Prakash Purswani, Russell T. Johns, Zuleima T. Karpyn
The relationship between residual saturation and wettability is critical for modeling multiphase processes like enhanced oil recovery, CO2 sequestration, and geologic storage of hydrogen. The wetting state of a core is often quantified through Amott indices, which are estimated from the ratio of the saturation fraction that flows spontaneously to the total saturation change that occurs due to spontaneous flow and forced injection. Observations from traditional coreflooding experiments show a minimum in the trends of residual oil saturation (Sor) around mixed-wet conditions. Amott indices, however, provide an average measure of wettability because of their intrinsic dependence on a variety of factors such as the initial oil saturation, aging conditions, rock heterogeneity, etc. Thus, the use of Amott indices could potentially cloud the observed trends of residual saturation with wettability.
Using pore-network modeling (PNM), we show that Sor varies monotonically with the contact angle, which is a direct measure of wettability. That is, for fixed initial oil saturation, the Sor decreases monotonically as the reservoir becomes more water-wet (decreasing contact angle). Further, the calculation of Amott indices for the PNM data sets shows that a plot of the Sor vs. Amott indices also shows this monotonic trend, but only if the initial oil saturation is kept fixed. Thus, for the cases presented here, we show that there is no minimum residual saturation at mixed-wet conditions as wettability changes.
In this research, we employ a numerical approach to quantify trends of Sor against the traditional definition of wettability. Through the analysis of our numerical work and literature experiments, we find that under isolated conditions (constant initial saturation), linear trends exist between Sor and wettability. This can have important implications for low salinity waterflooding, water-alternating-gas enhanced oil recovery, or CO2 sequestration where the effects of wettability are critical to understand phase trapping.
残余饱和度与润湿性之间的关系对于模拟提高石油采收率、二氧化碳封存和氢气地质封存等多相过程至关重要。岩心的润湿状态通常通过阿莫特指数来量化，该指数根据自发流动的饱和度部分与自发流动和强制注入引起的总饱和度变化之比估算得出。传统岩心注水实验的观察结果表明，在混湿条件下，残余油饱和度 (Sor) 的变化趋势最小。然而，由于阿莫特指数与多种因素（如初始油饱和度、老化条件、岩石异质性等）有内在的依赖关系，因此只能对润湿性进行平均测量。利用孔隙网络建模（PNM），我们发现 Sor 随接触角的变化而单调变化，而接触角是润湿性的直接衡量标准。也就是说，在初始含油饱和度固定的情况下，随着储层的水湿度增加（接触角减小），Sor 会单调地减小。此外，PNM 数据集的阿莫特指数计算表明，Sor 与阿莫特指数的关系图也显示出这种单调趋势，但前提是初始油饱和度保持固定。因此，对于本文介绍的情况，我们表明，随着润湿性的变化，在混湿条件下不存在最小残余饱和度。在这项研究中，我们采用了一种数值方法，对照润湿性的传统定义来量化 Sor 的趋势。通过对我们的数值工作和文献实验的分析，我们发现在孤立条件下（恒定的初始饱和度），Sor 和润湿性之间存在线性趋势。这对低盐度注水、水煤气强化采油或二氧化碳封存具有重要意义，因为在这些情况下，润湿性的影响对于理解相捕集至关重要。
{"title":"Relationship Between Residual Saturations and Wettability Using Pore-Network Modeling","authors":"Prakash Purswani, Russell T. Johns, Zuleima T. Karpyn","doi":"10.2118/206379-pa","DOIUrl":"https://doi.org/10.2118/206379-pa","url":null,"abstract":"<p>The relationship between residual saturation and wettability is critical for modeling multiphase processes like enhanced oil recovery, CO<sub>2</sub> sequestration, and geologic storage of hydrogen. The wetting state of a core is often quantified through Amott indices, which are estimated from the ratio of the saturation fraction that flows spontaneously to the total saturation change that occurs due to spontaneous flow and forced injection. Observations from traditional coreflooding experiments show a minimum in the trends of residual oil saturation (<em>S<sub>or</sub></em>) around mixed-wet conditions. Amott indices, however, provide an average measure of wettability because of their intrinsic dependence on a variety of factors such as the initial oil saturation, aging conditions, rock heterogeneity, etc. Thus, the use of Amott indices could potentially cloud the observed trends of residual saturation with wettability.</p><p>Using pore-network modeling (PNM), we show that <em>S<sub>or</sub></em> varies monotonically with the contact angle, which is a direct measure of wettability. That is, for fixed initial oil saturation, the <em>S<sub>or</sub></em> decreases monotonically as the reservoir becomes more water-wet (decreasing contact angle). Further, the calculation of Amott indices for the PNM data sets shows that a plot of the <em>S<sub>or</sub></em> vs. Amott indices also shows this monotonic trend, but only if the initial oil saturation is kept fixed. Thus, for the cases presented here, we show that there is no minimum residual saturation at mixed-wet conditions as wettability changes.</p><p>In this research, we employ a numerical approach to quantify trends of <em>S<sub>or</sub></em> against the traditional definition of wettability. Through the analysis of our numerical work and literature experiments, we find that under isolated conditions (constant initial saturation), linear trends exist between <em>S<sub>or</sub></em> and wettability. This can have important implications for low salinity waterflooding, water-alternating-gas enhanced oil recovery, or CO<sub>2</sub> sequestration where the effects of wettability are critical to understand phase trapping.</p>","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"32 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140581375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Sidetrack and deepening wells play a pivotal role in enhancing oil and gas recovery while simultaneously reducing drilling costs, particularly in cluster well scenarios. These wells leverage existing wellbores effectively, resulting in substantial reductions in development expenses. Deepening wells maximize cost savings by utilizing the entire length of preexisting wellbores. These wells strategically access low-permeability layers, thin pay zones, wedge zones, and marginal reserves while also serving as rapid response solutions during emergencies to expedite risk mitigation in accidents. There is a pressing need for expedient, safer, and cost-effective well designs to achieve economic efficiency, which necessitates the development of advanced design methodologies. However, designing optimized 3D sidetrack and deepening well trajectories for oil and gas reservoir access while mitigating collision risks is a complex and time-consuming task that demands meticulous planning and exhaustive well path analysis, often involving multiple iterations to ensure cost-effective solutions meeting drillability and safety constraints. In this study, we develop an integrated framework for the accelerated design of sidetrack and deepening well trajectories, complemented by a trajectory optimization algorithm to generate safer and cost-effective well trajectories. The developed framework is rigorously tested in a live Nigerian oil and gas field. The case study involves the design of a sidetrack and a deepening well trajectory in a crowded brownfield consisting of 21 legacy wells. The results of the case study exhibit the significance of the established framework on streamlining the well design process, leading to expedited creation of efficient and safe sidetrack and deepening well trajectories.
{"title":"Accelerated Design of Sidetrack and Deepening Well Trajectories","authors":"Umesh Singh, Rizwan Pathan, Amol Dilip Joshi, Adrien Cavé, Cédric Fouchard, Antonin Baume","doi":"10.2118/218395-pa","DOIUrl":"https://doi.org/10.2118/218395-pa","url":null,"abstract":"\u0000 Sidetrack and deepening wells play a pivotal role in enhancing oil and gas recovery while simultaneously reducing drilling costs, particularly in cluster well scenarios. These wells leverage existing wellbores effectively, resulting in substantial reductions in development expenses. Deepening wells maximize cost savings by utilizing the entire length of preexisting wellbores. These wells strategically access low-permeability layers, thin pay zones, wedge zones, and marginal reserves while also serving as rapid response solutions during emergencies to expedite risk mitigation in accidents. There is a pressing need for expedient, safer, and cost-effective well designs to achieve economic efficiency, which necessitates the development of advanced design methodologies. However, designing optimized 3D sidetrack and deepening well trajectories for oil and gas reservoir access while mitigating collision risks is a complex and time-consuming task that demands meticulous planning and exhaustive well path analysis, often involving multiple iterations to ensure cost-effective solutions meeting drillability and safety constraints. In this study, we develop an integrated framework for the accelerated design of sidetrack and deepening well trajectories, complemented by a trajectory optimization algorithm to generate safer and cost-effective well trajectories. The developed framework is rigorously tested in a live Nigerian oil and gas field. The case study involves the design of a sidetrack and a deepening well trajectory in a crowded brownfield consisting of 21 legacy wells. The results of the case study exhibit the significance of the established framework on streamlining the well design process, leading to expedited creation of efficient and safe sidetrack and deepening well trajectories.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"282 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138985912","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Cao Wei, Haitao Li, Hongwen Luo, Ying Li, Shiqing Cheng
Accurate identification of the individual-layer parameters for vertically fractured wells in commingled reservoirs is essential for development plan design, reservoir numerical simulation, and stimulation measure selection. Different semi-analytical and numerical models are generally applied in multilayer transient testing (MLT) analysis to determine the properties of individual layer. However, these approaches require numerous computations and are complicated to program due to the fracture and reservoir discretization. This work thus presents the generalized analytical solutions of vertically fractured wells in infinite, closed, or constant-pressure commingled reservoirs with both computational and functional simplicity. The fully analytical solutions are derived based on the early-time approximate solutions of infinite-conductivity fracture and trilinear flow models, infinite-conductivity fracture solutions, pressure superposition principle, and Duhamel principle. A systematic verification by employing a standardized well testing software and trilinear flow model is conducted to ensure the general application accuracy of the presented solutions. The results show that the developed analytical solutions are valid when the dimensionless fracture conductivity is more than 2 (FcD > 2) with an average absolute percent deviation (AAD) of ~2% for pressure and that is ~4% for pressure derivative. The developed analytical solutions also exhibit improvements in early-time pressure and derivative calculation. Finally, a field case of a four-layer fractured well is interpreted by the developed solutions and well testing software to illustrate the feasibility. The interpretation results of two methods are nearly identical, with only a minor difference. The developed analytical solutions are computationally accurate while maintaining functional simplicity and can be considered as an alternative to the current semi-analytical and numerical approaches in MLT analysis.
{"title":"Generalized Analytical Solutions of Vertically Fractured Wells in Commingled Reservoirs: Field Case Study","authors":"Cao Wei, Haitao Li, Hongwen Luo, Ying Li, Shiqing Cheng","doi":"10.2118/218391-pa","DOIUrl":"https://doi.org/10.2118/218391-pa","url":null,"abstract":"\u0000 Accurate identification of the individual-layer parameters for vertically fractured wells in commingled reservoirs is essential for development plan design, reservoir numerical simulation, and stimulation measure selection. Different semi-analytical and numerical models are generally applied in multilayer transient testing (MLT) analysis to determine the properties of individual layer. However, these approaches require numerous computations and are complicated to program due to the fracture and reservoir discretization. This work thus presents the generalized analytical solutions of vertically fractured wells in infinite, closed, or constant-pressure commingled reservoirs with both computational and functional simplicity. The fully analytical solutions are derived based on the early-time approximate solutions of infinite-conductivity fracture and trilinear flow models, infinite-conductivity fracture solutions, pressure superposition principle, and Duhamel principle. A systematic verification by employing a standardized well testing software and trilinear flow model is conducted to ensure the general application accuracy of the presented solutions. The results show that the developed analytical solutions are valid when the dimensionless fracture conductivity is more than 2 (FcD > 2) with an average absolute percent deviation (AAD) of ~2% for pressure and that is ~4% for pressure derivative. The developed analytical solutions also exhibit improvements in early-time pressure and derivative calculation. Finally, a field case of a four-layer fractured well is interpreted by the developed solutions and well testing software to illustrate the feasibility. The interpretation results of two methods are nearly identical, with only a minor difference. The developed analytical solutions are computationally accurate while maintaining functional simplicity and can be considered as an alternative to the current semi-analytical and numerical approaches in MLT analysis.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"534 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139023564","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Junrong Liu, Yanhui Han, Qingsheng Jia, Lei Zhang, Ming Liu, Zhigang Li
The accurate measurement of dynamic water cut is of great interest for analyzing reservoir performance and optimizing oilwell production. Downhole water-cut measurement is a very challenging work. Moreover, the surface-measured water cut is a comprehensive indicator of commingled producing well and it is difficult to use this parameter to deduce the downhole water cut of each contributing layer. In this paper, we propose to use distributed fiber-optic acoustic sensing (DAS) technology for the classification of water-cut range. DAS can dynamically monitor the entire wellbore by “listening” to the acoustic signals during flow. A large number of laboratory experimental data from DAS have been collected and analyzed using wavelet time scattering transform and short-time Fourier transform (STFT). The extracted low-variance scattering feature, short time-frequency feature, and fusion feature (combination of two extracted features) were learned with backpropagation (BP) neural network, decision tree (DT), and random forest (RF) algorithm. Then, a classification method of water-cut range in oil-water flow was established with machine learning. Field DAS data were collected from two oil wells to verify the effectiveness of the proposed method. The classification accuracies for the vertical well (Well A) are 92.4% and 87.4% by DT and RF model, respectively. For the horizontal well (Well B), the average classification accuracy exceeds 90% for all three methods. Water shutoff measure was conducted in Well B, and an obvious water decrease was realized. The result shows that the fusion feature overweighs single feature in machine learning with DAS data. This study provides a novel way to identify downhole water-cut range and detect water entry location in horizontal, vertical, and deviated oil-producing wells.
{"title":"Oil-Water Flowing Experiments and Water-Cut Range Classification Approach Using Distributed Acoustic Sensing","authors":"Junrong Liu, Yanhui Han, Qingsheng Jia, Lei Zhang, Ming Liu, Zhigang Li","doi":"10.2118/218389-pa","DOIUrl":"https://doi.org/10.2118/218389-pa","url":null,"abstract":"\u0000 The accurate measurement of dynamic water cut is of great interest for analyzing reservoir performance and optimizing oilwell production. Downhole water-cut measurement is a very challenging work. Moreover, the surface-measured water cut is a comprehensive indicator of commingled producing well and it is difficult to use this parameter to deduce the downhole water cut of each contributing layer. In this paper, we propose to use distributed fiber-optic acoustic sensing (DAS) technology for the classification of water-cut range. DAS can dynamically monitor the entire wellbore by “listening” to the acoustic signals during flow. A large number of laboratory experimental data from DAS have been collected and analyzed using wavelet time scattering transform and short-time Fourier transform (STFT). The extracted low-variance scattering feature, short time-frequency feature, and fusion feature (combination of two extracted features) were learned with backpropagation (BP) neural network, decision tree (DT), and random forest (RF) algorithm. Then, a classification method of water-cut range in oil-water flow was established with machine learning. Field DAS data were collected from two oil wells to verify the effectiveness of the proposed method. The classification accuracies for the vertical well (Well A) are 92.4% and 87.4% by DT and RF model, respectively. For the horizontal well (Well B), the average classification accuracy exceeds 90% for all three methods. Water shutoff measure was conducted in Well B, and an obvious water decrease was realized. The result shows that the fusion feature overweighs single feature in machine learning with DAS data. This study provides a novel way to identify downhole water-cut range and detect water entry location in horizontal, vertical, and deviated oil-producing wells.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"118 46","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138608278","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yan Chen, Bo Miao, Yang Wang, Yunan Huang, YuQiang Jiang, Xiangchao Shi
Liquid loading occurs when gas production falls below the critical liquid-carrying flow rate of the gas well, resulting in the inability to remove the condensate or water in the gas well. Liquid loading can lead to a sharp reduction in production, which affects the gas well ultimate recovery. Accurate prediction of the timing of liquid loading is important for implementing mitigations that reduce liquid accumulation in the production tubing and prevent gas production impairment, as well as for the stability of production. Existing liquid-loading forecasting methods have a time offset in the determination of liquid loading, and there is great variation in the results for different gas wells. Currently, supervisory control and data acquisition (SCADA) systems are widely used for gas well production data acquisition, but the data are not effectively utilized. Deep machine learning techniques are applied to the field data from gas wells and have achieved significant effectiveness. In this study, a bidirectional long short-term memory network (Bi-LSTM) was used to conduct feature extraction on the production data, and the extracted feature was spliced together with the geological and engineering parameter feature. These features were combined with self-attention mechanisms to predict the time of the next liquid loading. Because the modeling results fit the actual liquid loading in production scenarios better, our method also customizes the loss functions. Experimental verification was conducted using actual production data from 13 gas wells. The recall was 1 and F1 was 0.87 for the experimental data in the model, and the customized loss function led to a 6.5% improvement in F1. The experimental results verify that our method can accurately forecast liquid-loading onset in gas wells in a timely manner, which can help reduce costs and increase efficiency in shale gas production.
{"title":"A Deep Regression Method for Gas Well Liquid Loading Prediction","authors":"Yan Chen, Bo Miao, Yang Wang, Yunan Huang, YuQiang Jiang, Xiangchao Shi","doi":"10.2118/218387-pa","DOIUrl":"https://doi.org/10.2118/218387-pa","url":null,"abstract":"\u0000 Liquid loading occurs when gas production falls below the critical liquid-carrying flow rate of the gas well, resulting in the inability to remove the condensate or water in the gas well. Liquid loading can lead to a sharp reduction in production, which affects the gas well ultimate recovery. Accurate prediction of the timing of liquid loading is important for implementing mitigations that reduce liquid accumulation in the production tubing and prevent gas production impairment, as well as for the stability of production. Existing liquid-loading forecasting methods have a time offset in the determination of liquid loading, and there is great variation in the results for different gas wells. Currently, supervisory control and data acquisition (SCADA) systems are widely used for gas well production data acquisition, but the data are not effectively utilized. Deep machine learning techniques are applied to the field data from gas wells and have achieved significant effectiveness. In this study, a bidirectional long short-term memory network (Bi-LSTM) was used to conduct feature extraction on the production data, and the extracted feature was spliced together with the geological and engineering parameter feature. These features were combined with self-attention mechanisms to predict the time of the next liquid loading. Because the modeling results fit the actual liquid loading in production scenarios better, our method also customizes the loss functions. Experimental verification was conducted using actual production data from 13 gas wells. The recall was 1 and F1 was 0.87 for the experimental data in the model, and the customized loss function led to a 6.5% improvement in F1. The experimental results verify that our method can accurately forecast liquid-loading onset in gas wells in a timely manner, which can help reduce costs and increase efficiency in shale gas production.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":" 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138613358","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
One of the major advances in polycrystalline diamond compact (PDC) bits in the last 10 years is the global adoption of 3D-shaped PDC cutters. By manipulating the cutter shape based on the understandings of cutter–rock interaction mechanisms, the cutting efficiency and mechanical properties of PDC cutters have been greatly improved. Ongoing innovations in 3D-shaped PDC cutter technology are critical to overcoming the more and more challenging formations in ultradeep wells, such as the 10 000-m-deep wells being drilled in China. Such an important role for 3D-shaped PDC cutters in oil and gas drilling applications necessitates a complete and effective failure analysis method. However, the current International Association of Drilling Contractors (IADC) dull grading cannot fulfill this objective. It is out of date in judging the damages to PDC bits and exhibits more limitations in addressing the unique challenges presented by complicated cutter shapes. To address this issue, an intelligent recognition model for PDC bit damage identification was developed based on the image analysis technology and the YOLOv7 algorithm. More than 10,000 dull bit images were used to train and validate this intelligent recognition model, which were collected from 363 PDC bits that suffered different degrees of damage after being used to drill 185 wells in the Sinopec Shengli Oilfield. Compared to the existing models, the developed intelligent recognition model has several notable contributions. First, the developed model is capable of recognizing the damages of various shaped PDC cutters commonly used by the global bit manufacturers, enabling a more accurate assessment of the failure behaviors of shaped cutters and their bits. The detection accuracy of the developed model exceeds 80% based on the confusion matrix. The recognition results by the developed artificial intelligence (AI) model are consistent with the actual failure modes judged by experienced drilling engineers. Second, the developed AI model provides direct qualitative identification of the failure modes and failure reasons for both cutters and PDC bits rather than the quantitative evaluation of the missing diamond layer used by IADC dull grading. Furthermore, the developed model eliminates the effect of reclaimed cutters on the AI detection results based on the implicit use of spatial cues in the YOLOv7 algorithm. The intelligent recognition model developed in this work can provide reliable and valuable guidance for the post-run evaluation, the bit selection for the next run, and the iterative optimization of bit design.
{"title":"Failure Forensics of Shaped PDC Cutters Using Image Analysis and Deep Learning","authors":"Wei Liu, Jianchao Li, Deli Gao","doi":"10.2118/218383-pa","DOIUrl":"https://doi.org/10.2118/218383-pa","url":null,"abstract":"\u0000 One of the major advances in polycrystalline diamond compact (PDC) bits in the last 10 years is the global adoption of 3D-shaped PDC cutters. By manipulating the cutter shape based on the understandings of cutter–rock interaction mechanisms, the cutting efficiency and mechanical properties of PDC cutters have been greatly improved. Ongoing innovations in 3D-shaped PDC cutter technology are critical to overcoming the more and more challenging formations in ultradeep wells, such as the 10 000-m-deep wells being drilled in China. Such an important role for 3D-shaped PDC cutters in oil and gas drilling applications necessitates a complete and effective failure analysis method. However, the current International Association of Drilling Contractors (IADC) dull grading cannot fulfill this objective. It is out of date in judging the damages to PDC bits and exhibits more limitations in addressing the unique challenges presented by complicated cutter shapes.\u0000 To address this issue, an intelligent recognition model for PDC bit damage identification was developed based on the image analysis technology and the YOLOv7 algorithm. More than 10,000 dull bit images were used to train and validate this intelligent recognition model, which were collected from 363 PDC bits that suffered different degrees of damage after being used to drill 185 wells in the Sinopec Shengli Oilfield. Compared to the existing models, the developed intelligent recognition model has several notable contributions. First, the developed model is capable of recognizing the damages of various shaped PDC cutters commonly used by the global bit manufacturers, enabling a more accurate assessment of the failure behaviors of shaped cutters and their bits. The detection accuracy of the developed model exceeds 80% based on the confusion matrix. The recognition results by the developed artificial intelligence (AI) model are consistent with the actual failure modes judged by experienced drilling engineers. Second, the developed AI model provides direct qualitative identification of the failure modes and failure reasons for both cutters and PDC bits rather than the quantitative evaluation of the missing diamond layer used by IADC dull grading. Furthermore, the developed model eliminates the effect of reclaimed cutters on the AI detection results based on the implicit use of spatial cues in the YOLOv7 algorithm. The intelligent recognition model developed in this work can provide reliable and valuable guidance for the post-run evaluation, the bit selection for the next run, and the iterative optimization of bit design.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"362 3","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139021699","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Shale barrier has been widely reported in many steam-assisted gravity drainage (SAGD) projects. For an SAGD project, the properties and distribution of shale barrier can significantly impede the vertical expansion and lateral spread of steam chamber. Currently, although some literature has discussed the shale barrier effect from different perspectives, a systematic investigation combining the scaled physical and numerical simulations is still lacking. Simultaneously, how to reduce the shale barrier effect is also challenging. In this study, aiming at the Long Lake oilsands resources, combining the methods of 3D experiment and numerical simulation, a new method based on a top horizontal injection well is proposed to reduce the impact of shale barrier on the SAGD process. First, based on a dimensionless scaling criterion of gravity-drainage process, we conducted two 3D gravity-drainage experiments (base case and improved case) to explore the effect of shale barrier and the performance of top injection well on SAGD production. During experiments, to improve the similarity between the laboratory 3D model and the field prototype, a new wellbore model and a physical simulation method of shale barrier are proposed. The location of the shale barrier is placed above the steam injection well, and the top injection well is set above the shale barrier. For an improved case, once the steam chamber front reaches the horizontal edge of the shale barrier, the top injection well can be activated as a steam injection well to replace the previous steam injection well in the SAGD well pair. From the experimental observation, the effect of the top injection well is evaluated. Subsequently, a set of numerical simulation runs are performed to match the experimental measurements. Therefore, from this laboratory-scale simulation model, the effect of shale barrier size is discussed, and the switch time of the top injection well is also optimized to maximize the recovery process. Experimental results indicate that a top injection well-based oil drainage mode can effectively unlock the heavy crude oil above shale barrier and improve the entire SAGD production. Compared with a basic SAGD case, the top injection well can increase the final oil recovery factor by about 8%. Simultaneously, through a mass conservation law, it is calculated that the unlocking angle of remaining oil reserve above the shale barrier is about 6°. The angle can be used to effectively evaluate the recoverable oil reserve after the SAGD process for the heavy oil reservoir with a shale barrier. The simulation results of our laboratory-scale numerical simulation model are in good agreement with the experimental observation. The optimized switch time of the top injection well is the end of the second lateral expansion stage. This paper proposes a new oil drainage mode that can effectively reduce the shale barrier effect on SAGD production and thus improve the recovery performance of heavy oil reservoirs.
{"title":"A New Method to Reduce Shale Barrier Effect on SAGD Process: Experimental and Numerical Simulation Studies using Laboratory-Scale Model","authors":"Xiaohu Dong, Huiqing Liu, Yunfei Tian, Siyi Liu, Jiaxin Li, Liangliang Jiang, Zhangxin Chen","doi":"10.2118/218390-pa","DOIUrl":"https://doi.org/10.2118/218390-pa","url":null,"abstract":"\u0000 Shale barrier has been widely reported in many steam-assisted gravity drainage (SAGD) projects. For an SAGD project, the properties and distribution of shale barrier can significantly impede the vertical expansion and lateral spread of steam chamber. Currently, although some literature has discussed the shale barrier effect from different perspectives, a systematic investigation combining the scaled physical and numerical simulations is still lacking. Simultaneously, how to reduce the shale barrier effect is also challenging. In this study, aiming at the Long Lake oilsands resources, combining the methods of 3D experiment and numerical simulation, a new method based on a top horizontal injection well is proposed to reduce the impact of shale barrier on the SAGD process. First, based on a dimensionless scaling criterion of gravity-drainage process, we conducted two 3D gravity-drainage experiments (base case and improved case) to explore the effect of shale barrier and the performance of top injection well on SAGD production. During experiments, to improve the similarity between the laboratory 3D model and the field prototype, a new wellbore model and a physical simulation method of shale barrier are proposed. The location of the shale barrier is placed above the steam injection well, and the top injection well is set above the shale barrier. For an improved case, once the steam chamber front reaches the horizontal edge of the shale barrier, the top injection well can be activated as a steam injection well to replace the previous steam injection well in the SAGD well pair. From the experimental observation, the effect of the top injection well is evaluated. Subsequently, a set of numerical simulation runs are performed to match the experimental measurements. Therefore, from this laboratory-scale simulation model, the effect of shale barrier size is discussed, and the switch time of the top injection well is also optimized to maximize the recovery process. Experimental results indicate that a top injection well-based oil drainage mode can effectively unlock the heavy crude oil above shale barrier and improve the entire SAGD production. Compared with a basic SAGD case, the top injection well can increase the final oil recovery factor by about 8%. Simultaneously, through a mass conservation law, it is calculated that the unlocking angle of remaining oil reserve above the shale barrier is about 6°. The angle can be used to effectively evaluate the recoverable oil reserve after the SAGD process for the heavy oil reservoir with a shale barrier. The simulation results of our laboratory-scale numerical simulation model are in good agreement with the experimental observation. The optimized switch time of the top injection well is the end of the second lateral expansion stage. This paper proposes a new oil drainage mode that can effectively reduce the shale barrier effect on SAGD production and thus improve the recovery performance of heavy oil reservoirs.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"23 30","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138624945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Bi, Jing Li, Keliu Wu, Zhangxin Chen, Shengnan Chen, Liangliang Jiang, Dong Feng, Peng Deng
Surrogate models play a vital role in reducing computational complexity and time burden for reservoir simulations. However, traditional surrogate models suffer from limitations in autonomous temporal information learning and restrictions in generalization potential, which is due to a lack of integration with physical knowledge. In response to these challenges, a physics-informed spatial-temporal neural network (PI-STNN) is proposed in this work, which incorporates flow theory into the loss function and uniquely integrates a deep convolutional encoder-decoder (DCED) with a convolutional long short-term memory (ConvLSTM) network. To demonstrate the robustness and generalization capabilities of the PI-STNN model, its performance was compared against both a purely data-driven model with the same neural network architecture and the renowned Fourier neural operator (FNO) in a comprehensive analysis. Besides, by adopting a transfer learning strategy, the trained PI-STNN model was adapted to the fractured flow fields to investigate the impact of natural fractures on its prediction accuracy. The results indicate that the PI-STNN not only excels in comparison with the purely data-driven model but also demonstrates a competitive edge over the FNO in reservoir simulation. Especially in strongly heterogeneous flow fields with fractures, the PI-STNN can still maintain high prediction accuracy. Building on this prediction accuracy, the PI-STNN model further offers a distinct advantage in efficiently performing uncertainty quantification, enabling rapid and comprehensive analysis of investment decisions in oil and gas development.
{"title":"A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification","authors":"J. Bi, Jing Li, Keliu Wu, Zhangxin Chen, Shengnan Chen, Liangliang Jiang, Dong Feng, Peng Deng","doi":"10.2118/218386-pa","DOIUrl":"https://doi.org/10.2118/218386-pa","url":null,"abstract":"\u0000 Surrogate models play a vital role in reducing computational complexity and time burden for reservoir simulations. However, traditional surrogate models suffer from limitations in autonomous temporal information learning and restrictions in generalization potential, which is due to a lack of integration with physical knowledge. In response to these challenges, a physics-informed spatial-temporal neural network (PI-STNN) is proposed in this work, which incorporates flow theory into the loss function and uniquely integrates a deep convolutional encoder-decoder (DCED) with a convolutional long short-term memory (ConvLSTM) network. To demonstrate the robustness and generalization capabilities of the PI-STNN model, its performance was compared against both a purely data-driven model with the same neural network architecture and the renowned Fourier neural operator (FNO) in a comprehensive analysis. Besides, by adopting a transfer learning strategy, the trained PI-STNN model was adapted to the fractured flow fields to investigate the impact of natural fractures on its prediction accuracy. The results indicate that the PI-STNN not only excels in comparison with the purely data-driven model but also demonstrates a competitive edge over the FNO in reservoir simulation. Especially in strongly heterogeneous flow fields with fractures, the PI-STNN can still maintain high prediction accuracy. Building on this prediction accuracy, the PI-STNN model further offers a distinct advantage in efficiently performing uncertainty quantification, enabling rapid and comprehensive analysis of investment decisions in oil and gas development.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"1 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139015641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Accurately estimating the failure probability is crucial in designing civil infrastructure systems, such as floating offshore platforms for oil and gas processing/production, to ensure their safe operation throughout their service periods. However, as a system becomes complex, the evaluation of a limit state function may involve the use of an external computer solver, resulting in a significant computational burden to perform Monte Carlo simulations (MCS). Moreover, the high-dimensionality of the limit state function may limit efficient sampling of input variables due to the “curse of dimensionality.” To address these issues, an efficient machine learning framework is proposed, combining polynomial chaos expansion (PCE) and active subspace. This will enable the accurate and efficient evaluation of the failure probability of an offshore structure, which typically involves a large number of uncertain parameters. Unlike conventional PCE schemes that use the original random variable space or the auxiliary variable space for building a surrogate model, the proposed method utilizes a reduced-dimension space to circumvent the “curse of dimensionality.” An appropriate coordinate transformation is first sought so that most of the variability of a limit state function can be accounted for. Next, a PCE surrogate limit state function is constructed on the derived low-dimensional “active subspace.” The Gram-Schmidt orthogonalization process is used for making basis polynomial functions, which is particularly effective when input random parameters do not follow the Askey scheme and/or when a dependence structure between the input parameters exists. Therefore, a nonlinear iso-probabilistic transformation, which makes the convergence of a surrogate to the true model difficult, is not required, unlike traditional PCE. Numerical examples, including limit state functions related to structural dynamics problems, are presented to illustrate the advantages of the proposed method in estimating failure probabilities for complex structural systems. Specifically, the method exhibits significantly improved efficiency in estimating the failure probability of an offshore floating structure without compromising accuracy as compared to traditional PCE and MCS.
{"title":"Failure Probability Prediction for Offshore Floating Structures Using Machine Learning","authors":"H. Lim","doi":"10.2118/218408-pa","DOIUrl":"https://doi.org/10.2118/218408-pa","url":null,"abstract":"\u0000 Accurately estimating the failure probability is crucial in designing civil infrastructure systems, such as floating offshore platforms for oil and gas processing/production, to ensure their safe operation throughout their service periods. However, as a system becomes complex, the evaluation of a limit state function may involve the use of an external computer solver, resulting in a significant computational burden to perform Monte Carlo simulations (MCS). Moreover, the high-dimensionality of the limit state function may limit efficient sampling of input variables due to the “curse of dimensionality.” To address these issues, an efficient machine learning framework is proposed, combining polynomial chaos expansion (PCE) and active subspace. This will enable the accurate and efficient evaluation of the failure probability of an offshore structure, which typically involves a large number of uncertain parameters. Unlike conventional PCE schemes that use the original random variable space or the auxiliary variable space for building a surrogate model, the proposed method utilizes a reduced-dimension space to circumvent the “curse of dimensionality.” An appropriate coordinate transformation is first sought so that most of the variability of a limit state function can be accounted for. Next, a PCE surrogate limit state function is constructed on the derived low-dimensional “active subspace.” The Gram-Schmidt orthogonalization process is used for making basis polynomial functions, which is particularly effective when input random parameters do not follow the Askey scheme and/or when a dependence structure between the input parameters exists. Therefore, a nonlinear iso-probabilistic transformation, which makes the convergence of a surrogate to the true model difficult, is not required, unlike traditional PCE. Numerical examples, including limit state functions related to structural dynamics problems, are presented to illustrate the advantages of the proposed method in estimating failure probabilities for complex structural systems. Specifically, the method exhibits significantly improved efficiency in estimating the failure probability of an offshore floating structure without compromising accuracy as compared to traditional PCE and MCS.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"208 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139026641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Yipu Liang, Sen Wang, Qihong Feng, Mengqi Zhang, Xiaopeng Cao, Xiukun Wang
Accurate characterization of shale pore structures is of paramount importance in elucidating the distribution and migration mechanisms of fluids within shale rocks. However, the acquisition of high-resolution (HR) images of shale rocks is limited by the precision of the scanning equipment. Even with higher-precision devices, compromising the image field of view becomes inevitable, making it challenging to faithfully represent the actual conditions of shale. We propose a stepwise 3D super-resolution (SR) reconstruction method for shale digital rocks based on the widely used focused-ion-beam scanning electron microscope (FIB-SEM) technique. This method effectively addresses the issues of inconsistent horizontal and vertical resolutions as well as low 3D image resolution in FIB-SEM images. By adopting this approach, we significantly enhance image details and clarity, enabling successful observations of pores smaller than 10 nm within shale and laying a foundation for further pore-scale flow simulations. Furthermore, we extract the pore network model (PNM) from the SR reconstructed digital rock to analyze the pore size distribution, coordination number, and pore-throat ratio of shale samples from the Jiyang Depression. The results demonstrate a pore radius distribution in the range of 0 nm to 40 nm, which aligns with the results from nitrogen adsorption experiments. Notably, pores with radii smaller than 10 nm account for 50% of the total connected pores. The proportion of isolated pores in the SR reconstructed shale PNM is significantly reduced, with the coordination number mainly distributed between 1 and 4. The pore-throat ratio of shale ranges from 1 to 3, indicating a relatively uniform development of pores and throats. This study introduces a novel method for accurately characterizing the shale pore structure, which aids researchers in evaluating the pore size distribution and connectivity of shales.
{"title":"Ultrahigh-Resolution Reconstruction of Shale Digital Rocks from FIB-SEM Images Using Deep Learning","authors":"Yipu Liang, Sen Wang, Qihong Feng, Mengqi Zhang, Xiaopeng Cao, Xiukun Wang","doi":"10.2118/218397-pa","DOIUrl":"https://doi.org/10.2118/218397-pa","url":null,"abstract":"\u0000 Accurate characterization of shale pore structures is of paramount importance in elucidating the distribution and migration mechanisms of fluids within shale rocks. However, the acquisition of high-resolution (HR) images of shale rocks is limited by the precision of the scanning equipment. Even with higher-precision devices, compromising the image field of view becomes inevitable, making it challenging to faithfully represent the actual conditions of shale. We propose a stepwise 3D super-resolution (SR) reconstruction method for shale digital rocks based on the widely used focused-ion-beam scanning electron microscope (FIB-SEM) technique. This method effectively addresses the issues of inconsistent horizontal and vertical resolutions as well as low 3D image resolution in FIB-SEM images. By adopting this approach, we significantly enhance image details and clarity, enabling successful observations of pores smaller than 10 nm within shale and laying a foundation for further pore-scale flow simulations. Furthermore, we extract the pore network model (PNM) from the SR reconstructed digital rock to analyze the pore size distribution, coordination number, and pore-throat ratio of shale samples from the Jiyang Depression. The results demonstrate a pore radius distribution in the range of 0 nm to 40 nm, which aligns with the results from nitrogen adsorption experiments. Notably, pores with radii smaller than 10 nm account for 50% of the total connected pores. The proportion of isolated pores in the SR reconstructed shale PNM is significantly reduced, with the coordination number mainly distributed between 1 and 4. The pore-throat ratio of shale ranges from 1 to 3, indicating a relatively uniform development of pores and throats. This study introduces a novel method for accurately characterizing the shale pore structure, which aids researchers in evaluating the pore size distribution and connectivity of shales.","PeriodicalId":22252,"journal":{"name":"SPE Journal","volume":"206 ","pages":""},"PeriodicalIF":3.6,"publicationDate":"2023-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138992493","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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