Shale gas reservoirs have become prominent contributors to the world's hydrocarbon resources and production. They exhibit multiple storage mechanisms, two of which are linked to the free and adsorbed gas phase. Since the adsorbed gas may be stored as a denser phase than the free gas, the contribution of the adsorbed phase can be significant. The adsorbed volume is related to the total organic carbon (TOC) and thus, higher TOC can indicate higher hydrocarbon inplace. Furthermore, productivity can be linked to TOC through the potential for overpressure and conversion of kerogen to pore space. However, estimation of the TOC is not a trivial problem, as it depends on geological factors such as depositional environment. In this study, we propose an integrated workflow using concepts of machine learning to estimate TOC. The workflow is divided into 3 sections which are area selection, sub-region categorization, and prediction modeling. Firstly, 3 active exploration and development areas (Kaybob, Pembina, and East shale basin) of the Duvernay Formation are highlighted and the geology of each specific area is analyzed. Thereafter, using the available core data and average properties of the attributes (Gamma Ray, resistivity, density, and distance from mean vitrinite reflectance line), each area is clustered into sub-regions using SVM, logistic regression, and k-means clustering. Finally, using Random Forest prediction, models for each sub-region are developed and ranked with average mean square errors and standard deviations. It is observed that the Kaybob area can be clustered into 2 regions. This observation is supported by the principal component plot (PC1 vs PC2), which shows a dual cloud structure. This is further supported through clustering analysis, which also revealed the same observation. Results of the prediction modeling found random forest as the best predictor, achieving a match wiht the core data with a error less than 10% and in some cases only a 1% deviation. Shale reservoir characterization requires estimation of the key parameters such as TOC. However, it is difficult to estimate TOC with purely physics-based or purely statistical models, as it requires limited specialized data and is impacted by subtle variations in the reservoir. This study suggests that TOC can be accurately estimated by combining geological interpretation and machine learning based algorithms without bearing cost of the specialized data.
页岩气储层已成为世界油气资源和产量的重要贡献者。它们表现出多种储存机制，其中两种与自由气相和吸附气相有关。由于吸附气可以作为比自由气更致密的相储存，因此吸附相的贡献可能是显著的。吸附体积与总有机碳(TOC)有关，TOC越高，有机质含量越高。此外，通过潜在的超压和干酪根向孔隙空间的转化，可以将生产力与TOC联系起来。然而，TOC的估算并不是一个简单的问题，它取决于沉积环境等地质因素。在这项研究中，我们提出了一个使用机器学习概念来估计TOC的集成工作流。工作流分为区域选择、子区域分类和预测建模3个部分。首先，重点介绍了Duvernay组3个活跃勘探开发区域(Kaybob、Pembina和East shale basin)，并对每个区域的地质情况进行了分析。然后，利用可用的岩心数据和属性的平均属性(伽马射线、电阻率、密度以及与平均镜质组反射率线的距离)，利用支持向量机、逻辑回归和k-means聚类将每个区域聚成子区域。最后，利用随机森林预测，对每个子区域建立模型，并使用平均均方误差和标准差进行排序。观察到Kaybob区域可以聚集成2个区域。这一观测结果得到了主成分图(PC1 vs PC2)的支持，它显示了双云结构。聚类分析进一步支持了这一点，聚类分析也揭示了相同的观察结果。预测建模结果发现随机森林是最好的预测器，实现了与核心数据的匹配，误差小于10%，在某些情况下只有1%的偏差。页岩储层表征需要对TOC等关键参数进行估计。然而，纯物理模型或纯统计模型很难估计TOC，因为它需要有限的专业数据，并且受储层细微变化的影响。该研究表明，通过结合地质解释和基于机器学习的算法，可以准确估算TOC，而无需承担专业数据的成本。
{"title":"Machine Learning Based Integrated Approach to Estimate Total Organic Carbon in Shale Reservoirs – A Case Study from Duvernay Formation, Alberta Canada","authors":"Gaurav Sharma, Derek Hayes","doi":"10.2118/208916-ms","DOIUrl":"https://doi.org/10.2118/208916-ms","url":null,"abstract":"\u0000 Shale gas reservoirs have become prominent contributors to the world's hydrocarbon resources and production. They exhibit multiple storage mechanisms, two of which are linked to the free and adsorbed gas phase. Since the adsorbed gas may be stored as a denser phase than the free gas, the contribution of the adsorbed phase can be significant. The adsorbed volume is related to the total organic carbon (TOC) and thus, higher TOC can indicate higher hydrocarbon inplace. Furthermore, productivity can be linked to TOC through the potential for overpressure and conversion of kerogen to pore space. However, estimation of the TOC is not a trivial problem, as it depends on geological factors such as depositional environment. In this study, we propose an integrated workflow using concepts of machine learning to estimate TOC.\u0000 The workflow is divided into 3 sections which are area selection, sub-region categorization, and prediction modeling. Firstly, 3 active exploration and development areas (Kaybob, Pembina, and East shale basin) of the Duvernay Formation are highlighted and the geology of each specific area is analyzed. Thereafter, using the available core data and average properties of the attributes (Gamma Ray, resistivity, density, and distance from mean vitrinite reflectance line), each area is clustered into sub-regions using SVM, logistic regression, and k-means clustering. Finally, using Random Forest prediction, models for each sub-region are developed and ranked with average mean square errors and standard deviations.\u0000 It is observed that the Kaybob area can be clustered into 2 regions. This observation is supported by the principal component plot (PC1 vs PC2), which shows a dual cloud structure. This is further supported through clustering analysis, which also revealed the same observation. Results of the prediction modeling found random forest as the best predictor, achieving a match wiht the core data with a error less than 10% and in some cases only a 1% deviation.\u0000 Shale reservoir characterization requires estimation of the key parameters such as TOC. However, it is difficult to estimate TOC with purely physics-based or purely statistical models, as it requires limited specialized data and is impacted by subtle variations in the reservoir. This study suggests that TOC can be accurately estimated by combining geological interpretation and machine learning based algorithms without bearing cost of the specialized data.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128542767","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 reservoirs in Kuqa foreland area of Tarim Basin in China are ultra-deep HTHP (High Temperature and High Pressure) naturally fractured sandstone reservoirs. Due to low permeability of the matrix (<0.1mD), stimulation of the natural fractures is the key to well productivity enhancement. Different stimulation techniques with different stimulation strengths have been tried in the last decade, but stimulation effectiveness varied. Therefore, machine learning method is employed to identify the main controlling factors and optimize the well stimulation design. Firstly, geological data, stimulation data, productivity data, etc. for more than 200 wells were used to develop data analysis models, and the major characteristic parameters and their weightiness were determined through machine learning. Afterwards, the stimulation parameters of these wells, including injection rate, fluid volume, proppant volume, etc., were correlated with post-stimulation open flow capacity increments using several regression modeling methods, and the weightiness of these stimulation parameters was determined through machine learning. Cross validation method was used to choose the most accurate and stable model, which was then used to optimize the stimulation parameters of new wells. The model is applied to two test wells. The stimulation technologies and stimulation parameters of the two wells are optimized. Compared with the natural productivity, the productivity after stimulation was increased by 5.5 times and 21.5 times respectively. Machine learning algorithms are used to find an implicit rule from a large amount of data and express the rule with a high dimension nonlinear algorithm equation. It is very useful but seldom has applications in the area of reservoir stimulation. This paper found the controlling parameters of reservoir stimulation in Kuqa foreland area of Tarim Basin through machine learning and successfully used it in well productivity enhancement practices.
{"title":"Using Machine Learning Method to Optimize Well Stimulation Design in Heterogeneous Naturally Fractured Tight Reservoirs","authors":"Huifeng Liu, Longlian Cui, Zundou Liu, Chuanyi Zhou, Maotang Yao, Haoming Ma, Qi Liu","doi":"10.2118/208971-ms","DOIUrl":"https://doi.org/10.2118/208971-ms","url":null,"abstract":"\u0000 The reservoirs in Kuqa foreland area of Tarim Basin in China are ultra-deep HTHP (High Temperature and High Pressure) naturally fractured sandstone reservoirs. Due to low permeability of the matrix (<0.1mD), stimulation of the natural fractures is the key to well productivity enhancement. Different stimulation techniques with different stimulation strengths have been tried in the last decade, but stimulation effectiveness varied. Therefore, machine learning method is employed to identify the main controlling factors and optimize the well stimulation design.\u0000 Firstly, geological data, stimulation data, productivity data, etc. for more than 200 wells were used to develop data analysis models, and the major characteristic parameters and their weightiness were determined through machine learning. Afterwards, the stimulation parameters of these wells, including injection rate, fluid volume, proppant volume, etc., were correlated with post-stimulation open flow capacity increments using several regression modeling methods, and the weightiness of these stimulation parameters was determined through machine learning. Cross validation method was used to choose the most accurate and stable model, which was then used to optimize the stimulation parameters of new wells. The model is applied to two test wells. The stimulation technologies and stimulation parameters of the two wells are optimized. Compared with the natural productivity, the productivity after stimulation was increased by 5.5 times and 21.5 times respectively.\u0000 Machine learning algorithms are used to find an implicit rule from a large amount of data and express the rule with a high dimension nonlinear algorithm equation. It is very useful but seldom has applications in the area of reservoir stimulation. This paper found the controlling parameters of reservoir stimulation in Kuqa foreland area of Tarim Basin through machine learning and successfully used it in well productivity enhancement practices.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127046787","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}
Canada's oil sands production and upgrading industry have plans for a regional CO2 pipeline, enabling carbon capture and sequestration (CCS) solutions for reducing industry CO2 emissions. To evaluate the relative merits of carbon capture solutions, a case study is developed of three hypothetical carbon capture facilities: one post-combustion from a SAGD facility, a second post-combustion from an upgrader hydrogen plant, and a third pre-combustion from an upgrader hydrogen plant. All cases are based on process configurations of commercially proven technologies. Capital costs are developed for each of the cases based on Fluor process expertise and historical cost data for the oil sands region. Process and utility balances are developed to inform net carbon intensity reductions along with operating costs. The study includes a discussion of the influencing factors to CCS economics, including looking at the carbon footprint balance of production and upgrading operations, the existing utility profile, economies of scale, and carbon lifecycle impacts of choices. In addition to net carbon avoidance from a $CAD/ton CO2 perspective, the results also inform on relative merits of carbon intensity reduction of produced liquid fuels which generate carbon credits and revenue under the Canadian Clean Fuel Standard (CFS). Consideration of both carbon tax avoidance and fuel carbon intensity needs to be considered to justify the capital investment.
{"title":"Study of Carbon Capture in Oil Sands Production and Upgrading","authors":"Jon Isley, Matthew Gutscher, Benjamin Henezi","doi":"10.2118/208941-ms","DOIUrl":"https://doi.org/10.2118/208941-ms","url":null,"abstract":"\u0000 Canada's oil sands production and upgrading industry have plans for a regional CO2 pipeline, enabling carbon capture and sequestration (CCS) solutions for reducing industry CO2 emissions. To evaluate the relative merits of carbon capture solutions, a case study is developed of three hypothetical carbon capture facilities: one post-combustion from a SAGD facility, a second post-combustion from an upgrader hydrogen plant, and a third pre-combustion from an upgrader hydrogen plant. All cases are based on process configurations of commercially proven technologies. Capital costs are developed for each of the cases based on Fluor process expertise and historical cost data for the oil sands region. Process and utility balances are developed to inform net carbon intensity reductions along with operating costs. The study includes a discussion of the influencing factors to CCS economics, including looking at the carbon footprint balance of production and upgrading operations, the existing utility profile, economies of scale, and carbon lifecycle impacts of choices. In addition to net carbon avoidance from a $CAD/ton CO2 perspective, the results also inform on relative merits of carbon intensity reduction of produced liquid fuels which generate carbon credits and revenue under the Canadian Clean Fuel Standard (CFS). Consideration of both carbon tax avoidance and fuel carbon intensity needs to be considered to justify the capital investment.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120817026","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}
Die Hu, Zhengdong Lei, S. Cartwright, S. Samoil, Siqi Xie, Zhangxin Chen
Refracturing candidate selection problems can be solved via production statistics, virtual intelligence and type-curve matching, and these methods are mostly developed using data-based models. They unleash great power of data but have not considered the influence of geological distributions in physics-based models. This paper combines the strengths of data and physics based models and proposes a hybrid analysis method to improve and strengthen the current methods. Three criteria, production performance, a completion index and a geological distribution around an offset well, and their sub-criteria are selected to build an evaluation system for refracturing candidate wells. Field data is collected and processed to calculate a completion index and production performance. To quantify a geological distribution around a well, a history-matched reservoir simulation model is required. Besides, a graph theory algorithm, Dijkstra’s shortest path, is used to quantify the influence of geological distributions in 3D reservoir models on wells. An analytic hierarchy process and grey correlation analysis are then used to establish a multi-level evaluation system and determine and rank each individual strategic factor. Finally, datapoints are shown in a 3D coordinate system, and custom defined weights are used to calculate the final ranking of potential refracturing wells. In addition, the hybrid analysis is presented on our self-developed visualization platform. A history-matched reservoir simulation model from the Y284 tight oil reservoir is used as a study case. Eight refractured wells’ data is collected and analyzed. As a grey correlation analysis result, a sub-criteron of productivity performance, relative productivity, ranks the first, followed by cumulative liquid production. Completion and resistance rank third and fourth with a small gap. Based on the analysis results, an evaluation system is built up. 14 refracturing candidate wells are analyzed and ranked using the evaluation system. These wells are displayed in a 3D coordinate system, where x, y and z directions represent three criteria separately. Wells distributed in the first quadrant are regarded as optimum candidates to apply refracturing treatments. Correlations of evaluation factors and increased oil production after refracturing treatment are plotted to validate the method. This study explores how to conduct hybrid analysis in a selection workflow of refracturing candidate wells. Combing visualization, interpretability, robust foundation and understanding of reservoir models with accuracy and efficiency, data-driven artificial intelligence algorithms, the experiences distilled, and insights gained from this project show great potential to apply hybrid analysis as well as modelling in oil and gas industry.
{"title":"Refracturing Candidate Selection in Tight Oil Reservoirs Using Hybrid Analysis of Data and Physics Based Models","authors":"Die Hu, Zhengdong Lei, S. Cartwright, S. Samoil, Siqi Xie, Zhangxin Chen","doi":"10.2118/208883-ms","DOIUrl":"https://doi.org/10.2118/208883-ms","url":null,"abstract":"\u0000 Refracturing candidate selection problems can be solved via production statistics, virtual intelligence and type-curve matching, and these methods are mostly developed using data-based models. They unleash great power of data but have not considered the influence of geological distributions in physics-based models. This paper combines the strengths of data and physics based models and proposes a hybrid analysis method to improve and strengthen the current methods.\u0000 Three criteria, production performance, a completion index and a geological distribution around an offset well, and their sub-criteria are selected to build an evaluation system for refracturing candidate wells. Field data is collected and processed to calculate a completion index and production performance. To quantify a geological distribution around a well, a history-matched reservoir simulation model is required. Besides, a graph theory algorithm, Dijkstra’s shortest path, is used to quantify the influence of geological distributions in 3D reservoir models on wells. An analytic hierarchy process and grey correlation analysis are then used to establish a multi-level evaluation system and determine and rank each individual strategic factor. Finally, datapoints are shown in a 3D coordinate system, and custom defined weights are used to calculate the final ranking of potential refracturing wells. In addition, the hybrid analysis is presented on our self-developed visualization platform.\u0000 A history-matched reservoir simulation model from the Y284 tight oil reservoir is used as a study case. Eight refractured wells’ data is collected and analyzed. As a grey correlation analysis result, a sub-criteron of productivity performance, relative productivity, ranks the first, followed by cumulative liquid production. Completion and resistance rank third and fourth with a small gap. Based on the analysis results, an evaluation system is built up. 14 refracturing candidate wells are analyzed and ranked using the evaluation system. These wells are displayed in a 3D coordinate system, where x, y and z directions represent three criteria separately. Wells distributed in the first quadrant are regarded as optimum candidates to apply refracturing treatments. Correlations of evaluation factors and increased oil production after refracturing treatment are plotted to validate the method.\u0000 This study explores how to conduct hybrid analysis in a selection workflow of refracturing candidate wells. Combing visualization, interpretability, robust foundation and understanding of reservoir models with accuracy and efficiency, data-driven artificial intelligence algorithms, the experiences distilled, and insights gained from this project show great potential to apply hybrid analysis as well as modelling in oil and gas industry.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132146014","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}
Digital offshore operations and analysis rely on the deployment of downhole completion technologies that can produce significant quantities of data. Historically, downhole monitoring technologies, such as fiber optics and permanent downhole gauges, have been a good source of wellbore data for modeling and analysis. Permanent downhole monitoring technologies have benefitted from the advancement of high temperature electronics, reducing overall power consumption, and directly affecting sensor and electronics reliability and longevity. Through the utilization of telemetry schemes for addressability, permanent downhole monitoring technologies have also helped to develop electro-hydraulic and all-electric downhole flow control technologies, by enabling increased wellbore compartmentalization and fast control of multiple wellbore intervals. Advanced reservoir control systems have the ability to integrate to smart and data driven systems. They can be subdivided into extrinsic and intrinsic systems. Intrinsic systems benefit from having integrated monitoring technologies that can be addressed through telemetry schemes, which are also used to control multiple wellbore intervals. Examples of intrinsic systems include intrinsic electro-hydraulic systems and all-electric systems. To date, plenty of testing has been done with these types of intrinsic systems, but this paper highlights the evaluation of an intrinsic electro-hydraulic system. Ultimately, the authors believe that a stepwise approach through the implementation of hybrid-electric digital systems is key to the overall acceptance of all-electric systems. The success and reliability of electro-hydraulic systems will play a significant role in mass acceptance of all-electric systems in the oilfield. Electro-hydraulic systems are a good segway into all electric systems and give operators the chance to utilize some of the existing infrastructure while benefiting from some of the optimizations brought on by the Digital Oilfield.
{"title":"Advanced Reservoir Control Systems Paving the Way for Digital Offshore Operations and Analysis","authors":"Elias Garcia, K. Robertson","doi":"10.2118/208898-ms","DOIUrl":"https://doi.org/10.2118/208898-ms","url":null,"abstract":"\u0000 Digital offshore operations and analysis rely on the deployment of downhole completion technologies that can produce significant quantities of data. Historically, downhole monitoring technologies, such as fiber optics and permanent downhole gauges, have been a good source of wellbore data for modeling and analysis. Permanent downhole monitoring technologies have benefitted from the advancement of high temperature electronics, reducing overall power consumption, and directly affecting sensor and electronics reliability and longevity. Through the utilization of telemetry schemes for addressability, permanent downhole monitoring technologies have also helped to develop electro-hydraulic and all-electric downhole flow control technologies, by enabling increased wellbore compartmentalization and fast control of multiple wellbore intervals.\u0000 Advanced reservoir control systems have the ability to integrate to smart and data driven systems. They can be subdivided into extrinsic and intrinsic systems. Intrinsic systems benefit from having integrated monitoring technologies that can be addressed through telemetry schemes, which are also used to control multiple wellbore intervals. Examples of intrinsic systems include intrinsic electro-hydraulic systems and all-electric systems. To date, plenty of testing has been done with these types of intrinsic systems, but this paper highlights the evaluation of an intrinsic electro-hydraulic system.\u0000 Ultimately, the authors believe that a stepwise approach through the implementation of hybrid-electric digital systems is key to the overall acceptance of all-electric systems. The success and reliability of electro-hydraulic systems will play a significant role in mass acceptance of all-electric systems in the oilfield. Electro-hydraulic systems are a good segway into all electric systems and give operators the chance to utilize some of the existing infrastructure while benefiting from some of the optimizations brought on by the Digital Oilfield.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128682440","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}
Globally, the oil and gas industry, directly and indirectly, accounts for 42% of global emissions, according to a Mckinsey study. In Canada, the oil and gas industry is the single biggest source of Greenhouse Gas (GHG) emissions, contributing 10% to the country's total gas emissions. At the same time, the sector is crucial for Canada's growth, accounting for 5% of its GDP and generating employment for several thousands. It is then no surprise that the industry is under tremendous pressure to produce energy with reduced emissions. AI plays a pivotal role in helping the oil and gas industry to reduce their emissions. In fact, the WEF estimates that with AI the oil and gas industry can reduce 350 million tonnes of CO2 emissions and 800 million gallons of water consumed by 2025. When it comes to process and asset optimization, oil and gas companies can reduce greenhouse gas emissions by 20% with minimal capital investment. However, deploying AI is not without challenges. If not implemented properly, it can prevent the company from realizing the benefits of the deployment. In fact, Gartner says that 85% of the AI projects will continue to fail by 2022. World Economic Forum states that 36% of oil and gas companies have already invested in big data and analytics. However, only 13% use the insights from this technology to drive their approach towards the market and their competitors. Both of these point to companies applying the technology in a piecemeal manner and how a lack of lack of effective strategy can make it challenging to accomplish the desired goals. In this presentation, Humera Malik and Forogh Askari will outline the three-step plan for oil and gas companies to effectively deploy AI across their operations that augments their workforce with AI insights to accelerate their sustainability efforts in the race to net zero.
{"title":"Three Step Plan to Put Canada at the Front of the Petroleum Sector's Race to Net-Zero","authors":"Humera Malik, Forogh Askari","doi":"10.2118/208925-ms","DOIUrl":"https://doi.org/10.2118/208925-ms","url":null,"abstract":"\u0000 Globally, the oil and gas industry, directly and indirectly, accounts for 42% of global emissions, according to a Mckinsey study. In Canada, the oil and gas industry is the single biggest source of Greenhouse Gas (GHG) emissions, contributing 10% to the country's total gas emissions. At the same time, the sector is crucial for Canada's growth, accounting for 5% of its GDP and generating employment for several thousands. It is then no surprise that the industry is under tremendous pressure to produce energy with reduced emissions.\u0000 AI plays a pivotal role in helping the oil and gas industry to reduce their emissions. In fact, the WEF estimates that with AI the oil and gas industry can reduce 350 million tonnes of CO2 emissions and 800 million gallons of water consumed by 2025. When it comes to process and asset optimization, oil and gas companies can reduce greenhouse gas emissions by 20% with minimal capital investment.\u0000 However, deploying AI is not without challenges. If not implemented properly, it can prevent the company from realizing the benefits of the deployment. In fact, Gartner says that 85% of the AI projects will continue to fail by 2022. World Economic Forum states that 36% of oil and gas companies have already invested in big data and analytics. However, only 13% use the insights from this technology to drive their approach towards the market and their competitors. Both of these point to companies applying the technology in a piecemeal manner and how a lack of lack of effective strategy can make it challenging to accomplish the desired goals.\u0000 In this presentation, Humera Malik and Forogh Askari will outline the three-step plan for oil and gas companies to effectively deploy AI across their operations that augments their workforce with AI insights to accelerate their sustainability efforts in the race to net zero.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134321228","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}
Canada has joined the growing list of countries committed to achieving net zero emissions by 2050. This will require a rapid transition to carbon-free energy systems over the next three decades, with Carbon Capture, Utilization, and Storage (CCUS) a core component of unlocking Canada's decarbonization objectives. It is estimated that Canada will need to capture upwards of 100 million metric tonnes of CO2e per year through CCUS to achieve net zero by 2050. However, Canadian CCUS projects currently face a plethora of commercial hurdles, ranging from capital intensive technology, long investment time horizons, lack of clarity of government incentives and policies, and disjointed carbon markets. Carbon pricing policies are one lever to drive industry adoption of CCUS, but a cohesive industry and government collaboration is required to establish the national infrastructure needed to scale and support the development of CCUS in Canada. The recent announcement of the Oil Sands Pathways to Net Zero comprises of six oilsands producers, representing 90 percent of oilsands production, and signals a willingness of industry to come together with government to tackle these issues and support the oil sands industry which is projected to add $3 trillion to GDP by 2050. The central pillar of their vision is the use shared transportation infrastructure and storage hubs. This model will require significant government support but what is the right model to secure Canada's future while de-risking public funding. Policy development is still required by government bodies to encourage the investment in, and the implementation of these multibillion-dollar, long term projects. The announcement of a Canadian federal investment tax incentive and enforcement of the incoming clean fuel standard may further drive organizations to incorporate CCUS into their decarbonization plans. To proceed, industry will require further clarification to determine the effects of policy decisions and potential government partnerships will have on the cost structure and commercial viability of CCUS projects. This paper will outline some of the current commercial barriers that industry faces with the adoption of CCUS. It will provide a roadmap on how to mobilize and partner to scale CCUS in Canada.
{"title":"Carbon, Capture, Utilization and Storage CCUS: How to Commercialize a Business with No Revenue","authors":"Rohit Madhva Terdal, N. Steeghs, Craig Walter","doi":"10.2118/208905-ms","DOIUrl":"https://doi.org/10.2118/208905-ms","url":null,"abstract":"\u0000 Canada has joined the growing list of countries committed to achieving net zero emissions by 2050. This will require a rapid transition to carbon-free energy systems over the next three decades, with Carbon Capture, Utilization, and Storage (CCUS) a core component of unlocking Canada's decarbonization objectives.\u0000 It is estimated that Canada will need to capture upwards of 100 million metric tonnes of CO2e per year through CCUS to achieve net zero by 2050. However, Canadian CCUS projects currently face a plethora of commercial hurdles, ranging from capital intensive technology, long investment time horizons, lack of clarity of government incentives and policies, and disjointed carbon markets.\u0000 Carbon pricing policies are one lever to drive industry adoption of CCUS, but a cohesive industry and government collaboration is required to establish the national infrastructure needed to scale and support the development of CCUS in Canada.\u0000 The recent announcement of the Oil Sands Pathways to Net Zero comprises of six oilsands producers, representing 90 percent of oilsands production, and signals a willingness of industry to come together with government to tackle these issues and support the oil sands industry which is projected to add $3 trillion to GDP by 2050. The central pillar of their vision is the use shared transportation infrastructure and storage hubs. This model will require significant government support but what is the right model to secure Canada's future while de-risking public funding.\u0000 Policy development is still required by government bodies to encourage the investment in, and the implementation of these multibillion-dollar, long term projects. The announcement of a Canadian federal investment tax incentive and enforcement of the incoming clean fuel standard may further drive organizations to incorporate CCUS into their decarbonization plans. To proceed, industry will require further clarification to determine the effects of policy decisions and potential government partnerships will have on the cost structure and commercial viability of CCUS projects.\u0000 This paper will outline some of the current commercial barriers that industry faces with the adoption of CCUS. It will provide a roadmap on how to mobilize and partner to scale CCUS in Canada.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127655128","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 Chicontepec Paleochannel contains unconventional tight oil shaly sandstone reservoirs also characterized by natural fractures. Chicontepec ranks as a giant reservoir with volumes of Original-Oil-in-Place (OOIP) ranging between 137,300 and 59,000 MMbbls (Guzman, 2019). Although the cumulative oil is significant (440.38 MMbbls) it only represents 0.32 to 0.75% of the OOIP. The objective of this study is developing a new characterization methodology with a view to increase oil recovery from Chicontepec. OOIP in Chicontepec paleochannels was estimated originally at 137,300 MMbbls. Despite several studies using state of the art methodologies, contracting major oil field services companies to test new technologies, and significant investments, the OOIP was decreased recently to 59,000 MMbbls due to lack of any significant success on the implemented projects. This study shows that the key to success is understanding the contribution of natural fractures. This is demonstrated with a new dual porosity petrophysical model for naturally fractured laminar shaly sandstone reservoirs developed in this study. The model assumes that matrix and fractures are in parallel. Laminar shaliness is handled with a parameter (Alam) that is a function of true and shale resistivities, and fractional shale volume. The methodology integrates data from observations in outcrops, quantitative evaluation of cores, well logs and actual production data. Past Chicontepec studies have assumed that the porosity exponent (m) in Archie and shaly sandstone equations, is constant. However, core studies indicate that Chicontepec m values become smaller as porosity decreases. The proposed dual porosity petrophysical model, when applied to actual Chicontepec wells, matches properly the laboratory values of m, and generates results that compare well with actual production data, e.g., the larger the value of fracture partitioning the larger is the cumulative oil production. Pattern recognition allows estimating fracture intensity with a partitioning coefficient, which is calculated as the ratio of fracture porosity to total porosity. The novelty of this study is the development of a new petrophysical dual porosity model for naturally fractured shaly sandstone reservoirs that integrates variable values of m from cores, fracture intensity, and cumulative production of individual Chicontepec wells. Thirty-one wells have been evaluated with good results using the proposed model.
{"title":"Characterization of the Giant Chicontepec Tight Oil Paleochannel in Mexico and Integration With Actual Cumulative Oil Production","authors":"Alejandra Gutierrez Oseguera, R. Aguilera","doi":"10.2118/208888-ms","DOIUrl":"https://doi.org/10.2118/208888-ms","url":null,"abstract":"\u0000 The Chicontepec Paleochannel contains unconventional tight oil shaly sandstone reservoirs also characterized by natural fractures. Chicontepec ranks as a giant reservoir with volumes of Original-Oil-in-Place (OOIP) ranging between 137,300 and 59,000 MMbbls (Guzman, 2019). Although the cumulative oil is significant (440.38 MMbbls) it only represents 0.32 to 0.75% of the OOIP. The objective of this study is developing a new characterization methodology with a view to increase oil recovery from Chicontepec. OOIP in Chicontepec paleochannels was estimated originally at 137,300 MMbbls. Despite several studies using state of the art methodologies, contracting major oil field services companies to test new technologies, and significant investments, the OOIP was decreased recently to 59,000 MMbbls due to lack of any significant success on the implemented projects.\u0000 This study shows that the key to success is understanding the contribution of natural fractures. This is demonstrated with a new dual porosity petrophysical model for naturally fractured laminar shaly sandstone reservoirs developed in this study. The model assumes that matrix and fractures are in parallel. Laminar shaliness is handled with a parameter (Alam) that is a function of true and shale resistivities, and fractional shale volume.\u0000 The methodology integrates data from observations in outcrops, quantitative evaluation of cores, well logs and actual production data. Past Chicontepec studies have assumed that the porosity exponent (m) in Archie and shaly sandstone equations, is constant. However, core studies indicate that Chicontepec m values become smaller as porosity decreases. The proposed dual porosity petrophysical model, when applied to actual Chicontepec wells, matches properly the laboratory values of m, and generates results that compare well with actual production data, e.g., the larger the value of fracture partitioning the larger is the cumulative oil production. Pattern recognition allows estimating fracture intensity with a partitioning coefficient, which is calculated as the ratio of fracture porosity to total porosity.\u0000 The novelty of this study is the development of a new petrophysical dual porosity model for naturally fractured shaly sandstone reservoirs that integrates variable values of m from cores, fracture intensity, and cumulative production of individual Chicontepec wells. Thirty-one wells have been evaluated with good results using the proposed model.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131641521","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}
Routine core analyses of unconsolidated oil sands often yield unreliable and inconsistent porosity and permeability values due to the destruction of in situ textures or fabrics during core retrieval and sampling processes. To overcome the drawbacks of routine core analysis we developed a new method, namely "Cyclically Restored In Situ Petrophysics (CRISP)", for analysis of petrophysical properties of unconsolidated oil sand reservoirs. The new approach begins with a replication of in situ texture via cyclic compaction of unconsolidated oil sands in a uniaxial piston cell with incremental higher axial loadings that mimic historic overburden pressure cycling induced by glacial cycles through the Pleistocene. After the texture restoration, the sample is flooded in situ with various liquids and/or solvents and gases to obtain multiple porosity and permeability data points. Forward and backward flow can be applied to test permeability in both directions. After analysis the sample is dried, weighed, and the grains can be further analyzed for particle size distribution, mineralogy, or other parameters. The preliminary test program investigated the accuracy and precision of the new method (CRISP) and compared CRISP to the commonly-used sleeved-plug net overburden analysis (NOB) method. Results indicate that CRISP permeability measurements to simulated formation brine are highly repeatable, with variance of 0.71% (mDarcy) for a study of 531 samples from McMurray Formation, and of 0.15% (mDarcy) in a 150 sample Lloydminster Fm. study. For both sets of samples, the brine permeabilities range from 1 to 5000 mD. The preliminary results also show that CRISP outperforms the sleeved-plug net overburden method (NOB) in precision, with vastly better conformance between repeated samples, and also yields lower porosities that agree more closely with presumed in situ porosities given geological constraints and geophysical log data than the NOB method. Further, CRISP requires equivalent time for analysis as the NOB approach, and uses the same format of samples. CRISP therefore represents a significant improvement for petrophysical properties analysis in unconsolidated oil sand reservoirs for better and more realistic reservoir evaluation and subsequent engineering development.
{"title":"The Cyclically Restored in Situ Petrophysics CRISP Method for Analysis of Petrophysical Properties of Unconsolidated Oil Sands Reservoirs: Overview and Testing Update","authors":"G. Spray, X. Cui, Darcy Brabant","doi":"10.2118/208969-ms","DOIUrl":"https://doi.org/10.2118/208969-ms","url":null,"abstract":"\u0000 Routine core analyses of unconsolidated oil sands often yield unreliable and inconsistent porosity and permeability values due to the destruction of in situ textures or fabrics during core retrieval and sampling processes. To overcome the drawbacks of routine core analysis we developed a new method, namely \"Cyclically Restored In Situ Petrophysics (CRISP)\", for analysis of petrophysical properties of unconsolidated oil sand reservoirs. The new approach begins with a replication of in situ texture via cyclic compaction of unconsolidated oil sands in a uniaxial piston cell with incremental higher axial loadings that mimic historic overburden pressure cycling induced by glacial cycles through the Pleistocene. After the texture restoration, the sample is flooded in situ with various liquids and/or solvents and gases to obtain multiple porosity and permeability data points. Forward and backward flow can be applied to test permeability in both directions. After analysis the sample is dried, weighed, and the grains can be further analyzed for particle size distribution, mineralogy, or other parameters.\u0000 The preliminary test program investigated the accuracy and precision of the new method (CRISP) and compared CRISP to the commonly-used sleeved-plug net overburden analysis (NOB) method. Results indicate that CRISP permeability measurements to simulated formation brine are highly repeatable, with variance of 0.71% (mDarcy) for a study of 531 samples from McMurray Formation, and of 0.15% (mDarcy) in a 150 sample Lloydminster Fm. study. For both sets of samples, the brine permeabilities range from 1 to 5000 mD. The preliminary results also show that CRISP outperforms the sleeved-plug net overburden method (NOB) in precision, with vastly better conformance between repeated samples, and also yields lower porosities that agree more closely with presumed in situ porosities given geological constraints and geophysical log data than the NOB method. Further, CRISP requires equivalent time for analysis as the NOB approach, and uses the same format of samples. CRISP therefore represents a significant improvement for petrophysical properties analysis in unconsolidated oil sand reservoirs for better and more realistic reservoir evaluation and subsequent engineering development.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122103475","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}
Mohamad Mohamadi-Baghmolaei, Parviz Zahedizadeh, A. Hajizadeh, S. Zendehboudi
The massive potential in biomass gasification could satisfy the rising energy demand. A promising technology for sustainable hydrogen production is supercritical water gasification of biomass (SCWG). This study proposes a new model to assess gas yields and char formation through SCWG. To this end, a thermodynamic approach is utilized to model the reactor, assuming the equilibrium condition. The impact of catalyst on the SCWG is also involved in the new model, considering a deviation term for the Gibbs free energy of solid char. Two different feedstocks, including sunflower and corncob, are assessed toward SCWG. The newly developed model considerably improves gas yields and char formation predictions considering the experimental data. Compared to the non-modified modeling strategy, the sunflower and corncob's gas yield and char formation are improved by 85.37 and 62.52, respectively. The sensitivity results indicate that temperature and feed concentration substantially impact the gas yields and char formation, while pressure is less impactful.
{"title":"Hydrogen Production and Char Formation Assessment through Supercritival Gasification of Biomass","authors":"Mohamad Mohamadi-Baghmolaei, Parviz Zahedizadeh, A. Hajizadeh, S. Zendehboudi","doi":"10.2118/208914-ms","DOIUrl":"https://doi.org/10.2118/208914-ms","url":null,"abstract":"\u0000 The massive potential in biomass gasification could satisfy the rising energy demand. A promising technology for sustainable hydrogen production is supercritical water gasification of biomass (SCWG). This study proposes a new model to assess gas yields and char formation through SCWG. To this end, a thermodynamic approach is utilized to model the reactor, assuming the equilibrium condition. The impact of catalyst on the SCWG is also involved in the new model, considering a deviation term for the Gibbs free energy of solid char. Two different feedstocks, including sunflower and corncob, are assessed toward SCWG. The newly developed model considerably improves gas yields and char formation predictions considering the experimental data. Compared to the non-modified modeling strategy, the sunflower and corncob's gas yield and char formation are improved by 85.37 and 62.52, respectively. The sensitivity results indicate that temperature and feed concentration substantially impact the gas yields and char formation, while pressure is less impactful.","PeriodicalId":146458,"journal":{"name":"Day 1 Wed, March 16, 2022","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128639918","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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