Modeling fluid flow in fractured reservoirs requires an accurate evaluation of the hydraulic properties of discrete fractures. Full Navier-Stokes simulations provide an accurate approximation of the flow within fractures, including fracture upscaling. However, its excessive computational cost makes it impractical. The traditionally used cubic law (CL) is known to overshoot the fracture hydraulic properties significantly. In this work, we propose an alternative method based on the cubic law. We first develop geometric rules based on the fracture topography data, by which we subdivide the fracture into segments and local cells. We then modify the aperture field by incorporating the effects of flow direction, flow tortuosity, normal aperture, and local roughness. The approach is applicable for fractures in 2D and 3D spaces. This paper presented almost all existing CL-based models in the literature, which include more than twenty models. We benchmarked all these models, including our proposed model, for thousands of fracture cases. High-resolution simulations solving the full-physics Navier-Stokes (NS) equations were used to compute the reference solutions. We highlight the behavior of accuracy and limitations of all tested models as a function of fracture geometric characteristics, such as roughness. The obtained accuracy of the proposed model showed the highest for more than 2000 fracture cases with a wide range of tortuosity, roughness, and mechanical aperture variations. None of the existing methods in the literature provide this level of accuracy and applicability. The proposed model retains the simplicity and efficiency of the cubic law and can be easily implemented in workflows for reservoir characterization and modeling.
{"title":"An Accurate Cubic Law for the Upscaling of Discrete Natural Fractures","authors":"Xupeng He, M. AlSinan, H. Kwak, H. Hoteit","doi":"10.2118/204906-ms","DOIUrl":"https://doi.org/10.2118/204906-ms","url":null,"abstract":"\u0000 Modeling fluid flow in fractured reservoirs requires an accurate evaluation of the hydraulic properties of discrete fractures. Full Navier-Stokes simulations provide an accurate approximation of the flow within fractures, including fracture upscaling. However, its excessive computational cost makes it impractical. The traditionally used cubic law (CL) is known to overshoot the fracture hydraulic properties significantly. In this work, we propose an alternative method based on the cubic law. We first develop geometric rules based on the fracture topography data, by which we subdivide the fracture into segments and local cells. We then modify the aperture field by incorporating the effects of flow direction, flow tortuosity, normal aperture, and local roughness. The approach is applicable for fractures in 2D and 3D spaces. This paper presented almost all existing CL-based models in the literature, which include more than twenty models. We benchmarked all these models, including our proposed model, for thousands of fracture cases. High-resolution simulations solving the full-physics Navier-Stokes (NS) equations were used to compute the reference solutions. We highlight the behavior of accuracy and limitations of all tested models as a function of fracture geometric characteristics, such as roughness. The obtained accuracy of the proposed model showed the highest for more than 2000 fracture cases with a wide range of tortuosity, roughness, and mechanical aperture variations. None of the existing methods in the literature provide this level of accuracy and applicability. The proposed model retains the simplicity and efficiency of the cubic law and can be easily implemented in workflows for reservoir characterization and modeling.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81878226","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 examines the energy transition consequences on the oil and gas energy system chain as it propagates from net importing through the transit to the net exporting countries (or regions). The fundamental energy system security concerns of importing, transit, and exporting regions are analyzed under the low carbon energy transition dynamics. The analysis is evidence-based on diversification of energy sources, energy supply and demand evolution, and energy demand management development. The analysis results imply that the energy system is going through technological and logistical reallocation of primary energy. The manifestation of such reallocation includes an increase in electrification, the rise of energy carrier options, and clean technologies. Under healthy and normal global economic growth, the reallocation mentioned above would have a mild effect on curbing the oil and gas primary energy demands growth. A case study concerning electric vehicles, which is part of the energy transition aspect, is presented to assess its impact on the energy system, precisely on the fossil fuel demand. Results show that electric vehicles are indirectly fueled, mainly from fossil-fired power stations through electric grids. Moreover, oil byproducts use in the electric vehicle industry confirms the reallocation of the energy system components' roles. The paper's contribution to the literature is the portrayal of the energy system security state under the low carbon energy transition. The significance of this representation is to shed light on the concerns of the net exporting, transit, and net importing regions under such evolution. Subsequently, it facilitates the development of measures toward mitigating world tensions and conflicts, enhancing the global socio-economic wellbeing, and preventing corruption.
{"title":"Primary Energy System Chain Security Under the Energy Transition","authors":"O. Alsayegh","doi":"10.2118/204893-ms","DOIUrl":"https://doi.org/10.2118/204893-ms","url":null,"abstract":"\u0000 This paper examines the energy transition consequences on the oil and gas energy system chain as it propagates from net importing through the transit to the net exporting countries (or regions). The fundamental energy system security concerns of importing, transit, and exporting regions are analyzed under the low carbon energy transition dynamics. The analysis is evidence-based on diversification of energy sources, energy supply and demand evolution, and energy demand management development. The analysis results imply that the energy system is going through technological and logistical reallocation of primary energy. The manifestation of such reallocation includes an increase in electrification, the rise of energy carrier options, and clean technologies. Under healthy and normal global economic growth, the reallocation mentioned above would have a mild effect on curbing the oil and gas primary energy demands growth. A case study concerning electric vehicles, which is part of the energy transition aspect, is presented to assess its impact on the energy system, precisely on the fossil fuel demand. Results show that electric vehicles are indirectly fueled, mainly from fossil-fired power stations through electric grids. Moreover, oil byproducts use in the electric vehicle industry confirms the reallocation of the energy system components' roles.\u0000 The paper's contribution to the literature is the portrayal of the energy system security state under the low carbon energy transition. The significance of this representation is to shed light on the concerns of the net exporting, transit, and net importing regions under such evolution. Subsequently, it facilitates the development of measures toward mitigating world tensions and conflicts, enhancing the global socio-economic wellbeing, and preventing corruption.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87087780","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}
Zhenmu Chen, T. Shaalan, Ghazi D Qahtani, Shahid Manzoor
Flow control devices (FCDs) like inflow control devices (ICDs) and interval control valves (ICVs) (i.e., equalizer) have increased applications in both conventional and unconventional resources. They have been used to mitigate water or gas coning problems for mature fields in conventional reservoirs, to alleviate premature water breakthrough in naturally fractured reservoirs, and to optimize the steam distribution in heavy oil reservoirs. There have been increased trend in using FCDs in the real field. Previously, complex well models have been implemented in a large-scale parallel reservoir simulator by Tareq et al. (2017). The implementation can simulate an intelligent field contains tens to hundreds of multilateral complex wells commonly referred in the literature as maximum reservoir contact (MRC) wells with mechanical components such as ICVs and ICDs. In this paper, a new framework to model controlling the FCDs in complex well applications will be presented. The implementation is integrated into a complex well model. It can be easily used to model the dynamical control of devices. Simulation studies using both sector model and field model have been conducted. A systematic full-field operation is used for device control applications of smart wells. Successful application of field level controls in smart wells has the benefit of the improved overall GOSP performance.
{"title":"Field-Scale Modeling of Smart Completion Tools for Optimum Recovery","authors":"Zhenmu Chen, T. Shaalan, Ghazi D Qahtani, Shahid Manzoor","doi":"10.2118/204807-ms","DOIUrl":"https://doi.org/10.2118/204807-ms","url":null,"abstract":"\u0000 Flow control devices (FCDs) like inflow control devices (ICDs) and interval control valves (ICVs) (i.e., equalizer) have increased applications in both conventional and unconventional resources. They have been used to mitigate water or gas coning problems for mature fields in conventional reservoirs, to alleviate premature water breakthrough in naturally fractured reservoirs, and to optimize the steam distribution in heavy oil reservoirs. There have been increased trend in using FCDs in the real field. Previously, complex well models have been implemented in a large-scale parallel reservoir simulator by Tareq et al. (2017). The implementation can simulate an intelligent field contains tens to hundreds of multilateral complex wells commonly referred in the literature as maximum reservoir contact (MRC) wells with mechanical components such as ICVs and ICDs.\u0000 In this paper, a new framework to model controlling the FCDs in complex well applications will be presented. The implementation is integrated into a complex well model. It can be easily used to model the dynamical control of devices. Simulation studies using both sector model and field model have been conducted. A systematic full-field operation is used for device control applications of smart wells. Successful application of field level controls in smart wells has the benefit of the improved overall GOSP performance.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"37 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76498411","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. Ghamdi, A. Isah, M. Elsayed, Kareem Garadi, A. Abdulraheem
Measurement of Special Core Analysis (SCAL) parameters is a costly and time-intensive process. Some of the disadvantages of the current techniques are that they are not performed in-situ, and can destroy the core plugs, e.g., mercury injection capillary pressure (MICP). The objective of this paper is to introduce and investigate the emerging techniques in measuring SCAL parameters using Nuclear Magnetic Resonance (NMR) and Artificial Intelligence (Al). The conventional methods for measuring SCAL parameters are well understood and are an industry standard. Yet, NMR and Al - which are revolutionizing the way petroleum engineers and scientists describe rock/fluid properties - have yet to be utilized to their full potential in reservoir description. In addition, integration of the two tools will open a greater opportunity in the field of reservoir description, where measurement of in-situ SCAL parameters could be achieved. This paper shows the results of NMR lab experiments and Al analytics for measuring capillary pressures and permeability. The data set was divided into 70% for training and 30% for validation. Artificial Neural Network (ANN) was used and the developed model compared well with the permeability and capillary pressure data measured from the conventional methods. Specifically, the model predicted permeability 10% error. Similarly, for the capillary pressures, the model was able to achieve an excellent match. This active research area of prediction of capillary pressure, permeability and other rock properties is a promising emerging technique that capitalizes on NMR/AI analytics. There is significant potential is being able to evaluate wettability in-situ. Core-plugs undergoing Amott-Harvey experiment with NMR measurements in the process can be used as a building block for an NMR/AI wettability determination technique. This potential aspect of NMR/AI analytics can have significant implications on field development and EOR projects The developed NMR-Al model is an excellent start to measure permeability and capillary pressure in-situ. This novel approach coupled with ongoing research for better handling of in-situ wettability measurement will provide the industry with enormous insight into the in-situ SCAL measurements which are currently considered as an elusive measurement with no robust logging technique to evaluate them in-situ.
{"title":"Emerging Techniques in Measuring Capillary Pressure and Permeability Using NMR and AI","authors":"A. Ghamdi, A. Isah, M. Elsayed, Kareem Garadi, A. Abdulraheem","doi":"10.2118/204633-ms","DOIUrl":"https://doi.org/10.2118/204633-ms","url":null,"abstract":"\u0000 Measurement of Special Core Analysis (SCAL) parameters is a costly and time-intensive process. Some of the disadvantages of the current techniques are that they are not performed in-situ, and can destroy the core plugs, e.g., mercury injection capillary pressure (MICP). The objective of this paper is to introduce and investigate the emerging techniques in measuring SCAL parameters using Nuclear Magnetic Resonance (NMR) and Artificial Intelligence (Al).\u0000 The conventional methods for measuring SCAL parameters are well understood and are an industry standard. Yet, NMR and Al - which are revolutionizing the way petroleum engineers and scientists describe rock/fluid properties - have yet to be utilized to their full potential in reservoir description. In addition, integration of the two tools will open a greater opportunity in the field of reservoir description, where measurement of in-situ SCAL parameters could be achieved. This paper shows the results of NMR lab experiments and Al analytics for measuring capillary pressures and permeability.\u0000 The data set was divided into 70% for training and 30% for validation. Artificial Neural Network (ANN) was used and the developed model compared well with the permeability and capillary pressure data measured from the conventional methods. Specifically, the model predicted permeability 10% error. Similarly, for the capillary pressures, the model was able to achieve an excellent match. This active research area of prediction of capillary pressure, permeability and other rock properties is a promising emerging technique that capitalizes on NMR/AI analytics. There is significant potential is being able to evaluate wettability in-situ. Core-plugs undergoing Amott-Harvey experiment with NMR measurements in the process can be used as a building block for an NMR/AI wettability determination technique. This potential aspect of NMR/AI analytics can have significant implications on field development and EOR projects\u0000 The developed NMR-Al model is an excellent start to measure permeability and capillary pressure in-situ. This novel approach coupled with ongoing research for better handling of in-situ wettability measurement will provide the industry with enormous insight into the in-situ SCAL measurements which are currently considered as an elusive measurement with no robust logging technique to evaluate them in-situ.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"120 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76349046","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}
Fractures are the prime conduits of flow for hydrocarbons in reservoir rocks. Identification and characterization of the fracture network yields valuable information for accurate reservoir evaluation. This study aims to portray the benefits and limitations for various existing fracture characterization methods and define strategic workflows for automated fracture characterization targeting both conventional and unconventional reservoirs separately. While traditional seismic provides qualitative information of fractures and faults on a macro scale, acoustics and other petrophysical logs provide a more comprehensive picture on a meso and micro level. High resolution image logs, with shallow depth of investigation are considered the industry standard for analysis of fractures. However, it is imperative to understand the framework of fracture in both near and far field. Various reservoir-specific collaborative workflows have been elucidated for a consistent evaluation of fracture network, results of which are further segregated using class-based machine learning techniques. This study embarks on understanding the critical requirements for fracture characterization in different lithological settings. Conventional reservoirs have good intrinsic porosity and permeability, yet presence of fractures further enhances the flow capacity. In clastic reservoirs, fractures provide an additional permeability assist to an already producible reservoir. In carbonate reservoirs, overall reservoir and production quality exclusively depends on presence of extensive fracture network as it quantitatively controls the fluid flow interactions among otherwise isolated vugs. Devoid of intrinsic porosity and permeability, the presence of open-extensive fractures is even more critical in unconventional reservoirs such as basement, shale-gas/oil and coal-bed methane, since it demarcates the reservoir zone and defines the economic viability for hydrocarbon exploration in reservoirs. Different forward modeling approaches using the best of conventional logs, borehole images, acoustic data (anisotropy analysis, borehole reflection survey and stoneley waveforms) and magnetic resonance logs have been presented to provide reservoir-specific fracture characterization. Linking the resolution and depth of investigation of different available techniques is vital for the determination of openness and extent of the fractures into the formation. The key innovative aspect of this project is the emphasis on an end-to-end suitable quantitative analysis of flow contributing fractures in different conventional and unconventional reservoirs. Successful establishment of this approach capturing critical information will be the stepping-stone for developing machine learning techniques for field level assessment.
{"title":"A Leap into Automation for Advanced Fracture Characterization","authors":"Radhika Patro, Manas Mishra, Hemlata Chawla, S. Devkar, Mrinal Sinha, Nistha Mukherjee","doi":"10.2118/204653-ms","DOIUrl":"https://doi.org/10.2118/204653-ms","url":null,"abstract":"\u0000 Fractures are the prime conduits of flow for hydrocarbons in reservoir rocks. Identification and characterization of the fracture network yields valuable information for accurate reservoir evaluation. This study aims to portray the benefits and limitations for various existing fracture characterization methods and define strategic workflows for automated fracture characterization targeting both conventional and unconventional reservoirs separately.\u0000 While traditional seismic provides qualitative information of fractures and faults on a macro scale, acoustics and other petrophysical logs provide a more comprehensive picture on a meso and micro level. High resolution image logs, with shallow depth of investigation are considered the industry standard for analysis of fractures. However, it is imperative to understand the framework of fracture in both near and far field. Various reservoir-specific collaborative workflows have been elucidated for a consistent evaluation of fracture network, results of which are further segregated using class-based machine learning techniques.\u0000 This study embarks on understanding the critical requirements for fracture characterization in different lithological settings. Conventional reservoirs have good intrinsic porosity and permeability, yet presence of fractures further enhances the flow capacity. In clastic reservoirs, fractures provide an additional permeability assist to an already producible reservoir. In carbonate reservoirs, overall reservoir and production quality exclusively depends on presence of extensive fracture network as it quantitatively controls the fluid flow interactions among otherwise isolated vugs.\u0000 Devoid of intrinsic porosity and permeability, the presence of open-extensive fractures is even more critical in unconventional reservoirs such as basement, shale-gas/oil and coal-bed methane, since it demarcates the reservoir zone and defines the economic viability for hydrocarbon exploration in reservoirs.\u0000 Different forward modeling approaches using the best of conventional logs, borehole images, acoustic data (anisotropy analysis, borehole reflection survey and stoneley waveforms) and magnetic resonance logs have been presented to provide reservoir-specific fracture characterization. Linking the resolution and depth of investigation of different available techniques is vital for the determination of openness and extent of the fractures into the formation.\u0000 The key innovative aspect of this project is the emphasis on an end-to-end suitable quantitative analysis of flow contributing fractures in different conventional and unconventional reservoirs. Successful establishment of this approach capturing critical information will be the stepping-stone for developing machine learning techniques for field level assessment.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75657051","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}
Ibrahim Al-Hulail, Oscar Arauji, Ali AlZaki, Mohamed Zeghouani
Proppant placement in a tight formation is extremely challenging. Therefore, using a high viscous friction reducer (HVFR) as a fracturing fluid for stimulation treatment in tight gas reservoirs is increasing within the industry because it can transport proppant, help reduce pipe friction generated during hydraulic-fracturing treatments, and efficiently clean up similar to the lower viscosity friction reducers (FRs). In this paper the implementation of the robust HVFR that is building higher viscosity at low concentrations, which minimizes energy loss and promotes turbulent flow within the pipe during the pumping of low viscosity, is discussed in detail. Performance evaluation of the new HVFR was conducted in the laboratory and compared to the lower viscosity FR. The study consisted of viscosity measurements at 70 and 180°F, compatibility with other additives, and proppant transport capabilities. Additionally, the viscosity generated from both FRs was compared using two water sources: water well A and treated sewage water. Viscosity measurements were performed across a wide range of FR and HVFR concentrations and under varying shear rates using a digital viscometer. To validate drag reduction capabilities for this HVFR in the field, the same groundwater with low salinity and low total dissolved solids (TDS) content were used for comparison purposes. The test plan for this new HVFR was for a well to be drilled to a total depth of 17,801 ft MD (10,693 ft TVD) with a 6,016-ft lateral section. Another part of the plan was to complete 41 stages—the first stage with the toe initiator, and subsequent stages using ball drops until Stage 8, were completed using the current FR. For Stage 8, the drag reduction from the new HVFR was evaluated against the current FR only during the pad stage. Then, FR or HVFR concentrations were used, with a gradual reduction from 2 to 1 gpt without compromising proppant placement from stages 9 to 37, alternating current FR and the new HVFR every four stages. From Stage 38 to 41, the same approach was used but with treated sewage water and alternating every other stage using current FR or HVFR at 1gpt. The implementation of the new HVFR showed better friction reduction when using the same concentration of the current FR. Also, achieving better average treating pressures with lower concentration. Based on that it is a cost-effective solution and the performance is better, this lead to reduce the HVFR volume to be pumped per stage compared to the current FR. For this study, drag reduction capabilities for this new HVFR were validated in the field at higher pumping rate conditions, potentially optimizing (reducing) the polymer concentration during a freshwater application. It was shown that lower concentrations of this HVFR provided higher viscosity, which helps improve proppant transport and operation placement.
在致密地层中放置支撑剂是极具挑战性的。因此，业内越来越多地使用高黏度减阻剂(HVFR)作为压裂液用于致密气藏的增产处理，因为它可以输送支撑剂，有助于减少水力压裂过程中产生的管柱摩擦，并且与低黏度减阻剂(FRs)类似，可以有效地进行清理。本文详细讨论了在低浓度下建立高粘度的鲁棒HVFR的实现，从而在低粘度泵送过程中最大限度地减少能量损失并促进管内湍流流动。在实验室中对新型HVFR进行了性能评估，并与低粘度FR进行了比较。研究包括在70°F和180°F下的粘度测量、与其他添加剂的相容性以及支撑剂的输送能力。此外，使用A井和处理过的污水两种水源，对两种FRs产生的粘度进行了比较。粘度测量是在大范围的FR和HVFR浓度和不同的剪切速率下使用数字粘度计进行的。为了验证该HVFR在现场的减阻能力，使用了低盐度、低总溶解固体(TDS)含量的相同地下水进行比较。新HVFR的测试计划是钻一口井，总深度为17801 ft MD (10693 ft TVD)，横向段为6016 ft。计划的另一部分是完成41级作业，第一级使用趾部启动器，随后的阶段使用球滴，直到第8级，使用现有FR完成。对于第8级，仅在垫段阶段，新HVFR的减阻效果与现有FR进行了评估。然后，使用FR或HVFR浓度，从第9级到第37级，在不影响支撑剂投放的情况下，从2 gpt逐渐降低到1 gpt，每4级使用交流电FR和新的HVFR。从第38阶段到第41阶段，使用了相同的方法，但使用了处理过的污水，并且每隔一段交替使用1gpt的电流FR或HVFR。在使用相同浓度的现有FR时，新型HVFR表现出更好的摩擦减少效果，并且在较低浓度下获得更好的平均处理压力。基于这一经济高效的解决方案，该方案的性能更好，与目前的FR相比，每级泵送的HVFR体积更小。在本研究中，新型HVFR的减阻能力在更高的泵送速率条件下进行了现场验证，有可能优化(降低)淡水应用过程中的聚合物浓度。研究表明，较低浓度的HVFR具有较高的粘度，有助于改善支撑剂的输送和作业位置。
{"title":"High Performance Friction Reducer for Slickwater Fracturing Applications: Laboratory Study and Field Implementation","authors":"Ibrahim Al-Hulail, Oscar Arauji, Ali AlZaki, Mohamed Zeghouani","doi":"10.2118/204878-ms","DOIUrl":"https://doi.org/10.2118/204878-ms","url":null,"abstract":"\u0000 Proppant placement in a tight formation is extremely challenging. Therefore, using a high viscous friction reducer (HVFR) as a fracturing fluid for stimulation treatment in tight gas reservoirs is increasing within the industry because it can transport proppant, help reduce pipe friction generated during hydraulic-fracturing treatments, and efficiently clean up similar to the lower viscosity friction reducers (FRs). In this paper the implementation of the robust HVFR that is building higher viscosity at low concentrations, which minimizes energy loss and promotes turbulent flow within the pipe during the pumping of low viscosity, is discussed in detail.\u0000 Performance evaluation of the new HVFR was conducted in the laboratory and compared to the lower viscosity FR. The study consisted of viscosity measurements at 70 and 180°F, compatibility with other additives, and proppant transport capabilities. Additionally, the viscosity generated from both FRs was compared using two water sources: water well A and treated sewage water. Viscosity measurements were performed across a wide range of FR and HVFR concentrations and under varying shear rates using a digital viscometer.\u0000 To validate drag reduction capabilities for this HVFR in the field, the same groundwater with low salinity and low total dissolved solids (TDS) content were used for comparison purposes. The test plan for this new HVFR was for a well to be drilled to a total depth of 17,801 ft MD (10,693 ft TVD) with a 6,016-ft lateral section. Another part of the plan was to complete 41 stages—the first stage with the toe initiator, and subsequent stages using ball drops until Stage 8, were completed using the current FR. For Stage 8, the drag reduction from the new HVFR was evaluated against the current FR only during the pad stage. Then, FR or HVFR concentrations were used, with a gradual reduction from 2 to 1 gpt without compromising proppant placement from stages 9 to 37, alternating current FR and the new HVFR every four stages. From Stage 38 to 41, the same approach was used but with treated sewage water and alternating every other stage using current FR or HVFR at 1gpt.\u0000 The implementation of the new HVFR showed better friction reduction when using the same concentration of the current FR. Also, achieving better average treating pressures with lower concentration. Based on that it is a cost-effective solution and the performance is better, this lead to reduce the HVFR volume to be pumped per stage compared to the current FR.\u0000 \u0000 \u0000 For this study, drag reduction capabilities for this new HVFR were validated in the field at higher pumping rate conditions, potentially optimizing (reducing) the polymer concentration during a freshwater application. It was shown that lower concentrations of this HVFR provided higher viscosity, which helps improve proppant transport and operation placement.\u0000","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73002702","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}
Yanhui Zhang, I. Hoteit, Klemens Katterbauer, A. Marsala
Saturation mapping in fractured carbonate reservoirs is a major challenge for oil and gas companies. The fracture channels within the reservoir are the primary water conductors that shape water front patterns and cause uneven sweep efficiency. Flow simulation for fractured reservoirs is typically time-consuming due to the inherent high nonlinearity. A data-driven approach to capture the main flow patterns is quintessential for efficient optimization of reservoir performance and uncertainty quantification. We employ an artificial intelligence (AI) aided proxy modeling framework for waterfront tracking in complex fractured carbonate reservoirs. The framework utilizes deep neural networks and reduced-order modeling to achieve an efficient representation of the reservoir dynamics to track and determine the fluid flow patterns within the fracture network. The AI-proxy model is examined on a synthetic two-dimensional (2D) fractured carbonate reservoir model. Training dataset including saturation and pressure maps at a series of time steps is generated using a dual-porosity dual-permeability (DPDP) model. Experimental results indicate a robust performance of the AI-aided proxy model, which successfully reproduce the key flow patterns within the reservoir and achieve orders of shorter running time than the full-order reservoir simulation. This suggests the great potential of utilizing the AI-aided proxy model for heavy-simulation-based reservoir applications such as history matching, production optimization, and uncertainty assessment.
{"title":"Artificial Intelligence Aided Proxy Model for Water Front Tracking in Fractured Carbonate Reservoirs","authors":"Yanhui Zhang, I. Hoteit, Klemens Katterbauer, A. Marsala","doi":"10.2118/204604-ms","DOIUrl":"https://doi.org/10.2118/204604-ms","url":null,"abstract":"\u0000 Saturation mapping in fractured carbonate reservoirs is a major challenge for oil and gas companies. The fracture channels within the reservoir are the primary water conductors that shape water front patterns and cause uneven sweep efficiency. Flow simulation for fractured reservoirs is typically time-consuming due to the inherent high nonlinearity. A data-driven approach to capture the main flow patterns is quintessential for efficient optimization of reservoir performance and uncertainty quantification.\u0000 We employ an artificial intelligence (AI) aided proxy modeling framework for waterfront tracking in complex fractured carbonate reservoirs. The framework utilizes deep neural networks and reduced-order modeling to achieve an efficient representation of the reservoir dynamics to track and determine the fluid flow patterns within the fracture network. The AI-proxy model is examined on a synthetic two-dimensional (2D) fractured carbonate reservoir model. Training dataset including saturation and pressure maps at a series of time steps is generated using a dual-porosity dual-permeability (DPDP) model. Experimental results indicate a robust performance of the AI-aided proxy model, which successfully reproduce the key flow patterns within the reservoir and achieve orders of shorter running time than the full-order reservoir simulation. This suggests the great potential of utilizing the AI-aided proxy model for heavy-simulation-based reservoir applications such as history matching, production optimization, and uncertainty assessment.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82257621","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}
Mohammed Al-Hashemi, D. Spivakovskaya, Evert Moes, P. I. '. Panhuis, G. Hemink, V. Shako, Dmitry Kortukov
Fiber Optic Systems, such as Distributed Temperature Sensing (DTS), have been used for wellbore surveillance for more than two decades. One of the traditional applications of DTS is injectivity profiling, both for hydraulically fractured and non-fractured wells. There is a long history of determining injectivity profiles using temperature profiles, usually by analyzing warm-back data with largely pure heat conduction models or by employing a so-called "hot-slug" approach that requires tracking of a temperature transient that arises at the onset of injection. In many of these attempts there is no analysis performed for the key influencing physical factors that could create significant ambiguity in the interpretation results. Among such factors we will consider in detail is the possible impact of cross-flow during the early warm-back stage, but also the temperature transient signal that is related to the location of the fiber-optic sensing cable behind the casing when the fast transient data are used for interpretation such as the "hot slug" during re-injection. In this paper it will be shown that despite all such potential complications, the high frequency and quality of the transient data that can be obtained from a continuous DTS measurement allow for a highly reliable and robust evaluation of the injectivity profile. The well-known challenge of the ambiguity of the interpretation, produced by the interpretation methods that are conventionally used, is overcome using the innovative "Pressure Rate Temperature Transient Analysis" method that takes maximum use of the complete DTS transient data set and all other available data at the level of the model-based interpretation. This method is based on conversion of field measurements into injectivity profiles taking into account the uncertainty in different parts of the data set, which includes the specifics of the DTS deployment, the uncertainty in surface flow rates, and possible data gaps in the history of the well. Several case studies will be discussed where this approach was applied to water injection wells. For the analysis, the re-injection and warmback DTS transient temperature measurements were taken from across the sandface. Furthermore, for comparison, injection profiles were also recorded by conventional PLTs in parallel. This case study will focus mostly on the advanced interpretation opportunities and the challenges related to crossflow through the wellbore during the warm-back phase, related to reservoir pressure dynamics, and finally related to the impact of the method of DTS deployment. In addition to describing the interpretation methodology, this paper will also show the final comparison of the fiber-optic evaluation with the interpretation obtained from the reference PLTs.
{"title":"Water Injection Profiling Using Fiber Optic Sensing by Applying the Novel Pressure Rate Temperature Transient PTRA Analysis","authors":"Mohammed Al-Hashemi, D. Spivakovskaya, Evert Moes, P. I. '. Panhuis, G. Hemink, V. Shako, Dmitry Kortukov","doi":"10.2118/204713-ms","DOIUrl":"https://doi.org/10.2118/204713-ms","url":null,"abstract":"\u0000 Fiber Optic Systems, such as Distributed Temperature Sensing (DTS), have been used for wellbore surveillance for more than two decades. One of the traditional applications of DTS is injectivity profiling, both for hydraulically fractured and non-fractured wells. There is a long history of determining injectivity profiles using temperature profiles, usually by analyzing warm-back data with largely pure heat conduction models or by employing a so-called \"hot-slug\" approach that requires tracking of a temperature transient that arises at the onset of injection. In many of these attempts there is no analysis performed for the key influencing physical factors that could create significant ambiguity in the interpretation results. Among such factors we will consider in detail is the possible impact of cross-flow during the early warm-back stage, but also the temperature transient signal that is related to the location of the fiber-optic sensing cable behind the casing when the fast transient data are used for interpretation such as the \"hot slug\" during re-injection.\u0000 In this paper it will be shown that despite all such potential complications, the high frequency and quality of the transient data that can be obtained from a continuous DTS measurement allow for a highly reliable and robust evaluation of the injectivity profile. The well-known challenge of the ambiguity of the interpretation, produced by the interpretation methods that are conventionally used, is overcome using the innovative \"Pressure Rate Temperature Transient Analysis\" method that takes maximum use of the complete DTS transient data set and all other available data at the level of the model-based interpretation. This method is based on conversion of field measurements into injectivity profiles taking into account the uncertainty in different parts of the data set, which includes the specifics of the DTS deployment, the uncertainty in surface flow rates, and possible data gaps in the history of the well.\u0000 Several case studies will be discussed where this approach was applied to water injection wells. For the analysis, the re-injection and warmback DTS transient temperature measurements were taken from across the sandface. Furthermore, for comparison, injection profiles were also recorded by conventional PLTs in parallel.\u0000 This case study will focus mostly on the advanced interpretation opportunities and the challenges related to crossflow through the wellbore during the warm-back phase, related to reservoir pressure dynamics, and finally related to the impact of the method of DTS deployment. In addition to describing the interpretation methodology, this paper will also show the final comparison of the fiber-optic evaluation with the interpretation obtained from the reference PLTs.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78513996","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}
Rock-typing is complicated and critical for numerical simulation. Therefore, some researchers proposed several clustering methods to make classification automatic and convenient. However, traditional methods only focus in specific area such as lithofacies or petrophysical data instead of integrated clustering. Besides, all the clustering method are related to classification interval determined subjectively. Therefore, a new clustering method for rock-typing integrated different disciplines is critical for modelling and reservoir simulation. In this paper, we proposed a novel semi-supervised clustering method integrated with data from different disciplines, which can divide rock type automatically and precisely. Considering AA reservoir is a porous carbonate reservoir with seldom fracture and vug, FZI (Flow Zone Indicator) and RQI (Reservoir Quality Index) is utilized as the corner stone of the clustering method after collection and plotting for porosity and permeability data for cores from AA reservoir. Then lithofacies, sedimentary facies and petrophysical data are applied as constraints to improve FZI method. Hamming distance and earth mover distance are imported to build integrated function for clustering method. Finally, based on output results of integrated clustering method from experimental data, grid properties of model in Petrel software are imported as the input parameter for further procession. Therefore, saturation region for numerical simulation built by rock-typing is constructed. The results show that new method could make classification accurately and easily. History matching results for watercut indicate that new saturation regions improve the numerical simulation performance.
岩石分型复杂，对数值模拟至关重要。因此，一些研究人员提出了几种聚类方法来实现自动和方便的分类。然而，传统的方法只关注特定区域，如岩相或岩石物理数据，而不是综合聚类。此外，所有的聚类方法都与主观确定的分类区间有关。因此，一种综合不同学科的岩石分型聚类方法对油藏建模和模拟具有重要意义。本文提出了一种结合不同学科数据的半监督聚类方法，可以自动准确地划分岩石类型。考虑到AA储层为孔隙型碳酸盐岩储层，裂缝和空隙较少，在对AA储层岩心的孔隙度和渗透率数据进行采集和作图后，以FZI (Flow Zone Indicator)和RQI (reservoir Quality Index)作为聚类方法的基石。然后以岩相、沉积相和岩石物理资料为约束，对FZI方法进行改进。引入汉明距离和土方移动距离构建集成函数进行聚类。最后，在综合聚类方法从实验数据中输出结果的基础上，导入Petrel软件中模型的网格属性作为输入参数进行进一步处理。因此，构建了岩石分型构建的数值模拟饱和区。结果表明，新方法分类简单、准确。含水历史拟合结果表明，新的饱和区域提高了数值模拟性能。
{"title":"Integrated Semi-Supervised Clustering Method and Its Application in Rock-Typing in AA Reservoir","authors":"Ruicheng Ma, D. Hu, Ya Deng, Limin Zhao, Shu Wang","doi":"10.2118/204777-ms","DOIUrl":"https://doi.org/10.2118/204777-ms","url":null,"abstract":"\u0000 Rock-typing is complicated and critical for numerical simulation. Therefore, some researchers proposed several clustering methods to make classification automatic and convenient. However, traditional methods only focus in specific area such as lithofacies or petrophysical data instead of integrated clustering. Besides, all the clustering method are related to classification interval determined subjectively. Therefore, a new clustering method for rock-typing integrated different disciplines is critical for modelling and reservoir simulation.\u0000 In this paper, we proposed a novel semi-supervised clustering method integrated with data from different disciplines, which can divide rock type automatically and precisely. Considering AA reservoir is a porous carbonate reservoir with seldom fracture and vug, FZI (Flow Zone Indicator) and RQI (Reservoir Quality Index) is utilized as the corner stone of the clustering method after collection and plotting for porosity and permeability data for cores from AA reservoir. Then lithofacies, sedimentary facies and petrophysical data are applied as constraints to improve FZI method. Hamming distance and earth mover distance are imported to build integrated function for clustering method. Finally, based on output results of integrated clustering method from experimental data, grid properties of model in Petrel software are imported as the input parameter for further procession. Therefore, saturation region for numerical simulation built by rock-typing is constructed. The results show that new method could make classification accurately and easily. History matching results for watercut indicate that new saturation regions improve the numerical simulation performance.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81135114","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 study investigates the impact of petrophysical rock properties on the velocity-pressure relationship in carbonates. It presents an approach to predict the changes in compressional velocity (Vp) as function of pressure in carbonates. The approach honors the complexity of carbonates by incorporating various petrophysical rock properties including bulk density, porosity, mineralogy and pore stiffness. The data used in this study consists of rock properties (density, porosity, mineralogy) and elastic velocity measured as function of confining pressure for 220 carbonate core plug samples from published literature. Pearson correlation coefficient was calculated to evaluate the significance of each property in predicting velocity-pressure relationship. A simple regression was formulated incorporating all significant input rock properties to predict Vp as function of pressure based on initial measured velocity at a given pressure. The predictions were compared with the measured Vp. The results show that the sensitivity of Vp to changes in pressure increases as the porosity and pore compressiblity increases. On the other hand, samples with higher bulk density and Vp / Vs ratio (at initial lowest pressure) show little Vp variations as function of increasing pressure. High Vp / Vs values are observed in samples that are well cemented and have less clay or silisiclastic fraction. Such characteristics reduce the compressibility of pores leading to non-variable velocity-pressure relationship. Incorporating the rock properties in regression analysis could successfully predict Vp as function of pressure with a correlation coefficient of 0.99 and average absolute error of less than 3%. Since all input parameters (rock properties) can be estimated from well logs, the presented approach can potentially be used to predict in-situ changes in Vp due to pressure changes. This can assist the interpretation of time lapse seismic, and in geomechanics-related applications.
{"title":"The Impact of Rock Microstructure and Petrophysical Properties on the Velocity-Pressure Relationship of Carbonates","authors":"A. El-Husseiny","doi":"10.2118/204735-ms","DOIUrl":"https://doi.org/10.2118/204735-ms","url":null,"abstract":"\u0000 This study investigates the impact of petrophysical rock properties on the velocity-pressure relationship in carbonates. It presents an approach to predict the changes in compressional velocity (Vp) as function of pressure in carbonates. The approach honors the complexity of carbonates by incorporating various petrophysical rock properties including bulk density, porosity, mineralogy and pore stiffness. The data used in this study consists of rock properties (density, porosity, mineralogy) and elastic velocity measured as function of confining pressure for 220 carbonate core plug samples from published literature. Pearson correlation coefficient was calculated to evaluate the significance of each property in predicting velocity-pressure relationship. A simple regression was formulated incorporating all significant input rock properties to predict Vp as function of pressure based on initial measured velocity at a given pressure. The predictions were compared with the measured Vp. The results show that the sensitivity of Vp to changes in pressure increases as the porosity and pore compressiblity increases. On the other hand, samples with higher bulk density and Vp / Vs ratio (at initial lowest pressure) show little Vp variations as function of increasing pressure. High Vp / Vs values are observed in samples that are well cemented and have less clay or silisiclastic fraction. Such characteristics reduce the compressibility of pores leading to non-variable velocity-pressure relationship. Incorporating the rock properties in regression analysis could successfully predict Vp as function of pressure with a correlation coefficient of 0.99 and average absolute error of less than 3%. Since all input parameters (rock properties) can be estimated from well logs, the presented approach can potentially be used to predict in-situ changes in Vp due to pressure changes. This can assist the interpretation of time lapse seismic, and in geomechanics-related applications.","PeriodicalId":11094,"journal":{"name":"Day 2 Mon, November 29, 2021","volume":"98 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85929305","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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