such as low temperature, high permeability, and moderate to high oil viscosity. However, there is a legitimate concern about the chemical stability of polymers when there is dissolved oxygen in the injection water or injection facility and its high concentrations of H2S in the reservoir. Several synthetic polymers and biopolymers were selected for stability testing under a wide range of conditions. We focused on identifying the concentration limits for co-presence of H2S and oxygen for which the synthetic and biopolymers are stable for an extended period, using different, widely available brine compositions. Experiments were conducted with and without standard polymer protection packages to evaluate their effects on stability and degradation under sour conditions. Viscosity of polymer solutions with varying concentrations of H2S and oxygen were measured and compared with the oxygen free or H2S free solution viscosities for a period of 6 months. Several methods of safely introducing H2S to the polymer solution were investigated and compared. The laboratory results indicated that biopolymers were stable at all the concentrations of oxygen and H2S concentrations studied. Three synthetic polymers tested showed some degradation in the presence of oxygen and H2S but were stable when either species is absent. The results indicated that oxygen is the limiting reagent in the degradation reaction with partially hydrolyzed polyacrylamide (HPAM) polymers under normal reservoir conditions. We observed little-to-no difference in degradation between samples with 10 or 100 ppm H2S at 500 ppb oxygen concentration, so H2S is not the limiting reagent under these conditions. Additionally, HPAM exposed to 10 ppm H2S and intermediate levels of oxygen (~0.5 ppm) only partially degrades, while samples exposed to H2S and ambient oxygen completely degrade. We anticipate these results will be useful for operators evaluating the potential of polymer flooding in sour reservoirs to follow a stricter polymer preparation at the surface facility to minimize oxygen concenration.
{"title":"Stability of Biopolymer and Partially Hydrolyzed Polyacrylamide in Presence of H2S and Oxygen","authors":"M. T. Al-Murayri, Dawood S. Kamal, J. G. Garcia, N. Al-Tameemi, J. Driver, Richard Hernandez, R. Fortenberry, Christopher Britton","doi":"10.2118/191581-MS","DOIUrl":"https://doi.org/10.2118/191581-MS","url":null,"abstract":"\u0000 There are many oil reservoirs worldwide with substantial amount of H2S but otherwise very favorable conditions for polymer flooding such as low temperature, high permeability, and moderate to high oil viscosity. However, there is a legitimate concern about the chemical stability of polymers when there is dissolved oxygen in the injection water or injection facility and its high concentrations of H2S in the reservoir.\u0000 Several synthetic polymers and biopolymers were selected for stability testing under a wide range of conditions. We focused on identifying the concentration limits for co-presence of H2S and oxygen for which the synthetic and biopolymers are stable for an extended period, using different, widely available brine compositions. Experiments were conducted with and without standard polymer protection packages to evaluate their effects on stability and degradation under sour conditions. Viscosity of polymer solutions with varying concentrations of H2S and oxygen were measured and compared with the oxygen free or H2S free solution viscosities for a period of 6 months. Several methods of safely introducing H2S to the polymer solution were investigated and compared.\u0000 The laboratory results indicated that biopolymers were stable at all the concentrations of oxygen and H2S concentrations studied. Three synthetic polymers tested showed some degradation in the presence of oxygen and H2S but were stable when either species is absent. The results indicated that oxygen is the limiting reagent in the degradation reaction with partially hydrolyzed polyacrylamide (HPAM) polymers under normal reservoir conditions.\u0000 We observed little-to-no difference in degradation between samples with 10 or 100 ppm H2S at 500 ppb oxygen concentration, so H2S is not the limiting reagent under these conditions. Additionally, HPAM exposed to 10 ppm H2S and intermediate levels of oxygen (~0.5 ppm) only partially degrades, while samples exposed to H2S and ambient oxygen completely degrade. We anticipate these results will be useful for operators evaluating the potential of polymer flooding in sour reservoirs to follow a stricter polymer preparation at the surface facility to minimize oxygen concenration.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"43 4-7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77727982","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}
P. Seth, Ripudaman Manchanda, Ashish Kumar, M. Sharma
Pressure interference measurements in fractured horizontal wells have been used to characterize hydraulic fractures (Kampfer and Dawson, 2016; Roussel and Agrawal, 2017). Past work has modeled this interference using static reservoir gridblocks as a proxy for hydraulic fractures. In this paper, we show that to accurately interpret the pressure response observed in a fractured monitor well, one needs to explicitly model the fractures and their propagation as a compliant discontinuity in the reservoir. A fully-coupled 3-D geomechanical reservoir model which models fractures explicitly as open and compliant channels has been developed to simulate pressure interference between hydraulic fractures in a multi-well pad. Using this model, we simulate dynamic fracture propagation at the treatment well while monitoring pressure at the monitor well. The pressure response inside the monitor well fracture is calculated accurately by explicitly modeling the monitor well fracture as a compliant discontinuity in the reservoir rock. We study the impact of mechanical stress interference between the fractures. The model is then used to simulate and analyze the treatment pressure response observed in a pair of wells in the Permian Basin. Simulation results indicate that hydraulic fracture propagation towards the monitor well results in changes in stress on the monitor fracture. Closure and opening of the monitor fracture is manifested directly as an increase/decrease in pressure in the monitor well fracture. We show that this pressure change in the monitor well is observed primarily because of the elastic effect of mechanically squeezing the monitor fracture by the dynamically propagating hydraulic fracture (not by direct hydraulic communication). As such it is essential to model the compliance of the fractures as has been done in this study. This monitor well pressure response is systematically analyzed to estimate fracture geometry for field data obtained from a Permian Basin well pad. Our representation of the propagating hydraulic fracture and the monitoring well fracture as compliant discontinuities in the reservoir is for the first time shown to be essential to capture the pressure response observed in the field. Previous models have simplified the problem by representing the fracture as static reservoir grid-blocks, and such models are clearly inadequate. Our model captures the impact of a propagating hydraulic fracture on the pressure response observed in a fractured monitor well much more accurately. Such pressure interference analysis can provide operators with a semi-quantitative estimate of hydraulic fracture geometry, relatively inexpensively.
压裂水平井的压力干扰测量已被用于表征水力裂缝(Kampfer和Dawson, 2016;Roussel and Agrawal, 2017)。过去的工作使用静态油藏网格块作为水力裂缝的代理来模拟这种干扰。在本文中，我们表明，为了准确地解释裂缝监测井中观察到的压力响应，需要明确地将裂缝及其扩展建模为油藏中的柔顺不连续面。开发了一种完全耦合的三维地质力学储层模型，该模型将裂缝明确地建模为开放和弯曲的通道，以模拟多井区水力裂缝之间的压力干扰。利用该模型，我们在监测井压力的同时模拟了处理井的动态裂缝扩展。通过将监测井裂缝明确地建模为储层岩石中的柔顺不连续面，准确地计算了监测井裂缝内部的压力响应。我们研究了裂缝间机械应力干扰的影响。然后将该模型用于模拟和分析在二叠纪盆地的一对井中观察到的处理压力响应。模拟结果表明，水力裂缝向监测井方向扩展导致监测裂缝应力发生变化。监测裂缝的闭合和打开直接表现为监测井裂缝内压力的增加/减少。我们发现，监测井的压力变化主要是由于动态扩展的水力裂缝(而不是直接的水力通信)机械挤压监测裂缝的弹性效应造成的。因此，正如本研究所做的那样，对骨折的顺应性进行建模是至关重要的。系统分析了该监测井的压力响应，以估计二叠纪盆地井台现场数据的裂缝几何形状。我们将扩展的水力裂缝和监测井裂缝表示为油藏中的柔顺不连续面，这首次证明了对于捕获现场观察到的压力响应至关重要。以前的模型将裂缝表示为静态储集网格块，从而简化了问题，这种模型显然是不充分的。我们的模型更准确地捕捉了水力裂缝扩展对裂缝监测井中观察到的压力响应的影响。这种压力干扰分析可以为作业者提供水力裂缝几何形状的半定量估计，成本相对较低。
{"title":"Estimating Hydraulic Fracture Geometry by Analyzing the Pressure Interference Between Fractured Horizontal Wells","authors":"P. Seth, Ripudaman Manchanda, Ashish Kumar, M. Sharma","doi":"10.2118/191492-MS","DOIUrl":"https://doi.org/10.2118/191492-MS","url":null,"abstract":"\u0000 Pressure interference measurements in fractured horizontal wells have been used to characterize hydraulic fractures (Kampfer and Dawson, 2016; Roussel and Agrawal, 2017). Past work has modeled this interference using static reservoir gridblocks as a proxy for hydraulic fractures. In this paper, we show that to accurately interpret the pressure response observed in a fractured monitor well, one needs to explicitly model the fractures and their propagation as a compliant discontinuity in the reservoir.\u0000 A fully-coupled 3-D geomechanical reservoir model which models fractures explicitly as open and compliant channels has been developed to simulate pressure interference between hydraulic fractures in a multi-well pad. Using this model, we simulate dynamic fracture propagation at the treatment well while monitoring pressure at the monitor well. The pressure response inside the monitor well fracture is calculated accurately by explicitly modeling the monitor well fracture as a compliant discontinuity in the reservoir rock. We study the impact of mechanical stress interference between the fractures. The model is then used to simulate and analyze the treatment pressure response observed in a pair of wells in the Permian Basin.\u0000 Simulation results indicate that hydraulic fracture propagation towards the monitor well results in changes in stress on the monitor fracture. Closure and opening of the monitor fracture is manifested directly as an increase/decrease in pressure in the monitor well fracture. We show that this pressure change in the monitor well is observed primarily because of the elastic effect of mechanically squeezing the monitor fracture by the dynamically propagating hydraulic fracture (not by direct hydraulic communication). As such it is essential to model the compliance of the fractures as has been done in this study. This monitor well pressure response is systematically analyzed to estimate fracture geometry for field data obtained from a Permian Basin well pad.\u0000 Our representation of the propagating hydraulic fracture and the monitoring well fracture as compliant discontinuities in the reservoir is for the first time shown to be essential to capture the pressure response observed in the field. Previous models have simplified the problem by representing the fracture as static reservoir grid-blocks, and such models are clearly inadequate. Our model captures the impact of a propagating hydraulic fracture on the pressure response observed in a fractured monitor well much more accurately. Such pressure interference analysis can provide operators with a semi-quantitative estimate of hydraulic fracture geometry, relatively inexpensively.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77728379","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 will focus on well integrity standards and give practical examples on how they are applied for wells in the Norwegian Sea. Further the paper will review how the standards are used for testing and verifying barriers in a well, and how well integrity on subsea and TLP (tension leg platforms) benefit with the introduction of annulus B monitoring. Examples will present how barriers can be moved and testing time reduced, while still complying to standards and local regulations as well as making the well design more robust. Data from a well using the annulus B monitoring system will be presented and explained to give an understanding how this can benefit subsea wells throughout life of well.
{"title":"Subsea Well Integrity - Permanent Monitoring Solution to Verify Critical Well Barriers, Simplify and Reduce Cost of Periodic Testing","authors":"S. Grimstad","doi":"10.2118/191677-MS","DOIUrl":"https://doi.org/10.2118/191677-MS","url":null,"abstract":"\u0000 This paper will focus on well integrity standards and give practical examples on how they are applied for wells in the Norwegian Sea.\u0000 Further the paper will review how the standards are used for testing and verifying barriers in a well, and how well integrity on subsea and TLP (tension leg platforms) benefit with the introduction of annulus B monitoring.\u0000 Examples will present how barriers can be moved and testing time reduced, while still complying to standards and local regulations as well as making the well design more robust.\u0000 Data from a well using the annulus B monitoring system will be presented and explained to give an understanding how this can benefit subsea wells throughout life of well.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"51 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90870618","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}
Harshkumar Patel, H. Hariharan, G. Bailey, G. Jung
API flanges maintain integrity through metal-to-metal seal between gasket and flange groove, where sealability depends on contact stresses through bolt makeup-load, tension, fluid-pressure, bending moment. Approaches like API-6AF2 have limitations. With increased deep-water operations, there is an urgent need to understand true sealability/leakage. This requires micro-scale examination of seal. Very few FEA in literature model surface conditions. The objective here has been to develop an analytical model to estimate contact stresses and leakage considering surface topography. This work presents a novel approach for modelling sealability/leakage in metal-to-metal surfaces. It utilizes a contact-mechanics and a fluid-flow model. Deterministic multi-asperity contact-mechanics model provides quantitative estimation of gasket contact stresses, contact gap, and contact area. The leakage model uses contact gap information and correlates it with hydraulic permeability between gasket and groove surfaces and predicts leakage using fluid flow through porous media equations. User inputs are gasket surface topography, size, material properties, operating pressure, and fluid viscosity. The calculations are performed on a small surface domain and results are then scaled-up to obtain contact load/leakage for the entire flange/gasket. Various types of artificially generated surfaces were considered in the model and a parametric study was conducted. Effects of surface finishing have been explained by visual representation of model outputs such as contact status, load distribution, and leakage path. It was observed that critical contact stress to achieve complete sealability is highly dependent on surface characteristics. For similar surface topography, leakage rates are primarily a function of surface RMS. For the same RMS, it is more difficult to seal a randomly rough surface than a patterned or uniform one. As expected, it is easier to seal a soft gasket than a harder one. Similarly, it becomes progressively difficult to seal larger flanges. Parametric studies/analysis can help improve understanding of leakage. The models can be used to understand relative magnitude of challenges in sealing gases/liquids at true viscosities. With further refinement and experimental validation, the models could serve as a design tool that could greatly assist in selecting effective seal and improve well process safety. Further, the presented approach can also be applied to develop leakage models for other metal-to-metal seal applications such as tubular connections, expandables, etc.
{"title":"Advanced Computer Modelling for Metal-to-Metal Seal in API Flanges","authors":"Harshkumar Patel, H. Hariharan, G. Bailey, G. Jung","doi":"10.2118/191636-MS","DOIUrl":"https://doi.org/10.2118/191636-MS","url":null,"abstract":"\u0000 API flanges maintain integrity through metal-to-metal seal between gasket and flange groove, where sealability depends on contact stresses through bolt makeup-load, tension, fluid-pressure, bending moment. Approaches like API-6AF2 have limitations. With increased deep-water operations, there is an urgent need to understand true sealability/leakage. This requires micro-scale examination of seal. Very few FEA in literature model surface conditions. The objective here has been to develop an analytical model to estimate contact stresses and leakage considering surface topography.\u0000 This work presents a novel approach for modelling sealability/leakage in metal-to-metal surfaces. It utilizes a contact-mechanics and a fluid-flow model. Deterministic multi-asperity contact-mechanics model provides quantitative estimation of gasket contact stresses, contact gap, and contact area. The leakage model uses contact gap information and correlates it with hydraulic permeability between gasket and groove surfaces and predicts leakage using fluid flow through porous media equations. User inputs are gasket surface topography, size, material properties, operating pressure, and fluid viscosity. The calculations are performed on a small surface domain and results are then scaled-up to obtain contact load/leakage for the entire flange/gasket.\u0000 Various types of artificially generated surfaces were considered in the model and a parametric study was conducted. Effects of surface finishing have been explained by visual representation of model outputs such as contact status, load distribution, and leakage path. It was observed that critical contact stress to achieve complete sealability is highly dependent on surface characteristics. For similar surface topography, leakage rates are primarily a function of surface RMS. For the same RMS, it is more difficult to seal a randomly rough surface than a patterned or uniform one. As expected, it is easier to seal a soft gasket than a harder one. Similarly, it becomes progressively difficult to seal larger flanges.\u0000 Parametric studies/analysis can help improve understanding of leakage. The models can be used to understand relative magnitude of challenges in sealing gases/liquids at true viscosities. With further refinement and experimental validation, the models could serve as a design tool that could greatly assist in selecting effective seal and improve well process safety. Further, the presented approach can also be applied to develop leakage models for other metal-to-metal seal applications such as tubular connections, expandables, etc.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86398953","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}
S. Amr, Hadeer El Ashhab, M. El-Saban, Paul S. Schietinger, Curtis Caile, Ayman Kaheel, Luis F. Rodríguez
This paper proposes a set of data driven models that use state of the art machine learning techniques and algorithms to predict monthly production of unconventional horizontal wells. The developed models are intended to forecast both producing locations (PLs) and non-producing well locations (NPLs). Furthermore, results of extensive experiments are presented that were conducted using different methodologies and features combinations. Results are measured against conventional Arps's decline curve analysis showing significant boost in prediction accuracy for both NPLs and PLs. The most accurate model outperforms Arps's-based estimates by almost 23% for NPLs and 36% for PLs. Results also show that using data from multiple basins in training models for another basin yields gains in accuracy, especially for basins with relatively small data.
{"title":"A Large-Scale Study for a Multi-Basin Machine Learning Model Predicting Horizontal Well Production","authors":"S. Amr, Hadeer El Ashhab, M. El-Saban, Paul S. Schietinger, Curtis Caile, Ayman Kaheel, Luis F. Rodríguez","doi":"10.2118/191538-MS","DOIUrl":"https://doi.org/10.2118/191538-MS","url":null,"abstract":"\u0000 This paper proposes a set of data driven models that use state of the art machine learning techniques and algorithms to predict monthly production of unconventional horizontal wells. The developed models are intended to forecast both producing locations (PLs) and non-producing well locations (NPLs). Furthermore, results of extensive experiments are presented that were conducted using different methodologies and features combinations. Results are measured against conventional Arps's decline curve analysis showing significant boost in prediction accuracy for both NPLs and PLs. The most accurate model outperforms Arps's-based estimates by almost 23% for NPLs and 36% for PLs. Results also show that using data from multiple basins in training models for another basin yields gains in accuracy, especially for basins with relatively small data.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"112 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80662162","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}
O. Bello, D. Bale, Lei Yang, D. Yang, Ajish Kb, Murali Lajith, S. Lazarus
Given the near ubiquity of fiber-optic, information and communication technologies in reservoir and well management, there is a significant need for one-stop shop downhole distributed sensing data analysis methods together with machine learning techniques towards autonomous analysis of such data sources. However, traditional approaches of converting distributed temperature sensor (DTS) data to actionable insights for optimizing gas lift well operations management remain dependent on training based on human annotations. Annotation of downhole distributed temperature sensor data is a laborious task that is not feasible in practice to train a big data classification algorithm for accurate and reliable anomaly detection of gas lift valves. Furthermore, even obtaining training examples for event diagnosis is challenging due to the rarity of some gas lift valve problems. In gas lift well surveillance, it is essential to generate real-time results to allow a swift response by an engineer to prevent harmful consequences of gas lift valve failure onsets on well performance. The online learning capabilities, also mean that the data classification model can be continuously updated to accommodate reservoir changes in the well environment. In this paper, we propose a novel online real-time DTS data visual analytics platform for gas lift wells using big data tools. The proposed system combines Apache Kafka for data ingestion, Apache Spark for in-memory data processing and analytics, Apache Cassandra for storing raw data and processed results, and INT geo toolkit for data visualization. Specifically, the data analytics pipeline uses data mining algorithms to statistically learn features from the DTS measurements. The learned features are used as inputs to a k-means algorithm and then use supervised learning to predict the performance status of gas lift valves and raise alarms based on analytics-based intelligent warning system. The performance of the proposed system architecture for detecting gas lift valve anomaly is evaluated under varying deployment scenarios. To the best of our knowledge, DTS data analytics pipeline system has not been used for real-time anomaly detection in gas lift well operations.
{"title":"Integrating Downhole Temperature Sensing Datasets and Visual Analytics for Improved Gas Lift Well Surveillance","authors":"O. Bello, D. Bale, Lei Yang, D. Yang, Ajish Kb, Murali Lajith, S. Lazarus","doi":"10.2118/191626-MS","DOIUrl":"https://doi.org/10.2118/191626-MS","url":null,"abstract":"\u0000 Given the near ubiquity of fiber-optic, information and communication technologies in reservoir and well management, there is a significant need for one-stop shop downhole distributed sensing data analysis methods together with machine learning techniques towards autonomous analysis of such data sources. However, traditional approaches of converting distributed temperature sensor (DTS) data to actionable insights for optimizing gas lift well operations management remain dependent on training based on human annotations. Annotation of downhole distributed temperature sensor data is a laborious task that is not feasible in practice to train a big data classification algorithm for accurate and reliable anomaly detection of gas lift valves. Furthermore, even obtaining training examples for event diagnosis is challenging due to the rarity of some gas lift valve problems. In gas lift well surveillance, it is essential to generate real-time results to allow a swift response by an engineer to prevent harmful consequences of gas lift valve failure onsets on well performance. The online learning capabilities, also mean that the data classification model can be continuously updated to accommodate reservoir changes in the well environment. In this paper, we propose a novel online real-time DTS data visual analytics platform for gas lift wells using big data tools. The proposed system combines Apache Kafka for data ingestion, Apache Spark for in-memory data processing and analytics, Apache Cassandra for storing raw data and processed results, and INT geo toolkit for data visualization. Specifically, the data analytics pipeline uses data mining algorithms to statistically learn features from the DTS measurements. The learned features are used as inputs to a k-means algorithm and then use supervised learning to predict the performance status of gas lift valves and raise alarms based on analytics-based intelligent warning system. The performance of the proposed system architecture for detecting gas lift valve anomaly is evaluated under varying deployment scenarios. To the best of our knowledge, DTS data analytics pipeline system has not been used for real-time anomaly detection in gas lift well operations.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89403575","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 last decade has spotted a tremendous upsurge in casing failures. The aftermaths of casing failure can include the possibility of blowouts, environmental pollution, injuries/fatalities, and loss of the entire well to name a few. The motivation behind this work is to present findings from a predictive analytics investigation of casing failure data using supervised and unsupervised data mining algorithms. Scientists and researchers have speculated the potential underlying causes of failure but to date this type of work remains unpublished and unavailable in the public domain literature. The study assembled comprehensive data from eighty land-based wells during drilling, fracturing, workover, and production operations. Twenty wells suffered from casing failure while the remaining sixty offset wells were compiled from well reports, fracturing treatment data, drilling records, and recovered casing data. The failures were unsystemic but included fatigue failure, bending stresses from excessive dogleg, buckling, high hoop stress on connections, and split coupling. The failures were detected at various depths, both in cemented and uncemented hole sections. Failures were spotted at the upper and lower production casing. Using a predictive analytics software from SAS, twenty-six variables were evaluated through the application of various data mining techniques on the failed casing data points. The missing data was accounted for using multivariate normal imputation. The study outcome addressed common casing sizes and couplings involved with each failure, failure location, hydraulic fracturing stages, cement impairment, dogleg severity, thermal and tensile loads, production-induced shearing, and DLS. The predictive algorithms used in this study included Logistic Regression, supervised Hierarchal Clustering, and Decision Trees. While the descriptive analytics manifested in visual representations included Scatterplot Matrices and PivotTables. A combination of the causes of failure were identified. A total of five statistical techniques using the aforementioned algorithms were developed to evaluate the concurrent effect of the interplay of these variables. Nineteen variables were believed to possess a high contribution to failure. Scatterplot matrix suggested a complex correlation between the total base water used in fracturing simulation and casing thickness. Logistic Regression suggested nine variables were significant including: TVD, operator, frac start month, MD of most severe DL, heel TVD, hole size, BHT, total proppant mass, cumulative DLS in lateral and build sections variables as significant failure contributors. PivotTables showed that the rate of casing failure was highest during the winter season. This investigation is aimed to develop a thorough understanding of casing failures and the myriad of contributing factors to develop comprehensive predictive models for future failure prediction via the application of data mining algorithms. These m
{"title":"Casing Failure Data Analytics: A Novel Data Mining Approach in Predicting Casing Failures for Improved Drilling Performance and Production Optimization","authors":"C. Noshi, S. Noynaert, J. Schubert","doi":"10.2118/191570-MS","DOIUrl":"https://doi.org/10.2118/191570-MS","url":null,"abstract":"\u0000 The last decade has spotted a tremendous upsurge in casing failures. The aftermaths of casing failure can include the possibility of blowouts, environmental pollution, injuries/fatalities, and loss of the entire well to name a few. The motivation behind this work is to present findings from a predictive analytics investigation of casing failure data using supervised and unsupervised data mining algorithms. Scientists and researchers have speculated the potential underlying causes of failure but to date this type of work remains unpublished and unavailable in the public domain literature.\u0000 The study assembled comprehensive data from eighty land-based wells during drilling, fracturing, workover, and production operations. Twenty wells suffered from casing failure while the remaining sixty offset wells were compiled from well reports, fracturing treatment data, drilling records, and recovered casing data. The failures were unsystemic but included fatigue failure, bending stresses from excessive dogleg, buckling, high hoop stress on connections, and split coupling. The failures were detected at various depths, both in cemented and uncemented hole sections. Failures were spotted at the upper and lower production casing. Using a predictive analytics software from SAS, twenty-six variables were evaluated through the application of various data mining techniques on the failed casing data points. The missing data was accounted for using multivariate normal imputation. The study outcome addressed common casing sizes and couplings involved with each failure, failure location, hydraulic fracturing stages, cement impairment, dogleg severity, thermal and tensile loads, production-induced shearing, and DLS. The predictive algorithms used in this study included Logistic Regression, supervised Hierarchal Clustering, and Decision Trees. While the descriptive analytics manifested in visual representations included Scatterplot Matrices and PivotTables. A combination of the causes of failure were identified. A total of five statistical techniques using the aforementioned algorithms were developed to evaluate the concurrent effect of the interplay of these variables. Nineteen variables were believed to possess a high contribution to failure. Scatterplot matrix suggested a complex correlation between the total base water used in fracturing simulation and casing thickness. Logistic Regression suggested nine variables were significant including: TVD, operator, frac start month, MD of most severe DL, heel TVD, hole size, BHT, total proppant mass, cumulative DLS in lateral and build sections variables as significant failure contributors. PivotTables showed that the rate of casing failure was highest during the winter season.\u0000 This investigation is aimed to develop a thorough understanding of casing failures and the myriad of contributing factors to develop comprehensive predictive models for future failure prediction via the application of data mining algorithms. These m","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"2013 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86465734","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}
T. Kristiansen, T. Dyngeland, S. Kinn, R. Flatebø, N. Aarseth
Shale is a general term used for argillaceous (clay-rich) rocks which are the most abundant sediment on the earth. It is believed that clay rich rocks comprise more than 50-75% of the geologic column. Shale has very varying petrophysical and mechanical properties. Shale is in the most cases acting as a trap or seal for hydrocarbon migration, but has also in more recent years been targeted as a reservoir target in some basins. In some wells it has been observed on cement bond logs that shales in uncemented intervals have moved in and closed the annulus. Pressure communication testing has been performed on these sections and the sections has been qualified as well barrier elements (Williams et al., 2009) for plug and abandonment (P&A) purposes. The main mechanism behind the deformation process is believed to be shale creep. In this paper we will discuss shale creep and other shale deformation mechanisms and how an understanding of these can be used to activate shale that has not contacted the casing yet to form a well barrier. We have developed a numerical model based on first order principles to better understand the mechanical deformation process. We are also supporting the modeling results with laboratory experiments, before we discuss a couple of field cases where shale intervals have been activated and verified to have formed a well barrier as part of the well construction process in new wells.
页岩是地球上沉积物最丰富的泥质(富含粘土)岩石的总称。据信，富含粘土的岩石占地质柱的50-75%以上。页岩具有非常不同的岩石物理和力学性质。页岩在大多数情况下作为油气运移的圈闭或密封，但近年来在一些盆地也被视为储层目标。在一些井中，水泥胶结测井观察到，未胶结层段的页岩进入并封闭环空。在这些井段进行了压力通信测试，这些井段已被认定为井眼隔离元件(Williams et al.， 2009)，可用于封井弃井(P&A)。变形过程背后的主要机制被认为是页岩蠕变。在本文中，我们将讨论页岩蠕变和其他页岩变形机制，以及如何利用对这些机制的理解来激活尚未接触套管的页岩，以形成井眼屏障。为了更好地理解机械变形过程，我们开发了基于一阶原理的数值模型。我们还通过实验室实验来支持建模结果，然后我们讨论了几个现场案例，在这些案例中，页岩层段已经被激活并验证形成了井障，这是新井建井过程的一部分。
{"title":"Activating Shale to Form Well Barriers: Theory and Field Examples","authors":"T. Kristiansen, T. Dyngeland, S. Kinn, R. Flatebø, N. Aarseth","doi":"10.2118/191607-MS","DOIUrl":"https://doi.org/10.2118/191607-MS","url":null,"abstract":"\u0000 Shale is a general term used for argillaceous (clay-rich) rocks which are the most abundant sediment on the earth. It is believed that clay rich rocks comprise more than 50-75% of the geologic column. Shale has very varying petrophysical and mechanical properties. Shale is in the most cases acting as a trap or seal for hydrocarbon migration, but has also in more recent years been targeted as a reservoir target in some basins. In some wells it has been observed on cement bond logs that shales in uncemented intervals have moved in and closed the annulus. Pressure communication testing has been performed on these sections and the sections has been qualified as well barrier elements (Williams et al., 2009) for plug and abandonment (P&A) purposes. The main mechanism behind the deformation process is believed to be shale creep.\u0000 In this paper we will discuss shale creep and other shale deformation mechanisms and how an understanding of these can be used to activate shale that has not contacted the casing yet to form a well barrier. We have developed a numerical model based on first order principles to better understand the mechanical deformation process. We are also supporting the modeling results with laboratory experiments, before we discuss a couple of field cases where shale intervals have been activated and verified to have formed a well barrier as part of the well construction process in new wells.","PeriodicalId":11015,"journal":{"name":"Day 1 Mon, September 24, 2018","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2018-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81913871","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：