Shuai Zhao, Wanfen Pu, Qingyuan Chen, C. Yuan, M. Varfolomeev
The in-situ combustion (ISC) technique has emerged as a significant approach for shale oil production. However, currently, there is a lack of experimental evidence supporting the stable propagation of combustion front within fractured shale. This study aimed to investigate the combustion characteristics within fractured shale by using a self-designed combustion tube (CT) and an experimental scheme. Subsequently, an analysis of shale structure and oil properties was conducted. The results demonstrated that while the combustion front could propagate through shale with a single fracture width of approximately 43 μm, the combustion intensity gradually diminished, leading to an inability to sustain stable propagation in the later part of the oil-detritus mixtures. The combustion intensity within the shale was enhanced by preheating the shale at 250°C, resulting in an improved oil recovery from 67.8% to 77.9%. The findings indicated that the complex fractured shale allowed for the stable propagation of the combustion front without a significant decrease in combustion intensity. Moreover, the T2 spectrum analysis of shale revealed a gradual expansion of the pore-fracture structure and improved shale connectivity after combustion. The T1-T2 response illustrated the transformation of solid and heavy components into lighter components. Furthermore, the content of saturates and H in the oil increased after combustion, whereas there was a significant decrease in resins, O, and S. Overall, this study provided technical evidence supporting the feasibility of employing the ISC technique for the development of shale oil reservoirs with additional fractures.
原地燃烧（ISC）技术已成为页岩油生产的重要方法。然而，目前还缺乏实验证据支持燃烧前沿在断裂页岩中的稳定传播。本研究旨在利用自行设计的燃烧管（CT）和实验方案，研究断裂页岩内的燃烧特性。随后，对页岩结构和石油特性进行了分析。结果表明，虽然燃烧前沿可以在单个断裂宽度约为 43 μm 的页岩中传播，但燃烧强度逐渐减弱，导致在油-杂质混合物的后期无法持续稳定传播。通过在 250°C 下预热页岩，提高了页岩内的燃烧强度，从而将采油率从 67.8% 提高到 77.9%。研究结果表明，复杂断裂页岩可使燃烧前沿稳定传播，而燃烧强度不会显著降低。此外，页岩的 T2 频谱分析表明，燃烧后孔隙-断裂结构逐渐扩大，页岩的连通性得到改善。T1-T2 反应表明固体和重组分转变为轻组分。此外，燃烧后石油中饱和物和 H 的含量增加，而树脂、O 和 S 的含量则显著减少。总之，这项研究提供了技术证据，支持采用 ISC 技术开发具有额外裂缝的页岩油藏的可行性。
{"title":"Propagation of Combustion Front within Fractured Shale and Its Influence on Shale Structure and Crude Oil Properties: An Experimental Study","authors":"Shuai Zhao, Wanfen Pu, Qingyuan Chen, C. Yuan, M. Varfolomeev","doi":"10.2118/219456-pa","DOIUrl":"https://doi.org/10.2118/219456-pa","url":null,"abstract":"\u0000 The in-situ combustion (ISC) technique has emerged as a significant approach for shale oil production. However, currently, there is a lack of experimental evidence supporting the stable propagation of combustion front within fractured shale. This study aimed to investigate the combustion characteristics within fractured shale by using a self-designed combustion tube (CT) and an experimental scheme. Subsequently, an analysis of shale structure and oil properties was conducted. The results demonstrated that while the combustion front could propagate through shale with a single fracture width of approximately 43 μm, the combustion intensity gradually diminished, leading to an inability to sustain stable propagation in the later part of the oil-detritus mixtures. The combustion intensity within the shale was enhanced by preheating the shale at 250°C, resulting in an improved oil recovery from 67.8% to 77.9%. The findings indicated that the complex fractured shale allowed for the stable propagation of the combustion front without a significant decrease in combustion intensity. Moreover, the T2 spectrum analysis of shale revealed a gradual expansion of the pore-fracture structure and improved shale connectivity after combustion. The T1-T2 response illustrated the transformation of solid and heavy components into lighter components. Furthermore, the content of saturates and H in the oil increased after combustion, whereas there was a significant decrease in resins, O, and S. Overall, this study provided technical evidence supporting the feasibility of employing the ISC technique for the development of shale oil reservoirs with additional fractures.","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"119 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139821134","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}
Well logs comprise sequential data detailing the geological properties of formations at varying depths encountered during drilling. They are fundamental for various applications in the petroleum industry. However, acquired well logs often contain noise and missing data, which impedes their utility. To address this, numerous methods have been developed to impute missing components in well logs, ranging from traditional deterministic methods to modern data-driven models. Despite their effectiveness, these methods face several challenges. First, many are deterministic, lacking the ability to capture and represent the inherent uncertainties in the data. In addition, they often require complete logging data as input, which presents challenges in data sets with substantial missing data. Moreover, most are predictive models designed with specific targets that require retraining for different variables, which limits their versatility in handling data sets with diverse missing components. This work proposes the use of a generative model based on the conditional denoising diffusion probabilistic model (CDDPM) to impute missing components within well logs. The CDDPM offers several advantages. Its inherent probabilistic nature allows it to capture uncertainties in the data, providing predictions in the form of probability distributions rather than single-point estimates. This helps engineers make more robust and informed decisions in practice, thus mitigating potential risks. More importantly, due to its generative nature, the model is trained to learn the underlying data distribution, not the specific input-output map, which enables it to impute all missing data simultaneously. Through experiments on a real-world data set, we demonstrate that our proposed method surpasses conventional data-driven techniques in performance. Both qualitative and quantitative evaluations confirm the effectiveness of the model in imputing missing components. This research highlights the potential of modern deep generative models in petroleum engineering applications.
{"title":"A Missing Well-Logs Imputation Method Based on Conditional Denoising Diffusion Probabilistic Models","authors":"Han Meng, Botao Lin, Ruxin Zhang, Yan Jin","doi":"10.2118/219452-pa","DOIUrl":"https://doi.org/10.2118/219452-pa","url":null,"abstract":"\u0000 Well logs comprise sequential data detailing the geological properties of formations at varying depths encountered during drilling. They are fundamental for various applications in the petroleum industry. However, acquired well logs often contain noise and missing data, which impedes their utility. To address this, numerous methods have been developed to impute missing components in well logs, ranging from traditional deterministic methods to modern data-driven models. Despite their effectiveness, these methods face several challenges. First, many are deterministic, lacking the ability to capture and represent the inherent uncertainties in the data. In addition, they often require complete logging data as input, which presents challenges in data sets with substantial missing data. Moreover, most are predictive models designed with specific targets that require retraining for different variables, which limits their versatility in handling data sets with diverse missing components. This work proposes the use of a generative model based on the conditional denoising diffusion probabilistic model (CDDPM) to impute missing components within well logs. The CDDPM offers several advantages. Its inherent probabilistic nature allows it to capture uncertainties in the data, providing predictions in the form of probability distributions rather than single-point estimates. This helps engineers make more robust and informed decisions in practice, thus mitigating potential risks. More importantly, due to its generative nature, the model is trained to learn the underlying data distribution, not the specific input-output map, which enables it to impute all missing data simultaneously. Through experiments on a real-world data set, we demonstrate that our proposed method surpasses conventional data-driven techniques in performance. Both qualitative and quantitative evaluations confirm the effectiveness of the model in imputing missing components. This research highlights the potential of modern deep generative models in petroleum engineering applications.","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"53 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139828478","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}
Jie Zheng, Zhihao Hu, Weixiao Wang, Yihua Dou, Jiahui Li, Xu Yang, Yarong Zhang, Yinping Cao
To solve problems such as additional tubing/casing load, casing deformation, and packer failure caused by changes in annular temperature during oil and gas reservoir fracturing and production, based on the well structure of oil and gas reservoirs and transition transient heat transfer mechanism, a four-field coupling simulation model of the temperature field in the main fluid domain of the tubing, the temperature field in the solid domain of the tubing, the temperature field in the annular fluid domain, and the temperature field in the solid domain of the casing is proposed. Considering the coupling of fluid temperature, pressure, and physical parameters, boundary conditions are established based on reservoir characteristics, wellbore heat transfer characteristics, and fracturing and production conditions, and are compiled into Fluent software for simulation through the user-defined function (UDF) method. The effects of the temperature and flow rate of injected fracturing fluid and produced oil and gas on the distribution of the wellbore temperature field and temperature gradient are studied. The research results show that by applying D14-1 and D5-5 gas wells to the model, the simulated temperature is in good agreement with the measured wellbore temperature, and the maximum errors of the simulated values of the two different wells are 6.4% and 4.3%, respectively. As the injection and production operation time increase, the heat transfer between the wellbore and the formation gradually stabilizes. At this time, the injection and production flow rate have little impact on the wellbore temperature field, while the injection and production temperature have a greater impact on the wellbore temperature field. The injection and production temperature will cause changes in annular temperature and temperature gradient, leading to an increase or decrease in pressure within a limited annular volume, resulting in local stress on the tubing and casing. The research results can provide a theoretical basis for the analysis of the temperature field and pressure field of the wellbore during fracturing and oil and gas production, ensuring the safety and stability of fracturing and production.
{"title":"Computational Fluid Dynamics Modeling and Analysis of Axial and Radial Temperature of Wellbore during Injection and Production Process","authors":"Jie Zheng, Zhihao Hu, Weixiao Wang, Yihua Dou, Jiahui Li, Xu Yang, Yarong Zhang, Yinping Cao","doi":"10.2118/219467-pa","DOIUrl":"https://doi.org/10.2118/219467-pa","url":null,"abstract":"\u0000 To solve problems such as additional tubing/casing load, casing deformation, and packer failure caused by changes in annular temperature during oil and gas reservoir fracturing and production, based on the well structure of oil and gas reservoirs and transition transient heat transfer mechanism, a four-field coupling simulation model of the temperature field in the main fluid domain of the tubing, the temperature field in the solid domain of the tubing, the temperature field in the annular fluid domain, and the temperature field in the solid domain of the casing is proposed. Considering the coupling of fluid temperature, pressure, and physical parameters, boundary conditions are established based on reservoir characteristics, wellbore heat transfer characteristics, and fracturing and production conditions, and are compiled into Fluent software for simulation through the user-defined function (UDF) method. The effects of the temperature and flow rate of injected fracturing fluid and produced oil and gas on the distribution of the wellbore temperature field and temperature gradient are studied. The research results show that by applying D14-1 and D5-5 gas wells to the model, the simulated temperature is in good agreement with the measured wellbore temperature, and the maximum errors of the simulated values of the two different wells are 6.4% and 4.3%, respectively. As the injection and production operation time increase, the heat transfer between the wellbore and the formation gradually stabilizes. At this time, the injection and production flow rate have little impact on the wellbore temperature field, while the injection and production temperature have a greater impact on the wellbore temperature field. The injection and production temperature will cause changes in annular temperature and temperature gradient, leading to an increase or decrease in pressure within a limited annular volume, resulting in local stress on the tubing and casing. The research results can provide a theoretical basis for the analysis of the temperature field and pressure field of the wellbore during fracturing and oil and gas production, ensuring the safety and stability of fracturing and production.","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"1017 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139831328","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}
Ramanzani Kalule, Hamid A. Abderrahmane, Shehzad Ahmed, W. Alameri, Mohamed Sassi
This paper deals with a mathematical modeling and optimization-based approach for estimating relative permeability and capillary pressure from average water saturation data collected during unsteady-state waterflooding experiments. Assuming the Lomeland-Ebeltoft-Thomas (LET) model for the variation of the relative permeability with saturation, the appropriate governing equations, boundary, and initial conditions were solved within the Pyomo framework. Using interior point optimization (IPOPT) with a least-squares objective function, the six parameters of the LET model that ensure the history matching between the measured and calculated average saturation are determined. Additionally, we inferred the capillary pressure function and performed a Sobol sensitivity analysis on the LET model parameters. The results showcase the reliability and robustness of our proposed approach, as it estimates the crucial parameters driving the variation of oil-water flow relative permeability across several cases and effectively predicts the capillary pressure trend. The proposed approach can be seen as an alternative to experimental and numerical simulation-based techniques for predicting relative permeability and capillary pressure curves.
本文介绍了一种基于数学建模和优化的方法，用于从非稳态注水实验中收集的平均含水饱和度数据估算相对渗透率和毛细管压力。假设相对渗透率随饱和度变化的模型为 Lomeland-Ebeltoft-Thomas（LET）模型，在 Pyomo 框架内求解了相应的控制方程、边界和初始条件。利用具有最小二乘目标函数的内部点优化（IPOPT），确定了 LET 模型的六个参数，以确保测量和计算的平均饱和度之间的历史匹配。此外，我们还推断了毛细管压力函数，并对 LET 模型参数进行了 Sobol 敏感性分析。结果表明，我们提出的方法既可靠又稳健，因为它估算出了几种情况下油水流动相对渗透率变化的关键参数，并有效地预测了毛细管压力的变化趋势。在预测相对渗透率和毛细管压力曲线时，我们提出的方法可以替代基于实验和数值模拟的技术。
{"title":"Dynamic Optimization for Petrophysical Property Estimation in Unsteady-State Coreflooding Using Pyomo","authors":"Ramanzani Kalule, Hamid A. Abderrahmane, Shehzad Ahmed, W. Alameri, Mohamed Sassi","doi":"10.2118/219450-pa","DOIUrl":"https://doi.org/10.2118/219450-pa","url":null,"abstract":"\u0000 This paper deals with a mathematical modeling and optimization-based approach for estimating relative permeability and capillary pressure from average water saturation data collected during unsteady-state waterflooding experiments. Assuming the Lomeland-Ebeltoft-Thomas (LET) model for the variation of the relative permeability with saturation, the appropriate governing equations, boundary, and initial conditions were solved within the Pyomo framework. Using interior point optimization (IPOPT) with a least-squares objective function, the six parameters of the LET model that ensure the history matching between the measured and calculated average saturation are determined. Additionally, we inferred the capillary pressure function and performed a Sobol sensitivity analysis on the LET model parameters. The results showcase the reliability and robustness of our proposed approach, as it estimates the crucial parameters driving the variation of oil-water flow relative permeability across several cases and effectively predicts the capillary pressure trend. The proposed approach can be seen as an alternative to experimental and numerical simulation-based techniques for predicting relative permeability and capillary pressure curves.","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"568 ","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139832300","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}
Clement Afagwu, Saad Alafnan, Mohamed Mahmoud, Shabeeb Alajmei, S. Patil
Shale and ultratight gas reservoirs are multiscale, containing organic matter (OM) and inorganic minerals in multiple pore compartments of different pore shapes and scales. Selecting a suitable model to describe the multiscale transport mechanisms requires a minimum understanding of the inherent pore shape, OM content, typical pore size, and inherent flow regime. Interestingly, during gas production and associated pressure depletion, some mechanisms, such as pore compressibility, pore diffusion, and diffusion of sorbed gas molecules, become significant at lower pressure. In this study, multiscale and multiphysics permeability models are introduced that couple the effects of poroelasticity (especially in slit-shaped pores with <1.0 aspect ratio) and sorbed gas diffusion, Fick diffusion, transition diffusion, or Knudsen diffusion, depending on the pore structural properties at multiscale for shale and ultratight gas applications. Shale here refers to organic-rich low-permeability rock with >1–2 wt% OM, while ultratight gas has negligible organic content with <1.0 wt%. These experimentally and computationally validated models could be combined with Gaussian pressure transient solutions to effectively understand the uncertainty in multiphysics gas permeability in addition to the hydraulic and natural fracture parameters for large-scale flow simulation of hydraulically fractured unconventional reservoirs.
{"title":"Permeability Modeling of Pore Shapes, Compaction, Sorption, and Molecular Diffusivity in Unconventional Reservoirs","authors":"Clement Afagwu, Saad Alafnan, Mohamed Mahmoud, Shabeeb Alajmei, S. Patil","doi":"10.2118/219460-pa","DOIUrl":"https://doi.org/10.2118/219460-pa","url":null,"abstract":"\u0000 Shale and ultratight gas reservoirs are multiscale, containing organic matter (OM) and inorganic minerals in multiple pore compartments of different pore shapes and scales. Selecting a suitable model to describe the multiscale transport mechanisms requires a minimum understanding of the inherent pore shape, OM content, typical pore size, and inherent flow regime. Interestingly, during gas production and associated pressure depletion, some mechanisms, such as pore compressibility, pore diffusion, and diffusion of sorbed gas molecules, become significant at lower pressure. In this study, multiscale and multiphysics permeability models are introduced that couple the effects of poroelasticity (especially in slit-shaped pores with <1.0 aspect ratio) and sorbed gas diffusion, Fick diffusion, transition diffusion, or Knudsen diffusion, depending on the pore structural properties at multiscale for shale and ultratight gas applications. Shale here refers to organic-rich low-permeability rock with >1–2 wt% OM, while ultratight gas has negligible organic content with <1.0 wt%. These experimentally and computationally validated models could be combined with Gaussian pressure transient solutions to effectively understand the uncertainty in multiphysics gas permeability in addition to the hydraulic and natural fracture parameters for large-scale flow simulation of hydraulically fractured unconventional reservoirs.","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"19 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139814373","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 introduces an approach for the impact of skin factor on the decline curve analysis of hydraulically fractured reservoirs. The objective is to consider this impact in the production forecasting and the ultimate recovery estimation. The approach focuses on reducing the uncertainties that could be raised from this impact on the production history and increasing the accuracy of the predicted flow rates. It proposes an easy and promising tool for the decline curve analysis that could be applied confidently to different oil- and gas-producing wells and different reservoirs. This approach utilizes the rate-normalized flow rate derivative β behavior of the fractured reservoirs. This derivative demonstrates a constant behavior with time for each flow regime when the production history has not undergone the impact of the skin factor. However, the constant behavior no longer exists when this impact has influenced the production history. Instead, a power-law type model governs the relationship between the flow rate derivative and production time. New analytical flow rate decline curve models, exponential-type, are derived from the flow rate derivative power-law type models for the flow regimes. Different models for calculating the skin factor are developed for the three linear flow regimes that could be observed during the transient state flow conditions. The proposed flow rate models are used to simulate the production history and forecast the future performance. Moreover, the hydraulic fracture conductivity can be calculated using these models as well as the flow rate loss caused by skin factor. Several case studies are examined by the proposed approach where the production history is used to characterize the dominant flow regimes. The study has reached several observations and conclusions. The impact of skin factor is seen clearly throughout transient state flow regimes; however, this impact declines sharply before reaching pseudosteady-state flow (boundary-dominated flow regime). The impact of the skin factor alternates the constant behavior of the flow rate derivative with time to a power-law type relationship. A straight line of a slope (0.5) is diagnosed during hydraulic fracture and formation linear flow regime on the log-log plot of the flow rate derivative β and time, while the bilinear flow regime demonstrates a straight line of a slope (0.25). Because of the skin factor, exponential decline curve models replace the power-law type models of the flow rate during the abovementioned flow regimes. These models exhibit an excellent match between the calculated flow rate and the production history. The maximum flow rate loss occurs during very early production time even though the skin factor during this time is less than the intermediate production time. This study presents a novel approach for the decline curve analysis taking into account the impact of skin factor. The novelty is represented by considering the flow regimes in the prod
{"title":"Skin Factor Consideration in Decline Curve Analysis","authors":"S. Al-Rbeawi","doi":"10.2118/219451-pa","DOIUrl":"https://doi.org/10.2118/219451-pa","url":null,"abstract":"\u0000 This paper introduces an approach for the impact of skin factor on the decline curve analysis of hydraulically fractured reservoirs. The objective is to consider this impact in the production forecasting and the ultimate recovery estimation. The approach focuses on reducing the uncertainties that could be raised from this impact on the production history and increasing the accuracy of the predicted flow rates. It proposes an easy and promising tool for the decline curve analysis that could be applied confidently to different oil- and gas-producing wells and different reservoirs.\u0000 This approach utilizes the rate-normalized flow rate derivative β behavior of the fractured reservoirs. This derivative demonstrates a constant behavior with time for each flow regime when the production history has not undergone the impact of the skin factor. However, the constant behavior no longer exists when this impact has influenced the production history. Instead, a power-law type model governs the relationship between the flow rate derivative and production time. New analytical flow rate decline curve models, exponential-type, are derived from the flow rate derivative power-law type models for the flow regimes. Different models for calculating the skin factor are developed for the three linear flow regimes that could be observed during the transient state flow conditions. The proposed flow rate models are used to simulate the production history and forecast the future performance. Moreover, the hydraulic fracture conductivity can be calculated using these models as well as the flow rate loss caused by skin factor. Several case studies are examined by the proposed approach where the production history is used to characterize the dominant flow regimes.\u0000 The study has reached several observations and conclusions. The impact of skin factor is seen clearly throughout transient state flow regimes; however, this impact declines sharply before reaching pseudosteady-state flow (boundary-dominated flow regime). The impact of the skin factor alternates the constant behavior of the flow rate derivative with time to a power-law type relationship. A straight line of a slope (0.5) is diagnosed during hydraulic fracture and formation linear flow regime on the log-log plot of the flow rate derivative β and time, while the bilinear flow regime demonstrates a straight line of a slope (0.25). Because of the skin factor, exponential decline curve models replace the power-law type models of the flow rate during the abovementioned flow regimes. These models exhibit an excellent match between the calculated flow rate and the production history. The maximum flow rate loss occurs during very early production time even though the skin factor during this time is less than the intermediate production time.\u0000 This study presents a novel approach for the decline curve analysis taking into account the impact of skin factor. The novelty is represented by considering the flow regimes in the prod","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"24 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139828549","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}
Well logs comprise sequential data detailing the geological properties of formations at varying depths encountered during drilling. They are fundamental for various applications in the petroleum industry. However, acquired well logs often contain noise and missing data, which impedes their utility. To address this, numerous methods have been developed to impute missing components in well logs, ranging from traditional deterministic methods to modern data-driven models. Despite their effectiveness, these methods face several challenges. First, many are deterministic, lacking the ability to capture and represent the inherent uncertainties in the data. In addition, they often require complete logging data as input, which presents challenges in data sets with substantial missing data. Moreover, most are predictive models designed with specific targets that require retraining for different variables, which limits their versatility in handling data sets with diverse missing components. This work proposes the use of a generative model based on the conditional denoising diffusion probabilistic model (CDDPM) to impute missing components within well logs. The CDDPM offers several advantages. Its inherent probabilistic nature allows it to capture uncertainties in the data, providing predictions in the form of probability distributions rather than single-point estimates. This helps engineers make more robust and informed decisions in practice, thus mitigating potential risks. More importantly, due to its generative nature, the model is trained to learn the underlying data distribution, not the specific input-output map, which enables it to impute all missing data simultaneously. Through experiments on a real-world data set, we demonstrate that our proposed method surpasses conventional data-driven techniques in performance. Both qualitative and quantitative evaluations confirm the effectiveness of the model in imputing missing components. This research highlights the potential of modern deep generative models in petroleum engineering applications.
{"title":"A Missing Well-Logs Imputation Method Based on Conditional Denoising Diffusion Probabilistic Models","authors":"Han Meng, Botao Lin, Ruxin Zhang, Yan Jin","doi":"10.2118/219452-pa","DOIUrl":"https://doi.org/10.2118/219452-pa","url":null,"abstract":"\u0000 Well logs comprise sequential data detailing the geological properties of formations at varying depths encountered during drilling. They are fundamental for various applications in the petroleum industry. However, acquired well logs often contain noise and missing data, which impedes their utility. To address this, numerous methods have been developed to impute missing components in well logs, ranging from traditional deterministic methods to modern data-driven models. Despite their effectiveness, these methods face several challenges. First, many are deterministic, lacking the ability to capture and represent the inherent uncertainties in the data. In addition, they often require complete logging data as input, which presents challenges in data sets with substantial missing data. Moreover, most are predictive models designed with specific targets that require retraining for different variables, which limits their versatility in handling data sets with diverse missing components. This work proposes the use of a generative model based on the conditional denoising diffusion probabilistic model (CDDPM) to impute missing components within well logs. The CDDPM offers several advantages. Its inherent probabilistic nature allows it to capture uncertainties in the data, providing predictions in the form of probability distributions rather than single-point estimates. This helps engineers make more robust and informed decisions in practice, thus mitigating potential risks. More importantly, due to its generative nature, the model is trained to learn the underlying data distribution, not the specific input-output map, which enables it to impute all missing data simultaneously. Through experiments on a real-world data set, we demonstrate that our proposed method surpasses conventional data-driven techniques in performance. Both qualitative and quantitative evaluations confirm the effectiveness of the model in imputing missing components. This research highlights the potential of modern deep generative models in petroleum engineering applications.","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"2 4","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139888467","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}
Ramanzani Kalule, Hamid A. Abderrahmane, Shehzad Ahmed, W. Alameri, Mohamed Sassi
This paper deals with a mathematical modeling and optimization-based approach for estimating relative permeability and capillary pressure from average water saturation data collected during unsteady-state waterflooding experiments. Assuming the Lomeland-Ebeltoft-Thomas (LET) model for the variation of the relative permeability with saturation, the appropriate governing equations, boundary, and initial conditions were solved within the Pyomo framework. Using interior point optimization (IPOPT) with a least-squares objective function, the six parameters of the LET model that ensure the history matching between the measured and calculated average saturation are determined. Additionally, we inferred the capillary pressure function and performed a Sobol sensitivity analysis on the LET model parameters. The results showcase the reliability and robustness of our proposed approach, as it estimates the crucial parameters driving the variation of oil-water flow relative permeability across several cases and effectively predicts the capillary pressure trend. The proposed approach can be seen as an alternative to experimental and numerical simulation-based techniques for predicting relative permeability and capillary pressure curves.
本文介绍了一种基于数学建模和优化的方法，用于从非稳态注水实验中收集的平均含水饱和度数据估算相对渗透率和毛细管压力。假设相对渗透率随饱和度变化的模型为 Lomeland-Ebeltoft-Thomas（LET）模型，在 Pyomo 框架内求解了相应的控制方程、边界和初始条件。利用具有最小二乘目标函数的内部点优化（IPOPT），确定了 LET 模型的六个参数，以确保测量和计算的平均饱和度之间的历史匹配。此外，我们还推断了毛细管压力函数，并对 LET 模型参数进行了 Sobol 敏感性分析。结果表明，我们提出的方法既可靠又稳健，因为它估算出了几种情况下油水流动相对渗透率变化的关键参数，并有效地预测了毛细管压力的变化趋势。在预测相对渗透率和毛细管压力曲线时，我们提出的方法可以替代基于实验和数值模拟的技术。
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How to effectively control the recoil response after an emergency disconnection is one of the core technical problems in ensuring the safe and reliable operation of a deepwater drilling riser. Currently, the theoretical analysis is based on a discretization model or numerical simulation, which ignores the continuity of the riser system and the coupling effects of load acting on the riser. To address this problem further, in this paper, we establish a mechanical model and control equation with infinite degree of freedom for riser recoil response, where the heave motion of the floating drilling platform, seawater damping, and the viscous resistance of drilling fluid discharge were taken into account. In addition, the correctness of the model and solving approach are verified against the Orcaflex software. On this basis, the influence of wave period, wave height, initial phase angle, and tension coefficient on the recoil characteristics are discussed. The success of riser emergency disconnection is related to the clearance between the lower marine riser package (LMRP) and the blowout preventer (BOP) and the axial force distribution of the riser. The influence of the above-mentioned factors on the riser recoil response is also complicated. On the basis of the assumptions put forward and the model established, some quantitative conclusions are drawn. This study is of reference significance for safety control of riser emergency disconnection operation.
{"title":"Application of Mode Superposition Method in the Recoil Response of Deepwater Drilling Risers after Emergency Disconnection","authors":"Yanbin Wang, Tian Luan, Deli Gao, Rui Li","doi":"10.2118/219455-pa","DOIUrl":"https://doi.org/10.2118/219455-pa","url":null,"abstract":"\u0000 How to effectively control the recoil response after an emergency disconnection is one of the core technical problems in ensuring the safe and reliable operation of a deepwater drilling riser. Currently, the theoretical analysis is based on a discretization model or numerical simulation, which ignores the continuity of the riser system and the coupling effects of load acting on the riser. To address this problem further, in this paper, we establish a mechanical model and control equation with infinite degree of freedom for riser recoil response, where the heave motion of the floating drilling platform, seawater damping, and the viscous resistance of drilling fluid discharge were taken into account. In addition, the correctness of the model and solving approach are verified against the Orcaflex software. On this basis, the influence of wave period, wave height, initial phase angle, and tension coefficient on the recoil characteristics are discussed. The success of riser emergency disconnection is related to the clearance between the lower marine riser package (LMRP) and the blowout preventer (BOP) and the axial force distribution of the riser. The influence of the above-mentioned factors on the riser recoil response is also complicated. On the basis of the assumptions put forward and the model established, some quantitative conclusions are drawn. This study is of reference significance for safety control of riser emergency disconnection operation.","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"42 10","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139873320","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. Baig, Sulaiman A. Alarifi, Mohamed Mahmoud, M. Kamal, Mobeen Murtaza, Manar M. AlAhmari, Abdulmohsen Alhumam
Sand production is one of the major problems that can occur in an oil or gas well. Enzyme-induced carbonate precipitation (EICP) methods have recently emerged as possible environment-friendly solutions for enhancing loose sand consolidation and preventing it from being produced with the fluids to the surface. This work explores increasing the consolidated sand strength and its treatment procedure using a modified EICP. The study also examines the characterization of precipitation generated by microorganisms using a computed tomography (CT) scan. To consolidate the sand specimen, nine different solutions were prepared. The solutions were a mixture of urea, urease, CaCl2, MgCl2, and xanthan gum in varying quantities. X-ray diffraction (XRD) analysis was conducted to determine the type of calcium carbonate (or CaCO3) polymorph. The morphology of calcium carbonate precipitation in the sand sample was visualized through scanning electron microscopy (SEM) imaging. The strength of consolidated samples was determined by the scratch test. The baseline EICP solution was exposed to different curing temperatures, namely, 25°C, 70°C, and 90°C. Out of these temperatures, the sample cured at 70°C showed the maximum strength, while the ones cured at 25°C demonstrated the weakest strength. This outcome emphasizes how crucial temperature control is in determining the strength development of the samples. The results highlight the importance of evaluating how varying curing temperatures affect specimen performance as well as emphasizing the need for accurate temperature control during experimental setups. Interestingly, samples made with a combination of CaCl2 and MgCl2 salts exhibited more strength when compared with EICP solutions formulated with only one type of salt. The consolidated sample that was prepared with xanthan gum with a concentration of 3 g/L showed high strength at 70°C. Notably, this technique offers a cost-effective solution compared with other methods developed to address sand production-related failures in production equipment. Furthermore, CT scans prove to be a valuable tool for investigating the characterization of microbially induced precipitation, including calcite, dolomite, and other minerals. This research underscores the professional approach in evaluating the efficacy of xanthan gum and CT scans in the context of EICP applications.
{"title":"Experimental Investigation of a Modified Enzyme-Induced Carbonate Precipitation Solution for Sand Production Control Applications","authors":"A. Baig, Sulaiman A. Alarifi, Mohamed Mahmoud, M. Kamal, Mobeen Murtaza, Manar M. AlAhmari, Abdulmohsen Alhumam","doi":"10.2118/219447-pa","DOIUrl":"https://doi.org/10.2118/219447-pa","url":null,"abstract":"\u0000 Sand production is one of the major problems that can occur in an oil or gas well. Enzyme-induced carbonate precipitation (EICP) methods have recently emerged as possible environment-friendly solutions for enhancing loose sand consolidation and preventing it from being produced with the fluids to the surface. This work explores increasing the consolidated sand strength and its treatment procedure using a modified EICP. The study also examines the characterization of precipitation generated by microorganisms using a computed tomography (CT) scan. To consolidate the sand specimen, nine different solutions were prepared. The solutions were a mixture of urea, urease, CaCl2, MgCl2, and xanthan gum in varying quantities. X-ray diffraction (XRD) analysis was conducted to determine the type of calcium carbonate (or CaCO3) polymorph. The morphology of calcium carbonate precipitation in the sand sample was visualized through scanning electron microscopy (SEM) imaging. The strength of consolidated samples was determined by the scratch test. The baseline EICP solution was exposed to different curing temperatures, namely, 25°C, 70°C, and 90°C. Out of these temperatures, the sample cured at 70°C showed the maximum strength, while the ones cured at 25°C demonstrated the weakest strength. This outcome emphasizes how crucial temperature control is in determining the strength development of the samples. The results highlight the importance of evaluating how varying curing temperatures affect specimen performance as well as emphasizing the need for accurate temperature control during experimental setups. Interestingly, samples made with a combination of CaCl2 and MgCl2 salts exhibited more strength when compared with EICP solutions formulated with only one type of salt. The consolidated sample that was prepared with xanthan gum with a concentration of 3 g/L showed high strength at 70°C. Notably, this technique offers a cost-effective solution compared with other methods developed to address sand production-related failures in production equipment. Furthermore, CT scans prove to be a valuable tool for investigating the characterization of microbially induced precipitation, including calcite, dolomite, and other minerals. This research underscores the professional approach in evaluating the efficacy of xanthan gum and CT scans in the context of EICP applications.","PeriodicalId":510854,"journal":{"name":"SPE Journal","volume":"25 5","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139881720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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