Stylianos Kyriacou, P. Sarma, J. Rafiee, Calad Carlos
With growing worldwide consensus about the impacts of climate change, the oil and gas industry faces unprecedented pressure to minimize its carbon footprint. The biggest source of carbon emissions in the industry is the so-called fugitive emissions, accounting for ~57% of the total oil and gas industry emissions, resulting from leaks in oil and gas pipelines and facilities. Fast, accurate and economic prediction of leaks in pipelines would significantly reduce fugitive emissions by reducing the time to respond to a leak. The proposed leak detection algorithm is a mixture of state-of-the-art machine learning and data assimilation techniques with well-known physical models and correlations of fluid flow in pipeline networks. The algorithm is tasked to continuously oversee pipeline operations by means of pressure and flow measurements. The proposed algorithm can probabilistically detect when and where a leak is taking place at the frequency of data collection (minutes/hours), thus minimizing the time to respond and the total fluid loss (fugitive emissions). The proposed algorithm utilizes a variant of the ensemble Kalman filter for probabilistic data assimilation together with an underlying network physics model. The model is augmented with meta-models and anomaly detection machine learning algorithms for real-time detection of leaks. The effectiveness of the proposed algorithm is demonstrated through a synthetic test case based on a realistic dataset.
{"title":"Pipeline Leak Detection Combining Machine Learning, Data Assimilation Approaches and Pipeline Fluid Flow Physics Models","authors":"Stylianos Kyriacou, P. Sarma, J. Rafiee, Calad Carlos","doi":"10.2523/iptc-22469-ea","DOIUrl":"https://doi.org/10.2523/iptc-22469-ea","url":null,"abstract":"\u0000 With growing worldwide consensus about the impacts of climate change, the oil and gas industry faces unprecedented pressure to minimize its carbon footprint. The biggest source of carbon emissions in the industry is the so-called fugitive emissions, accounting for ~57% of the total oil and gas industry emissions, resulting from leaks in oil and gas pipelines and facilities. Fast, accurate and economic prediction of leaks in pipelines would significantly reduce fugitive emissions by reducing the time to respond to a leak.\u0000 The proposed leak detection algorithm is a mixture of state-of-the-art machine learning and data assimilation techniques with well-known physical models and correlations of fluid flow in pipeline networks. The algorithm is tasked to continuously oversee pipeline operations by means of pressure and flow measurements. The proposed algorithm can probabilistically detect when and where a leak is taking place at the frequency of data collection (minutes/hours), thus minimizing the time to respond and the total fluid loss (fugitive emissions). The proposed algorithm utilizes a variant of the ensemble Kalman filter for probabilistic data assimilation together with an underlying network physics model. The model is augmented with meta-models and anomaly detection machine learning algorithms for real-time detection of leaks. The effectiveness of the proposed algorithm is demonstrated through a synthetic test case based on a realistic dataset.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"31 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75990880","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}
F. Malki, O. Alade, Jafar Al Hamad, Dahfer Al Shehri, Mohamed Mahmoud
New viscosity model named "Faisal-Viscosity model" was developed to address the large gap in the current rules and models in giving accurate predictions on the behaviour of bitumen-solvent mixtures. The Faisal's developed viscosity model is successfully able to give a more accurate prediction for bitumen solvent mixtures compared to the Ried model with a maximum relative error of 57% while Ried's equation has a maximum error of 1931%.
{"title":"Improved Viscosity Model for Bitumen-Solvent Binary Mixtures","authors":"F. Malki, O. Alade, Jafar Al Hamad, Dahfer Al Shehri, Mohamed Mahmoud","doi":"10.2523/iptc-22561-ea","DOIUrl":"https://doi.org/10.2523/iptc-22561-ea","url":null,"abstract":"\u0000 New viscosity model named \"Faisal-Viscosity model\" was developed to address the large gap in the current rules and models in giving accurate predictions on the behaviour of bitumen-solvent mixtures. The Faisal's developed viscosity model is successfully able to give a more accurate prediction for bitumen solvent mixtures compared to the Ried model with a maximum relative error of 57% while Ried's equation has a maximum error of 1931%.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76158991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Venturi tube, a classic single-phase flow meter, proved to be a reliable and accurate wet gas flow meter, but it requires correction. The presence of liquid content increases the measured differential pressure across the Venturi tube and causes over-reading (OR). Developing novel methods to correct the Venturi tube readings has become a target in the oil & gas industry to quantify production from individual wells. This work is proposing a new approach to overcome the OR challenge using machine learning (ML) models. The ML model to predict OR was developed using the random forest (RF) technique. Initially, synthetic dataset of producing wells were generated. A variation on the type of gasses and liquids were studied including nitrogen and benzene, argon and water, natural gas and water and natural gas and decan. The input parameters consist of fluid properties and fluid conditions. The fluid properties are including gas and liquid phases. The target for these inputs is to predict the OR. The results were then evaluated based on root-mean-square error (RMSE), and the fitness of the model was assessed by the coefficient of determination R2. The ML model showed high accuracy results for the OR prediction of the testing data of R2=0.997 and RMSE of 0.7%. The developed model was then applied to a new set of data of air and water for further validation. This validation resulted in R2= 0.998 and RMSE of 0.5%. This shows that the RF technique is capable of predicting the OR in wet gas when the aforementioned input parameters are used. The uniqueness of this ML model is that the inputs are all measurable in the field. Once the OR is predicted, the "real" gas mass flow rate can be calculated directly. The novelty of this work lies in providing a robust method to calculate the "real" gas mass flow rate real-time which ultimately diminishes the need to use published correlations derived from experimental conditions that are not necessarily representative of the oil field conditions.
{"title":"Application of Machine Learning in Wet Gas Measurement Predictions","authors":"Ziad Sidaoui, Yasmeen Alsunbul, M. Abbad","doi":"10.2523/iptc-22495-ea","DOIUrl":"https://doi.org/10.2523/iptc-22495-ea","url":null,"abstract":"\u0000 The Venturi tube, a classic single-phase flow meter, proved to be a reliable and accurate wet gas flow meter, but it requires correction. The presence of liquid content increases the measured differential pressure across the Venturi tube and causes over-reading (OR). Developing novel methods to correct the Venturi tube readings has become a target in the oil & gas industry to quantify production from individual wells. This work is proposing a new approach to overcome the OR challenge using machine learning (ML) models.\u0000 The ML model to predict OR was developed using the random forest (RF) technique. Initially, synthetic dataset of producing wells were generated. A variation on the type of gasses and liquids were studied including nitrogen and benzene, argon and water, natural gas and water and natural gas and decan. The input parameters consist of fluid properties and fluid conditions. The fluid properties are including gas and liquid phases. The target for these inputs is to predict the OR. The results were then evaluated based on root-mean-square error (RMSE), and the fitness of the model was assessed by the coefficient of determination R2.\u0000 The ML model showed high accuracy results for the OR prediction of the testing data of R2=0.997 and RMSE of 0.7%. The developed model was then applied to a new set of data of air and water for further validation. This validation resulted in R2= 0.998 and RMSE of 0.5%. This shows that the RF technique is capable of predicting the OR in wet gas when the aforementioned input parameters are used. The uniqueness of this ML model is that the inputs are all measurable in the field. Once the OR is predicted, the \"real\" gas mass flow rate can be calculated directly.\u0000 The novelty of this work lies in providing a robust method to calculate the \"real\" gas mass flow rate real-time which ultimately diminishes the need to use published correlations derived from experimental conditions that are not necessarily representative of the oil field conditions.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"26 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74059207","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}
J. Rafiee, P. Sarma, Yong Zhao, Sebastian Plotno, C. Calad, Dayanara Betancourt
Various types of predictive models have been applied over the years to make quantitative decisions for unconventional development plans. These models are either very simple (e.g., type-curves) which ignore the reservoir physics or are too complex (e.g., simulation models) to be able to run for an entire field efficiently. In this paper, we propose a model for design, prediction and optimization of unconventional wells efficiently using a combination of reservoir physics with machine learning methodologies. The proposed model is the amalgamation of the state-of-the-art in machine learning and reservoir physics into a seamless full field model. The physical model ensures that model predictions are always realistic and reliable while the machine learning algorithm allows us to utilize different types of data to make a prediction which cannot be directly integrated into the physical model. The model uses a probabilistic approach to estimate P10-P50-P90 production curves to account for uncertainty in predictions. The data from more than 1800 unconventional wells in a real field is used to train and test our proposed model. The input features are completion design parameters like lateral length, proppant concentration, well spacing, etc., and the output in a full time series of expected oil production from the well. The results show that our modelʼs prediction leads to correlations of more than 0.75 for the test set which is indicative of its good predictive accuracy. The sensitivity analysis of the parameters of the model on the cumulative production shows that volume of injection fluid, length of the lateral and the proppant concentration are among the most important parameters.
{"title":"Combining Machine Learning and Physics for Robust Optimization of Completion Design and Well Location of Unconventional Wells","authors":"J. Rafiee, P. Sarma, Yong Zhao, Sebastian Plotno, C. Calad, Dayanara Betancourt","doi":"10.2523/iptc-22214-ms","DOIUrl":"https://doi.org/10.2523/iptc-22214-ms","url":null,"abstract":"\u0000 Various types of predictive models have been applied over the years to make quantitative decisions for unconventional development plans. These models are either very simple (e.g., type-curves) which ignore the reservoir physics or are too complex (e.g., simulation models) to be able to run for an entire field efficiently. In this paper, we propose a model for design, prediction and optimization of unconventional wells efficiently using a combination of reservoir physics with machine learning methodologies. The proposed model is the amalgamation of the state-of-the-art in machine learning and reservoir physics into a seamless full field model. The physical model ensures that model predictions are always realistic and reliable while the machine learning algorithm allows us to utilize different types of data to make a prediction which cannot be directly integrated into the physical model. The model uses a probabilistic approach to estimate P10-P50-P90 production curves to account for uncertainty in predictions.\u0000 The data from more than 1800 unconventional wells in a real field is used to train and test our proposed model. The input features are completion design parameters like lateral length, proppant concentration, well spacing, etc., and the output in a full time series of expected oil production from the well. The results show that our modelʼs prediction leads to correlations of more than 0.75 for the test set which is indicative of its good predictive accuracy. The sensitivity analysis of the parameters of the model on the cumulative production shows that volume of injection fluid, length of the lateral and the proppant concentration are among the most important parameters.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"47 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79200100","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}
Zhibin Jiang, L. Sima, Wei Zhou, Xiaoguang Wang, Hanbing Xu, Kun Ding, Lisha Qi, Sheng Zheng, D. Xie, Liming Lian, Chuanchuan Qian, Jiezhong Wang, Zhiwen Bai
Mud loss is one of the most severe incidents encountered while drilling. As the focus of exploration has moved to the deeper and more complicated formations, mud loss happens more frequently, causing safety incidents and lowering the drilling efficiency. The lost mud invades into formations, makes harm to the reservoir and affects the accuracy of formation evaluation. Large numbers of studies have proved that the key strategy to deal with mud loss is to prevent it and follow with remedial action. The prerequisite of such measurements is locating the formation of mud loss. Loss pressure is the highest pressure that formation can hold before mud loss happens. It is important for deciding mud weight and critical for fractured formations. Therefore, an innovative ring-cracking loss model is created by integrating minimum horizontal principal stress loss model, statistical loss model, and critical crack width loss model after analyzing the loss mechanism of fractured formation. These four models are applied to the fractured formation of block X, and the results are compared to the actual downhole pressure.The result from ring-cracking-based loss model is less than the actual downhole pressure. This model is suitable for field application and can guide the design of mud density.
{"title":"A Systematic Approach to Leak-Off Pressure Prediction in Unconventional Resources –A Case Study from Junggar Basin, China","authors":"Zhibin Jiang, L. Sima, Wei Zhou, Xiaoguang Wang, Hanbing Xu, Kun Ding, Lisha Qi, Sheng Zheng, D. Xie, Liming Lian, Chuanchuan Qian, Jiezhong Wang, Zhiwen Bai","doi":"10.2523/iptc-22065-ms","DOIUrl":"https://doi.org/10.2523/iptc-22065-ms","url":null,"abstract":"\u0000 Mud loss is one of the most severe incidents encountered while drilling. As the focus of exploration has moved to the deeper and more complicated formations, mud loss happens more frequently, causing safety incidents and lowering the drilling efficiency. The lost mud invades into formations, makes harm to the reservoir and affects the accuracy of formation evaluation. Large numbers of studies have proved that the key strategy to deal with mud loss is to prevent it and follow with remedial action. The prerequisite of such measurements is locating the formation of mud loss. Loss pressure is the highest pressure that formation can hold before mud loss happens. It is important for deciding mud weight and critical for fractured formations. Therefore, an innovative ring-cracking loss model is created by integrating minimum horizontal principal stress loss model, statistical loss model, and critical crack width loss model after analyzing the loss mechanism of fractured formation. These four models are applied to the fractured formation of block X, and the results are compared to the actual downhole pressure.The result from ring-cracking-based loss model is less than the actual downhole pressure. This model is suitable for field application and can guide the design of mud density.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80399850","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}
For the first time, two different sizes of nonmetallic casing strings were installed in water wells to cover shallow potable aquifers. This paper describes the reasons for deployment, planning and design, logistics, operational challenges, lessons learned, and the way forward for this newly deployed technology. In the initial stages of the project, fiberglass-reinforced thermoset resin (RTR) pipes manufactured locally were evaluated in terms of ratings, dimension, and method of connection and feasibility for downhole applications. Two nonmetallic casing strings, 19.7″ and 11″, were selected to be run in hole. Design consideration also included compatibility with available casing running and handling tools to ensure safe and efficient field handling and running. At this stage, carbon steel casings were still needed to connect the nonmetallic casing to the surface wellhead equipment and to the float equipment at the bottom of the string. Specially designed crossovers were manufactured and tested prior to enabling combination of nonmetallic and carbon steel casing. All manufactured casing joints and crossovers were tested based on the best available criteria for the nonmetallic industry. Different challenges were encountered in the design stage, such as overcoming the buoyancy force while running and cementing the nonmetallic casing, all of which to be tackled. Cement slurry design and casing accessories were modified based on the simulations scenarios that were run. These designs were subsequently modified in response to issues, i.e., total losses, encountered while drilling. Successful evaluation of the nonmetallic casing deployment was conducted from multiple aspects, including running efficiency, casing wear, and cement quality. Drillpipe protectors were utilized to reduce the possible casing damage due to wear. The nonmetallic casing joints were connected through crossovers to a top metallic casing and float equipment at bottom. Both casing strings were successfully run to depth and cemented in place. Both casings were pressure tested successfully after performing the logging jobs that indicated the level and quality of cement pumped around the strings. Logs showed no considerable change in both nonmetallic casing thickness. The well was completed with open hole, tested and flowed naturally to surface. A conventional power water injector wellhead was installed before release. The design, review and assessment processes, as well as several lessons learned from the first ever deployment of the nonmetallic casing in a water supply well, are the key takeaways from this paper.
{"title":"First Successful Deployment of Nonmetallic Casing Strings: A Case History","authors":"Fauzia Waluyo, Ali Hijles, Muhammad A. Alhelal","doi":"10.2523/iptc-22660-ms","DOIUrl":"https://doi.org/10.2523/iptc-22660-ms","url":null,"abstract":"For the first time, two different sizes of nonmetallic casing strings were installed in water wells to cover shallow potable aquifers. This paper describes the reasons for deployment, planning and design, logistics, operational challenges, lessons learned, and the way forward for this newly deployed technology.\u0000 In the initial stages of the project, fiberglass-reinforced thermoset resin (RTR) pipes manufactured locally were evaluated in terms of ratings, dimension, and method of connection and feasibility for downhole applications. Two nonmetallic casing strings, 19.7″ and 11″, were selected to be run in hole. Design consideration also included compatibility with available casing running and handling tools to ensure safe and efficient field handling and running. At this stage, carbon steel casings were still needed to connect the nonmetallic casing to the surface wellhead equipment and to the float equipment at the bottom of the string. Specially designed crossovers were manufactured and tested prior to enabling combination of nonmetallic and carbon steel casing. All manufactured casing joints and crossovers were tested based on the best available criteria for the nonmetallic industry.\u0000 Different challenges were encountered in the design stage, such as overcoming the buoyancy force while running and cementing the nonmetallic casing, all of which to be tackled. Cement slurry design and casing accessories were modified based on the simulations scenarios that were run. These designs were subsequently modified in response to issues, i.e., total losses, encountered while drilling.\u0000 Successful evaluation of the nonmetallic casing deployment was conducted from multiple aspects, including running efficiency, casing wear, and cement quality. Drillpipe protectors were utilized to reduce the possible casing damage due to wear.\u0000 The nonmetallic casing joints were connected through crossovers to a top metallic casing and float equipment at bottom. Both casing strings were successfully run to depth and cemented in place.\u0000 Both casings were pressure tested successfully after performing the logging jobs that indicated the level and quality of cement pumped around the strings. Logs showed no considerable change in both nonmetallic casing thickness.\u0000 The well was completed with open hole, tested and flowed naturally to surface. A conventional power water injector wellhead was installed before release.\u0000 The design, review and assessment processes, as well as several lessons learned from the first ever deployment of the nonmetallic casing in a water supply well, are the key takeaways from this paper.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"109 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80575256","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}
Explicit modeling of discrete fractures at the field scale is computationally intensive, and therefore, upscaling the fractures in the context of an equivalent continuum model is indispensable for field-scale simulations. This work introduces a new upscaling technique based on the local-global multiple-boundary (LG-MB) upscaling method for fluid flow in naturally fractured reservoirs. We extend the commonly-used local-global (LG) upscaling method by introducing a new approach to compute the fluid fluxes within the fractures crossing the grid-block boundaries. This method is based on the multiple-boundary (MB) approach applied within a local-global upscaling procedure. The global coarse-scale simulations are implemented to determine the boundary conditions for the calculations of local upscaled permeability. The procedure allows repeating the process to assure consistency between the global and local calculations. We implement the multi-point flux approximation (MPFA) finite volume method coupled with embedded discrete-fracture model within the MRST framework. We then verify the proposed LG-MB upscaling technique by comparing it with the reference fine-scale solutions and other existing local and local-global upscaling techniques. Results show that the proposed approach provides pronounced accuracy compared to local-based upscaled models with minor computational overhead. Compared to traditional local-global upscaling techniques, it provides more computational accuracy yet with the same efficiency. The superiority of the proposed LG-MB upscaling technique is attributed to two factors related to 1) the multiple-boundary technique to capture the flow behavior with high anisotropy from fractures non-alignment with the grid; 2) the use of local-global approach to accurately obtain the real flow boundary conditions. This work introduces a new upscaling method for flow simulation in naturally fractured reservoirs. We demonstrate its applicability in field applications and its superiority to existing upscaling methods. This method is accurate and straightforward and can be implemented within existing upscaling workflows.
{"title":"A New Local-Global Upscaling Method for Flow Simulation in Naturally Fractured Reservoirs","authors":"Xupeng He, M. AlSinan, H. Kwak, H. Hoteit","doi":"10.2523/iptc-22048-ms","DOIUrl":"https://doi.org/10.2523/iptc-22048-ms","url":null,"abstract":"\u0000 Explicit modeling of discrete fractures at the field scale is computationally intensive, and therefore, upscaling the fractures in the context of an equivalent continuum model is indispensable for field-scale simulations. This work introduces a new upscaling technique based on the local-global multiple-boundary (LG-MB) upscaling method for fluid flow in naturally fractured reservoirs.\u0000 We extend the commonly-used local-global (LG) upscaling method by introducing a new approach to compute the fluid fluxes within the fractures crossing the grid-block boundaries. This method is based on the multiple-boundary (MB) approach applied within a local-global upscaling procedure. The global coarse-scale simulations are implemented to determine the boundary conditions for the calculations of local upscaled permeability. The procedure allows repeating the process to assure consistency between the global and local calculations. We implement the multi-point flux approximation (MPFA) finite volume method coupled with embedded discrete-fracture model within the MRST framework. We then verify the proposed LG-MB upscaling technique by comparing it with the reference fine-scale solutions and other existing local and local-global upscaling techniques. Results show that the proposed approach provides pronounced accuracy compared to local-based upscaled models with minor computational overhead. Compared to traditional local-global upscaling techniques, it provides more computational accuracy yet with the same efficiency. The superiority of the proposed LG-MB upscaling technique is attributed to two factors related to 1) the multiple-boundary technique to capture the flow behavior with high anisotropy from fractures non-alignment with the grid; 2) the use of local-global approach to accurately obtain the real flow boundary conditions. This work introduces a new upscaling method for flow simulation in naturally fractured reservoirs. We demonstrate its applicability in field applications and its superiority to existing upscaling methods. This method is accurate and straightforward and can be implemented within existing upscaling workflows.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"121 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80802446","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}
As reservoir simulation models continue to grow in their size and complexity, the computational cost of reservoir simulation is constantly increasing. Since most of the reservoir simulation time is typically spent in the linear solver (being in the innermost part and the most challenging to parallelize and scale), efficient linear solvers are of utmost importance for reducing reservoir simulation turnaround times. In this work, we study the scalability of a versatile multiscale linear solver, namely the restriction-smoothed basis multiscale method (MsRSB) (Møyner and Lie (2016)) on the emerging massively parallel GPU architecture, and compare it to its performance on the multi-core CPU architecture. MsRSB, unlike traditional multiscale approaches, uses iterative smoothing to adaptively compute multiscale basis functions, allowing it to handle a wide range of difficult grid orientations seen in real-world industrial applications. While MsRSB can be parallelized directly, its reliance on a smoother to determine the basis of functions results in unusual control and data flow patterns. To achieve effective scalability, these patterns must be carefully designed and implemented on massively parallel systems. We extend Manea et al. (2016) and Manea and Almani (2019) work on parallel multiscale methods to move the MsRSB special kernels to shared-memory parallel multi-core and GPU architectures. Highly heterogeneous multimillion-cell 3D problems, adopted from the SPE10 Benchmark (Christie and Blunt (2001) have been used to illustrate the scalability of our parallel MsRSB development. The GPU implementation is benchmarked on a massively parallel architecture consisting of Nvidia Volta V100 GPUs, while the multi-core implementation is benchmarked on a shared memory multi-core architecture consisting of two packages of Intel's Haswell-EP Xeon(R) CPU E5-2667. For both the setup and solution stages, we compare the multi-core implementation versus the GPU implementation. The GPU-based MsRSB implementation shows great scalability, with over a 4-fold reduction in runtime when compared to the optimized multi-core implementation.
随着油藏模拟模型规模和复杂度的不断增长，油藏模拟的计算成本也在不断增加。由于大多数油藏模拟时间通常花在线性求解器上(在最内部，最难以并行化和缩放)，因此高效的线性求解器对于减少油藏模拟周转时间至关重要。在这项工作中，我们研究了一种通用多尺度线性求解器，即限制平滑基多尺度方法(MsRSB) (Møyner和Lie(2016))在新兴的大规模并行GPU架构上的可扩展性，并将其与多核CPU架构上的性能进行了比较。与传统的多尺度方法不同，MsRSB使用迭代平滑来自适应计算多尺度基函数，使其能够处理实际工业应用中出现的各种困难网格方向。虽然MsRSB可以直接并行化，但它依赖于平滑器来确定函数的基础，从而导致不寻常的控制和数据流模式。为了实现有效的可伸缩性，必须在大规模并行系统上仔细设计和实现这些模式。我们扩展了Manea等人(2016)和Manea和Almani(2019)对并行多尺度方法的研究，将MsRSB特殊内核移动到共享内存并行多核和GPU架构。采用SPE10基准(Christie and Blunt(2001))的高度异构的百万单元3D问题已被用来说明我们并行MsRSB开发的可扩展性。GPU实现是在由Nvidia Volta V100 GPU组成的大规模并行架构上进行基准测试的，而多核实现是在由两个英特尔Haswell-EP Xeon(R) CPU E5-2667组成的共享内存多核架构上进行基准测试的。对于设置和解决方案阶段，我们比较了多核实现与GPU实现。基于gpu的MsRSB实现显示出极大的可扩展性，与优化的多核实现相比，运行时减少了4倍以上。
{"title":"GPU-Enabled Scalable Multiscale Solver for Reservoir Simulation","authors":"A. Manea","doi":"10.2523/iptc-22024-ms","DOIUrl":"https://doi.org/10.2523/iptc-22024-ms","url":null,"abstract":"\u0000 As reservoir simulation models continue to grow in their size and complexity, the computational cost of reservoir simulation is constantly increasing. Since most of the reservoir simulation time is typically spent in the linear solver (being in the innermost part and the most challenging to parallelize and scale), efficient linear solvers are of utmost importance for reducing reservoir simulation turnaround times. In this work, we study the scalability of a versatile multiscale linear solver, namely the restriction-smoothed basis multiscale method (MsRSB) (Møyner and Lie (2016)) on the emerging massively parallel GPU architecture, and compare it to its performance on the multi-core CPU architecture.\u0000 MsRSB, unlike traditional multiscale approaches, uses iterative smoothing to adaptively compute multiscale basis functions, allowing it to handle a wide range of difficult grid orientations seen in real-world industrial applications. While MsRSB can be parallelized directly, its reliance on a smoother to determine the basis of functions results in unusual control and data flow patterns. To achieve effective scalability, these patterns must be carefully designed and implemented on massively parallel systems. We extend Manea et al. (2016) and Manea and Almani (2019) work on parallel multiscale methods to move the MsRSB special kernels to shared-memory parallel multi-core and GPU architectures.\u0000 Highly heterogeneous multimillion-cell 3D problems, adopted from the SPE10 Benchmark (Christie and Blunt (2001) have been used to illustrate the scalability of our parallel MsRSB development. The GPU implementation is benchmarked on a massively parallel architecture consisting of Nvidia Volta V100 GPUs, while the multi-core implementation is benchmarked on a shared memory multi-core architecture consisting of two packages of Intel's Haswell-EP Xeon(R) CPU E5-2667. For both the setup and solution stages, we compare the multi-core implementation versus the GPU implementation. The GPU-based MsRSB implementation shows great scalability, with over a 4-fold reduction in runtime when compared to the optimized multi-core implementation.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74659705","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}
Over the past decades, many countries have started to place emphasis towards electricity production from renewable sources, such as solar and wind, to limit the amount of CO2 emissions in the atmosphere and reduce global warming. However, solar and wind energy are exclusively reliant on climate conditions; thus, secure and continuous power supply cannot be guaranteed. Therefore, maintaining reliable and continuous power supply calls for concepts and implementation of energy storage techniques. CO2 subsurface energy storage is one of the most innovative techniques that could be applied to solve drawbacks of traditional storage techniques such as scale limitation in both capacity and time, low efficiencies, environmental concerns, or high costs. In this paper, we reviewed and assessed the use of CO2 subsurface energy storage systems by looking at the thermodynamic cycles, machinery, and reservoir conditions. Moreover, a comprehensive study on multiphase flow in porous media has been conducted by looking at capillarity, relative permeability, mass balance, heat balance, thermal properties, and phase behavior. Different well configurations have been compared by performing injection and production simulations to conclude that the use of horizontal injection and production wells is preferred over other proposed well configurations for many reasons such as decreasing the amount of initial CO2 needed to develop and operate the reservoir, covering a large area of the reservoir, and increasing the system's capacity and efficiency. The findings show that CO2 subsurface energy storage system can operate if certain requirements exist: 1) availability of initial CO2 supply, 2) availability of the necessary equipment and solar, or wind, power plants, 3) safety of the targeted location, 4) two deep, clean reservoirs with high porosity and high permeability and 5) presence of a caprock. Ensuring the existence of requirements determined from this study will allow a safe, large (in terms of capacity and time), efficient, and cheap method to store and produce renewable energy continuously. In the foreseeable future, the world will inevitably depend on electricity production from renewable sources; therefore, more energy storage techniques have to be developed. CO2 subsurface energy storage systems have to be considered as they can contribute to reducing emissions and global warming, ensuring a secure and continuous power supply, and solving the drawbacks of traditional storage techniques. The outcomes of this paper will contribute to the growth and development of CO2 subsurface energy storage systems
{"title":"Supercritical CO2 Recirculation in Reservoirs for Continuous Storage and Production of Renewable Energy","authors":"Ibraheem Aljughaiman","doi":"10.2523/iptc-22268-ms","DOIUrl":"https://doi.org/10.2523/iptc-22268-ms","url":null,"abstract":"\u0000 Over the past decades, many countries have started to place emphasis towards electricity production from renewable sources, such as solar and wind, to limit the amount of CO2 emissions in the atmosphere and reduce global warming. However, solar and wind energy are exclusively reliant on climate conditions; thus, secure and continuous power supply cannot be guaranteed. Therefore, maintaining reliable and continuous power supply calls for concepts and implementation of energy storage techniques. CO2 subsurface energy storage is one of the most innovative techniques that could be applied to solve drawbacks of traditional storage techniques such as scale limitation in both capacity and time, low efficiencies, environmental concerns, or high costs.\u0000 In this paper, we reviewed and assessed the use of CO2 subsurface energy storage systems by looking at the thermodynamic cycles, machinery, and reservoir conditions. Moreover, a comprehensive study on multiphase flow in porous media has been conducted by looking at capillarity, relative permeability, mass balance, heat balance, thermal properties, and phase behavior. Different well configurations have been compared by performing injection and production simulations to conclude that the use of horizontal injection and production wells is preferred over other proposed well configurations for many reasons such as decreasing the amount of initial CO2 needed to develop and operate the reservoir, covering a large area of the reservoir, and increasing the system's capacity and efficiency.\u0000 The findings show that CO2 subsurface energy storage system can operate if certain requirements exist: 1) availability of initial CO2 supply, 2) availability of the necessary equipment and solar, or wind, power plants, 3) safety of the targeted location, 4) two deep, clean reservoirs with high porosity and high permeability and 5) presence of a caprock. Ensuring the existence of requirements determined from this study will allow a safe, large (in terms of capacity and time), efficient, and cheap method to store and produce renewable energy continuously.\u0000 In the foreseeable future, the world will inevitably depend on electricity production from renewable sources; therefore, more energy storage techniques have to be developed. CO2 subsurface energy storage systems have to be considered as they can contribute to reducing emissions and global warming, ensuring a secure and continuous power supply, and solving the drawbacks of traditional storage techniques. The outcomes of this paper will contribute to the growth and development of CO2 subsurface energy storage systems","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"272 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76556149","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}
Northwest oilfield M reservoir is deep block sandstone reservoir with strong aquifer, having thin layer, large water-oil volume ratio, high temperature and salinity, strong heterogeneity, being developed more than 20 years by depletion method, influenced by bottom water, reservoir comes into high water cut period, recovery degree is only 26%, requiring for further enhanced oil recovery technology. CO2 is easily miscible with oil, but whether CO2 is applicable for the reservoir with strong aquifer or not is still unclear. No foundation can be referred to in area optimization for CO2 flooding. It is necessary to conduct research on influencing factors of CO2 flooding in the sandstone reservoir with aquifer and set up the favorable target optimization technology. Reservoir engineering method was applied to study M reservoir production pattern. Based on G&G, the geological-dynamic response law was established and productivity influencing factors were studied. Considering flowing differences between block reservoir and layered reservoir, the flowing field is affected by vertical sweep of bottom water, internal small faults have sealing effect on plane sweep, development unit division method was formed on the basis of vertical hydrodynamic characteristics. According to CO2 occurrence state and dynamic distribution in oil layer and strong aquifer, three types of favorable target have been classified, favorable target optimization technology is established for CO2 flooding in deep block sandstone reservoir with strong aquifer. Research results show that the south and middle block are structurally high, edge water intrusion is weak, main layer is relatively thick and interlayer is developed, I and II type well are the main production well. While the north block is structurally low, reservoir quality is poorer, II and III type wells amount a lot. CO2 density is higher than the oil, in the interlayer undeveloped block, CO2 migrates vertically to the oil-water interface, then spreads laterally forced by bottom water, resulting in an increase in the oil-water interface tension and a decrease in the diffusion rate to the water. The more developed the interlayer is, the larger the sweep volume of CO2 is. In high structural position, fault sealing, interlayer developed and thick layer, the enrichment degree of remaining oil is still high, important to ensure CO2 lateral sweep. As results, favorable target for CO2 flooding is classified into I, II and III type. CO2 flooding pilot test was carried out in type I favorable target. Till now, 18,000 tons of CO2 had been injected, oil exchange ratio in 1st year was 0.13. Research results lay the foundation for the efficient utilization of the remaining oil, so as to explore an effective development method for deep, ultra-deep sandstone reservoir to greatly enhanced oil recovery.
{"title":"Favorable Target Optimization for CO2 Flooding in Deep Block Sandstone Reservoir with Bottom Water: A Case Study of Northwest Oilfield M Reservoir","authors":"Ting Xu, Haiying Liao, Yingfu He, Junjie Nie, Maolei Cui, Yabing Guo","doi":"10.2523/iptc-22303-ms","DOIUrl":"https://doi.org/10.2523/iptc-22303-ms","url":null,"abstract":"\u0000 Northwest oilfield M reservoir is deep block sandstone reservoir with strong aquifer, having thin layer, large water-oil volume ratio, high temperature and salinity, strong heterogeneity, being developed more than 20 years by depletion method, influenced by bottom water, reservoir comes into high water cut period, recovery degree is only 26%, requiring for further enhanced oil recovery technology. CO2 is easily miscible with oil, but whether CO2 is applicable for the reservoir with strong aquifer or not is still unclear. No foundation can be referred to in area optimization for CO2 flooding. It is necessary to conduct research on influencing factors of CO2 flooding in the sandstone reservoir with aquifer and set up the favorable target optimization technology.\u0000 Reservoir engineering method was applied to study M reservoir production pattern. Based on G&G, the geological-dynamic response law was established and productivity influencing factors were studied. Considering flowing differences between block reservoir and layered reservoir, the flowing field is affected by vertical sweep of bottom water, internal small faults have sealing effect on plane sweep, development unit division method was formed on the basis of vertical hydrodynamic characteristics. According to CO2 occurrence state and dynamic distribution in oil layer and strong aquifer, three types of favorable target have been classified, favorable target optimization technology is established for CO2 flooding in deep block sandstone reservoir with strong aquifer.\u0000 Research results show that the south and middle block are structurally high, edge water intrusion is weak, main layer is relatively thick and interlayer is developed, I and II type well are the main production well. While the north block is structurally low, reservoir quality is poorer, II and III type wells amount a lot. CO2 density is higher than the oil, in the interlayer undeveloped block, CO2 migrates vertically to the oil-water interface, then spreads laterally forced by bottom water, resulting in an increase in the oil-water interface tension and a decrease in the diffusion rate to the water. The more developed the interlayer is, the larger the sweep volume of CO2 is. In high structural position, fault sealing, interlayer developed and thick layer, the enrichment degree of remaining oil is still high, important to ensure CO2 lateral sweep. As results, favorable target for CO2 flooding is classified into I, II and III type.\u0000 CO2 flooding pilot test was carried out in type I favorable target. Till now, 18,000 tons of CO2 had been injected, oil exchange ratio in 1st year was 0.13. Research results lay the foundation for the efficient utilization of the remaining oil, so as to explore an effective development method for deep, ultra-deep sandstone reservoir to greatly enhanced oil recovery.","PeriodicalId":11027,"journal":{"name":"Day 3 Wed, February 23, 2022","volume":"418 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2022-02-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76486720","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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