M. F. Fathalla, M. A. Al Hosani, I. Mohamed, A. A. Al Bairaq, Djamal Kherroubi, A. Abdullayev, Allen Roopal
An onshore gas field contains several gas wells which have low–intermittent production rates. The poor production has been attributed to liquid loading issue in the wellbore. This study will investigate the impact of optimizing the tubing and liner completion design to improve the gas production rates from the wells. Numerous sensitivity runs are carried out with varying tubing and liner dimensions, to identity optimal downhole completions design. The study begins by identifying weak wells having severe gas production problems. Once the weak wells have been identified, wellbore schematics for those wells are studied. Simulation runs are performed with the current downhole completion design and this will be used as the base case. Several completion designs are considered to minimize the effect of liquid loading in the wells; these include reducing the tubing diameter but keeping the existing liner diameter the same, keeping the original tubing diameter the same but only reducing the liner diameter, extending the tubing to the Total Depth (TD) while keeping the original tubing diameter, and extending a reduced diameter tubing string to the TD. The primary cause of the liquid loading seems to be the reduced velocity of the incoming gas from the reservoir as it flows through the wellbore. A simulation study was performed using the various completion designs to optimize the well completion and achieve higher gas velocities in the weak wells. The results of the study showed significant improvement in gas production rates when the tubing diameter and liner diameter were reduced, providing further evidence that increased velocity of the incoming fluids due to restricted flow led to less liquid loading. The paper demonstrates the impact of downhole completion design on the productivity of the gas wells. The study shows that revisiting the existing completion designs and optimizing them using commercial simulators can lead to significant improvement in well production rates. It is also noted that restricting the flow near the sand face increases the velocity of the incoming fluid and reduces liquid loading in the wells.
{"title":"Optimizing Tubing and Liner Completion Design to Improve Gas Production from Existing Wells: Case Study for ADNOC Onshore Field Abu Dhabi, UAE","authors":"M. F. Fathalla, M. A. Al Hosani, I. Mohamed, A. A. Al Bairaq, Djamal Kherroubi, A. Abdullayev, Allen Roopal","doi":"10.2118/207430-ms","DOIUrl":"https://doi.org/10.2118/207430-ms","url":null,"abstract":"\u0000 An onshore gas field contains several gas wells which have low–intermittent production rates. The poor production has been attributed to liquid loading issue in the wellbore. This study will investigate the impact of optimizing the tubing and liner completion design to improve the gas production rates from the wells. Numerous sensitivity runs are carried out with varying tubing and liner dimensions, to identity optimal downhole completions design.\u0000 The study begins by identifying weak wells having severe gas production problems. Once the weak wells have been identified, wellbore schematics for those wells are studied. Simulation runs are performed with the current downhole completion design and this will be used as the base case. Several completion designs are considered to minimize the effect of liquid loading in the wells; these include reducing the tubing diameter but keeping the existing liner diameter the same, keeping the original tubing diameter the same but only reducing the liner diameter, extending the tubing to the Total Depth (TD) while keeping the original tubing diameter, and extending a reduced diameter tubing string to the TD.\u0000 The primary cause of the liquid loading seems to be the reduced velocity of the incoming gas from the reservoir as it flows through the wellbore. A simulation study was performed using the various completion designs to optimize the well completion and achieve higher gas velocities in the weak wells. The results of the study showed significant improvement in gas production rates when the tubing diameter and liner diameter were reduced, providing further evidence that increased velocity of the incoming fluids due to restricted flow led to less liquid loading.\u0000 The paper demonstrates the impact of downhole completion design on the productivity of the gas wells. The study shows that revisiting the existing completion designs and optimizing them using commercial simulators can lead to significant improvement in well production rates. It is also noted that restricting the flow near the sand face increases the velocity of the incoming fluid and reduces liquid loading in the wells.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83802272","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}
Oscar M. Molina, C. Mejia, M. Tyagi, F. Medellin, H. Elshahawi, Kumar Sujatha
The geothermal energy industry has never quite realized its true potential despite the seemingly magical promise of nonstop, 24/7 renewable energy sitting just below the surface of the Earth. In this paper, we discuss an integrated cloud-based workflow aimed at evaluating the cost-effectiveness of adopting geothermal production in low to medium enthalpy systems by either repurposing existing oil and gas wells or by co-producing thermal and fossil energy. The workflow introduces an automated and intrinsically secure decision-making process to convert mature oil and gas wells into geothermal wells, enabling both operational and financial assessment of the conversion process, whether partial or complete. The proposed workflow focuses on the reliability and transparency of fully automated technical processes for the geological, hydrodynamic, and mechanical configuration of the production system to ensure the financial success of the conversion project, in terms of heat production potential and cost of development. The decision-making portion of the workflow comprises the technical, social, environmental factors driving the return on investment for the total or partial conversion of wells to geothermal production. These components are evaluated using artificial intelligence (AI) algorithms that reduce bias in the decision-making process. The automated workflow involves assessment of the following: Heat Potential: A data-driven model to determine the geothermal heat potential using geological conditions from basin modeling and data from offset wells.Flow Modeling: An ultra-fast, physics-based modeling approach to determine pressure and temperature changes along wellbores to model fluid flow potential, thermal flux, and injection operations.Mechanical Integrity: Casing and completions integrity and configuration are embedded in the process for flow rates modeling.Environmental, Social, and Governance (ESG): A decision modeling framework is setup to ensure the transparent validation of the technical components and ESG factors, including potential for water pollution, carbon emissions, and social factors such as induced seismicity and ambient noise levels The assurance of key ESG metrics will ensure a viable and sustainable transition into a globally available low-carbon source of energy such as geothermal. Our novel cloud- based automated decision-making environment incorporates a blockchain framework to ensure transparency of technical-related processes and tasks, driving the financial success of the conversion project. Ultimately, our automated workflow is designed to encourage and support the widespread adoption of low-carbon energy in the oil and gas industry.
{"title":"Geothermal Production from Existing Oil and Gas Wells: A Sustainable Repurposing Model","authors":"Oscar M. Molina, C. Mejia, M. Tyagi, F. Medellin, H. Elshahawi, Kumar Sujatha","doi":"10.2118/207801-ms","DOIUrl":"https://doi.org/10.2118/207801-ms","url":null,"abstract":"\u0000 The geothermal energy industry has never quite realized its true potential despite the seemingly magical promise of nonstop, 24/7 renewable energy sitting just below the surface of the Earth. In this paper, we discuss an integrated cloud-based workflow aimed at evaluating the cost-effectiveness of adopting geothermal production in low to medium enthalpy systems by either repurposing existing oil and gas wells or by co-producing thermal and fossil energy. The workflow introduces an automated and intrinsically secure decision-making process to convert mature oil and gas wells into geothermal wells, enabling both operational and financial assessment of the conversion process, whether partial or complete.\u0000 The proposed workflow focuses on the reliability and transparency of fully automated technical processes for the geological, hydrodynamic, and mechanical configuration of the production system to ensure the financial success of the conversion project, in terms of heat production potential and cost of development. The decision-making portion of the workflow comprises the technical, social, environmental factors driving the return on investment for the total or partial conversion of wells to geothermal production. These components are evaluated using artificial intelligence (AI) algorithms that reduce bias in the decision-making process. The automated workflow involves assessment of the following: Heat Potential: A data-driven model to determine the geothermal heat potential using geological conditions from basin modeling and data from offset wells.Flow Modeling: An ultra-fast, physics-based modeling approach to determine pressure and temperature changes along wellbores to model fluid flow potential, thermal flux, and injection operations.Mechanical Integrity: Casing and completions integrity and configuration are embedded in the process for flow rates modeling.Environmental, Social, and Governance (ESG): A decision modeling framework is setup to ensure the transparent validation of the technical components and ESG factors, including potential for water pollution, carbon emissions, and social factors such as induced seismicity and ambient noise levels\u0000 The assurance of key ESG metrics will ensure a viable and sustainable transition into a globally available low-carbon source of energy such as geothermal. Our novel cloud- based automated decision-making environment incorporates a blockchain framework to ensure transparency of technical-related processes and tasks, driving the financial success of the conversion project. Ultimately, our automated workflow is designed to encourage and support the widespread adoption of low-carbon energy in the oil and gas industry.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82383939","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}
While many companies have embarked on their digital transformation journeys implementing different forms of "Digital Twins" to cover specific business processes and challenges, their main challenge has been integrating these disparate Digital Twin projects into one single combined view to create significant new value and competitive advantage for the business. To be effective, the Digital Twin needs to be capable of supporting the entire asset lifecycle from the early phases of a capital project to operations and maintenance up to asset retirement, leveraging the same data in a platform able to support the end-to-end process. This paper looks at several approaches, which large owner operators at different levels of organizational information management maturity have used to build their Enterprise Scale Digital Twin strategies. It uses the lessons learned to highlight the successes and failures of these strategies and recommended approaches going forward. The results observed, identify that whilst there is a reasonably standard roadmap for approaching the development of Digital Twins most customers begin at different points along that journey. It also highlights that the end goal may not be the same across an Enterprise and that by taking the development of a Digital Twin as a series of incremental steps, independent of the starting point, serves to accelerate the journey by driving an increase in the organizational maturity in terms of People, Process and Technology and an improvement in data quality. One of the key components in any strategy was the ability to manage the information standard for the Digital Twin at an Enterprise level, for both greenfield and brownfield organizations and assets. The paper concludes the benefits of technical and commercial scalability and the requirements to get a solid manageable and trustworthy core of information should be at the heart of any Enterprise-wide Digital Twin strategy. This is contrary to the common approach of building a single detailed Proof of Concept (PoC) addressing as many use cases as possible and then templatizing that as an approach to repeat around the Enterprise, which often leads to failure on additional deployments where the maturity and challenges are different.
{"title":"Success Factors of an Enterprise-Wide Digital Twin Strategy","authors":"Steve Parvin","doi":"10.2118/207248-ms","DOIUrl":"https://doi.org/10.2118/207248-ms","url":null,"abstract":"\u0000 While many companies have embarked on their digital transformation journeys implementing different forms of \"Digital Twins\" to cover specific business processes and challenges, their main challenge has been integrating these disparate Digital Twin projects into one single combined view to create significant new value and competitive advantage for the business.\u0000 To be effective, the Digital Twin needs to be capable of supporting the entire asset lifecycle from the early phases of a capital project to operations and maintenance up to asset retirement, leveraging the same data in a platform able to support the end-to-end process.\u0000 This paper looks at several approaches, which large owner operators at different levels of organizational information management maturity have used to build their Enterprise Scale Digital Twin strategies.\u0000 It uses the lessons learned to highlight the successes and failures of these strategies and recommended approaches going forward. The results observed, identify that whilst there is a reasonably standard roadmap for approaching the development of Digital Twins most customers begin at different points along that journey.\u0000 It also highlights that the end goal may not be the same across an Enterprise and that by taking the development of a Digital Twin as a series of incremental steps, independent of the starting point, serves to accelerate the journey by driving an increase in the organizational maturity in terms of People, Process and Technology and an improvement in data quality. One of the key components in any strategy was the ability to manage the information standard for the Digital Twin at an Enterprise level, for both greenfield and brownfield organizations and assets.\u0000 The paper concludes the benefits of technical and commercial scalability and the requirements to get a solid manageable and trustworthy core of information should be at the heart of any Enterprise-wide Digital Twin strategy. This is contrary to the common approach of building a single detailed Proof of Concept (PoC) addressing as many use cases as possible and then templatizing that as an approach to repeat around the Enterprise, which often leads to failure on additional deployments where the maturity and challenges are different.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83818504","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. Figueroa, Gustavo Mejías, J. Frias, Bonifacio Brito, Diana L. Velázquez, Carmen J. Ramírez, F. Téllez, Juan Briceño, J. Salas, Ángel Olivares, Georgina Olán, Andrés Flores, Jesús Arroyo, John E. Busteed, René Hernández, J. Gonzalez
Enhanced hydrocarbon production in a high-pressure/high-temperature (HP/HT) carbonate reservoir, involves generating highly conductive channels using efficient diversion techniques and custom-designed acid-based fluid systems. Advanced stimulation design includes injection of different reactive fluids, which involves challenges associated with controlling fluid leak-off, implementing optimal diversion techniques, controlling acid reaction rates to withstand high-temperature conditions, and designing appropriate pumping schedules to increase well productivity and sustainability of its production through efficient acid etching and uniform fluid distribution in the pay zone. Laboratory tests such as rock mineralogy, acid etching on core samples and solubility tests on formation cuttings were performed to confirm rock dissolving capability, and to identify stimulation fluids that could generate optimal fracture lengths and maximus etching in the zone of interest while corrosion test was run to ensure corrosion control at HT conditions. After analyzing laboratory tests results, acid fluid systems were selected together with a self-crosslinking acid system for its diversion properties. In addition, customized pumping schedule was constructed using acid fracturing and diverting simulators and based on optimal conductivity/productivity results fluid stages number and sequence, flow rates and acid volumes were selected. The engineered acid treatment generated a network of conductive fractures that resulted in a significant improvement over initial production rate. Diverting agent efficiency was observed during pumping treatment by a 1,300 psi increase in surface pressures when the diverting agent entered the formation. Oil production increased from 648.7 to 3105.89 BPD, and gas production increased from 4.9 to 26.92 MMSCFD. This success results demonstrates that engineering design coupled with laboratory tailor fluids designs, integrated with a flawless execution, are the key to a successful stimulation. This paper describes the details of acidizing technique, treatment design and lessons learned during execution and results.
{"title":"Using Advance Acid Fracturing Design to Increase the Production Efficiency in a HPHT Reservoir: A Success Story from Southern Mexico","authors":"F. Figueroa, Gustavo Mejías, J. Frias, Bonifacio Brito, Diana L. Velázquez, Carmen J. Ramírez, F. Téllez, Juan Briceño, J. Salas, Ángel Olivares, Georgina Olán, Andrés Flores, Jesús Arroyo, John E. Busteed, René Hernández, J. Gonzalez","doi":"10.2118/207332-ms","DOIUrl":"https://doi.org/10.2118/207332-ms","url":null,"abstract":"\u0000 Enhanced hydrocarbon production in a high-pressure/high-temperature (HP/HT) carbonate reservoir, involves generating highly conductive channels using efficient diversion techniques and custom-designed acid-based fluid systems. Advanced stimulation design includes injection of different reactive fluids, which involves challenges associated with controlling fluid leak-off, implementing optimal diversion techniques, controlling acid reaction rates to withstand high-temperature conditions, and designing appropriate pumping schedules to increase well productivity and sustainability of its production through efficient acid etching and uniform fluid distribution in the pay zone.\u0000 Laboratory tests such as rock mineralogy, acid etching on core samples and solubility tests on formation cuttings were performed to confirm rock dissolving capability, and to identify stimulation fluids that could generate optimal fracture lengths and maximus etching in the zone of interest while corrosion test was run to ensure corrosion control at HT conditions. After analyzing laboratory tests results, acid fluid systems were selected together with a self-crosslinking acid system for its diversion properties. In addition, customized pumping schedule was constructed using acid fracturing and diverting simulators and based on optimal conductivity/productivity results fluid stages number and sequence, flow rates and acid volumes were selected.\u0000 The engineered acid treatment generated a network of conductive fractures that resulted in a significant improvement over initial production rate. Diverting agent efficiency was observed during pumping treatment by a 1,300 psi increase in surface pressures when the diverting agent entered the formation. Oil production increased from 648.7 to 3105.89 BPD, and gas production increased from 4.9 to 26.92 MMSCFD. This success results demonstrates that engineering design coupled with laboratory tailor fluids designs, integrated with a flawless execution, are the key to a successful stimulation. This paper describes the details of acidizing technique, treatment design and lessons learned during execution and results.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84551140","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}
Stress-dependence of reservoir matrix and fractures can strongly affect the performance of multifractured horizontal wells (MFHWs) completed in unconventional hydrocarbon reservoirs. In order to model fluid flow in unconventional reservoirs exhibiting this stress-dependence, most traditional reservoir flow simulators, and many simulators described in published work, use conventional reservoir fluid flow model formulations. These formulations typically neglect the influence of the rate of change of volumetric strain of the reservoir matrix and fractures, even though reservoir stress and pressure change significantly during the course of production. As a result, the effect of matrix and fracture deformation on production is neglected, which can lead to errors in predicting production performance in most stress-sensitive reservoirs. To address this problem, some studies have proposed the use of porosity and transmissibility multipliers to model stress-sensitive reservoirs. However, in order to apply this approach, multipliers must be estimated from laboratory experiments, or used as a history-match parameter, possibly resulting in large errors in well performance predictions. Alternatively, fully-coupled, fully numerical geomechanical simulation can be performed, but these methods are computationally costly, and models are difficult to setup. This paper presents a new fully-coupled, two-way analytical modeling approach that can be used to simulate fluid flow in stress-sensitive unconventional reservoirs produced through MFHWs. The model couples poroelastic geomechanics theory with fluid flow formulations. The two-way coupled fluid flow-geomechanical analytical model is applied simultaneously to both the matrix and fracture regions. In the proposed algorithm, a porosity-compressibility coupling parameter for the two physical models is setup to update the stress- and pressure-dependent fracture/matrix properties iteratively, which are later used as input data for the fracture-matrix reservoir fluid flow model at each iteration step. The analytical approach developed for the fully-coupled, two-way analytical model, using the enhanced fracture region conceptual model, is validated by comparing the results with numerical simulation. Predictions using the fully-coupled enhanced fracture region model are then compared with the same enhanced fracture region model but with the conventional pressure-dependent modeling approach implemented. A sensitivity study performed by comparing the new fully-coupled model predictions with and without geomechanics effects accounted for reveals that, without geomechanics effects, production performance in stress-sensitive reservoirs might be overestimated. The study also demonstrates that use of the conventional stress-dependent modeling approach may cause production performance to be underestimated. Therefore, the proposed fully-coupled, two-way analytical model can be useful for practical engineering purposes.
{"title":"A Semi-Analytical Geomechanical Approach for Forecasting Production Performance in Multifractured Composite Systems","authors":"A. B. Lamidi, C. Clarkson","doi":"10.2118/208154-ms","DOIUrl":"https://doi.org/10.2118/208154-ms","url":null,"abstract":"\u0000 Stress-dependence of reservoir matrix and fractures can strongly affect the performance of multifractured horizontal wells (MFHWs) completed in unconventional hydrocarbon reservoirs. In order to model fluid flow in unconventional reservoirs exhibiting this stress-dependence, most traditional reservoir flow simulators, and many simulators described in published work, use conventional reservoir fluid flow model formulations. These formulations typically neglect the influence of the rate of change of volumetric strain of the reservoir matrix and fractures, even though reservoir stress and pressure change significantly during the course of production. As a result, the effect of matrix and fracture deformation on production is neglected, which can lead to errors in predicting production performance in most stress-sensitive reservoirs. To address this problem, some studies have proposed the use of porosity and transmissibility multipliers to model stress-sensitive reservoirs. However, in order to apply this approach, multipliers must be estimated from laboratory experiments, or used as a history-match parameter, possibly resulting in large errors in well performance predictions. Alternatively, fully-coupled, fully numerical geomechanical simulation can be performed, but these methods are computationally costly, and models are difficult to setup.\u0000 This paper presents a new fully-coupled, two-way analytical modeling approach that can be used to simulate fluid flow in stress-sensitive unconventional reservoirs produced through MFHWs. The model couples poroelastic geomechanics theory with fluid flow formulations. The two-way coupled fluid flow-geomechanical analytical model is applied simultaneously to both the matrix and fracture regions. In the proposed algorithm, a porosity-compressibility coupling parameter for the two physical models is setup to update the stress- and pressure-dependent fracture/matrix properties iteratively, which are later used as input data for the fracture-matrix reservoir fluid flow model at each iteration step.\u0000 The analytical approach developed for the fully-coupled, two-way analytical model, using the enhanced fracture region conceptual model, is validated by comparing the results with numerical simulation. Predictions using the fully-coupled enhanced fracture region model are then compared with the same enhanced fracture region model but with the conventional pressure-dependent modeling approach implemented. A sensitivity study performed by comparing the new fully-coupled model predictions with and without geomechanics effects accounted for reveals that, without geomechanics effects, production performance in stress-sensitive reservoirs might be overestimated. The study also demonstrates that use of the conventional stress-dependent modeling approach may cause production performance to be underestimated. Therefore, the proposed fully-coupled, two-way analytical model can be useful for practical engineering purposes.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"13 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"88468508","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}
Many oil & gas companies embarked on their Digital Transformation (DX) journeys with rapid adoption of emerging digital technologies. Successful digital transformation initiatives are necessary for Oil/Gas Companies to improve efficiencies, streamline their operations and meet pressing challenges for cost reduction, increased efficiency, and improved safety. Oil & Gas fields and processing facilities require robust and reliable telecommunication infrastructure to support the application of much needed digital technologies. The availability of high-speed connectivity has been a challenge for many Oil/Gas companies operating in remote or hazardous locations. The latest Fifth Generation cellular technology (5 G) addresses such essential Oil/Gas requirements as increased speeds/bandwidths, very low network latencies, ultra-reliable communications, and the capacity to handle large number of users. 5G is designed around following technologies: Small (micro) cells requiring less power,Higher frequencies offering bigger data handling capacities,Cloud and Edge Computing for low network latencies and added security. These 5G design features will enable numerous Oil/Gas applications such as Industrial Robots/Drones, Virtual/Augmented Reality, Video Surveillance with Artificial Intelligence (AI) features (Face Recognition, Object Recognition & intelligent Image processing), Remote Asset Management, Industrial IoT communication between Sensors, Gateways and Device Controllers, Pipeline Leak Detection Systems, Telemetry and SCADA. 5G is therefore poised to be a key enabler in Digital Transformation. The UAE has an advanced telecom infrastructure that offers customers high speed fibre optic connectivity. UAE telecom operators have also been quick to deploy 5G in main cities. Etisalat for example is running a 5G pilot on Das Island with ADNOC Offshore and a major Network Supplier to test potential use cases of the technology in the O&G industry. The push for 5G in O&G will benefit from cooperation between the O&G industry, the telecom operators and technology providers. The role of Governments and the Telecom Regulators can further accelerate adoption through allocation of frequency spectrum to enable 5G Private Network model of deployment. The private network model is crucial for the Oil & Gas industry in view of the special requirements, nature and remoteness of oil and gas installations.
{"title":"The Use of 5G Technologies in the Digital Transformation of the Oil/Gas Industry","authors":"Sadik Jadir Al-Jadir","doi":"10.2118/207529-ms","DOIUrl":"https://doi.org/10.2118/207529-ms","url":null,"abstract":"\u0000 Many oil & gas companies embarked on their Digital Transformation (DX) journeys with rapid adoption of emerging digital technologies. Successful digital transformation initiatives are necessary for Oil/Gas Companies to improve efficiencies, streamline their operations and meet pressing challenges for cost reduction, increased efficiency, and improved safety.\u0000 Oil & Gas fields and processing facilities require robust and reliable telecommunication infrastructure to support the application of much needed digital technologies. The availability of high-speed connectivity has been a challenge for many Oil/Gas companies operating in remote or hazardous locations. The latest Fifth Generation cellular technology (5 G) addresses such essential Oil/Gas requirements as increased speeds/bandwidths, very low network latencies, ultra-reliable communications, and the capacity to handle large number of users.\u0000 5G is designed around following technologies: Small (micro) cells requiring less power,Higher frequencies offering bigger data handling capacities,Cloud and Edge Computing for low network latencies and added security.\u0000 These 5G design features will enable numerous Oil/Gas applications such as Industrial Robots/Drones, Virtual/Augmented Reality, Video Surveillance with Artificial Intelligence (AI) features (Face Recognition, Object Recognition & intelligent Image processing), Remote Asset Management, Industrial IoT communication between Sensors, Gateways and Device Controllers, Pipeline Leak Detection Systems, Telemetry and SCADA. 5G is therefore poised to be a key enabler in Digital Transformation.\u0000 The UAE has an advanced telecom infrastructure that offers customers high speed fibre optic connectivity. UAE telecom operators have also been quick to deploy 5G in main cities. Etisalat for example is running a 5G pilot on Das Island with ADNOC Offshore and a major Network Supplier to test potential use cases of the technology in the O&G industry.\u0000 The push for 5G in O&G will benefit from cooperation between the O&G industry, the telecom operators and technology providers. The role of Governments and the Telecom Regulators can further accelerate adoption through allocation of frequency spectrum to enable 5G Private Network model of deployment. The private network model is crucial for the Oil & Gas industry in view of the special requirements, nature and remoteness of oil and gas installations.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"39 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72885830","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}
C. Jacquemyn, G. Hampson, M. Jackson, D. Petrovskyy, S. Geiger, J. M. Machado Silva, S. Judice, F. Rahman, M. Sousa
Rapid Reservoir Modelling (RRM) is a software tool that combines geological operators and a flow diagnostics module with sketch-based interface and modelling technology. The geological operators account for all interactions of stratigraphic surfaces and ensure that the resulting 3D models are stratigraphically valid. The geological operators allow users to sketch in any order, from oldest to youngest, from large to small, or free of any prescribed order, depending on data-driven or concept-driven uncertainty in interpretation. Flow diagnostics assessment of the sketched models enforces the link between geological interpretation and flow behaviour without using time-consuming and computationally expensive workflows. Output of RRM models includes static measures of facies architecture, flow diagnostics and model elements that can be exported to industry-standard software. A deep-water case is presented to show how assessing the impact of different scenarios at a prototyping stage allows users to make informed decisions about subsequent modelling efforts and approaches. Furthermore, RRM provides a valuable method for training or to develop geological interpretation skills, in front of an outcrop or directly on subsurface data.
{"title":"Rapid Reservoir Modelling: Sketch-Based Geological Modelling with Fast Flow Diagnostics","authors":"C. Jacquemyn, G. Hampson, M. Jackson, D. Petrovskyy, S. Geiger, J. M. Machado Silva, S. Judice, F. Rahman, M. Sousa","doi":"10.2118/208041-ms","DOIUrl":"https://doi.org/10.2118/208041-ms","url":null,"abstract":"\u0000 Rapid Reservoir Modelling (RRM) is a software tool that combines geological operators and a flow diagnostics module with sketch-based interface and modelling technology. The geological operators account for all interactions of stratigraphic surfaces and ensure that the resulting 3D models are stratigraphically valid. The geological operators allow users to sketch in any order, from oldest to youngest, from large to small, or free of any prescribed order, depending on data-driven or concept-driven uncertainty in interpretation. Flow diagnostics assessment of the sketched models enforces the link between geological interpretation and flow behaviour without using time-consuming and computationally expensive workflows. Output of RRM models includes static measures of facies architecture, flow diagnostics and model elements that can be exported to industry-standard software. A deep-water case is presented to show how assessing the impact of different scenarios at a prototyping stage allows users to make informed decisions about subsequent modelling efforts and approaches. Furthermore, RRM provides a valuable method for training or to develop geological interpretation skills, in front of an outcrop or directly on subsurface data.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"43 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80945454","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}
Subba Ramarao Rachapudi Venkata, N. Reddicharla, S. Alshehhi, Indra Utama, S. A. Al Nuimi, Dávid Gönczi, Oussema Toumi, Eleonora Pechorskaya, Georg Schweiger, Franz Führer
Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability. The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation. The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process. The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation. A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and
{"title":"Artificial Intelligence Assisted Well Portfolio Optimization - An Automated Reservoir Management Advisory System to Maximize the Asset Value - Case Study from ADNOC Onshore","authors":"Subba Ramarao Rachapudi Venkata, N. Reddicharla, S. Alshehhi, Indra Utama, S. A. Al Nuimi, Dávid Gönczi, Oussema Toumi, Eleonora Pechorskaya, Georg Schweiger, Franz Führer","doi":"10.2118/207274-ms","DOIUrl":"https://doi.org/10.2118/207274-ms","url":null,"abstract":"\u0000 Matured hydrocarbon fields are continuously deteriorating and selection of well interventions turn into critical task with an objective of achieving higher business value. Time consuming simulation models and classical decision-making approach making it difficult to rapidly identify the best underperforming, potential rig and rig-less candidates. Therefore, the objective of this paper is to demonstrate the automated solution with data driven machine learning (ML) & AI assisted workflows to prioritize the intervention opportunities that can deliver higher sustainable oil rate and profitability.\u0000 The solution consists of establishing a customized database using inputs from various sources including production & completion data, flat files and simulation models. Automation of Data gathering along with technical and economical calculations were implemented to overcome the repetitive and less added value tasks. Second layer of solution includes configuration of tailor-made workflows to conduct the analysis of well performance, logs, output from simulation models (static reservoir model, well models) along with historical events. Further these workflows were combination of current best practices of an integrated assessment of subsurface opportunities through analytical computations along with machine learning driven techniques for ranking the well intervention opportunities with consideration of complexity in implementation.\u0000 The automated process outcome is a comprehensive list of future well intervention candidates like well conversion to gas lift, water shutoff, stimulation and nitrogen kick-off opportunities. The opportunity ranking is completed with AI assisted supported scoring system that takes input from technical, financial and implementation risk scores. In addition, intuitive dashboards are built and tailored with the involvement of management and engineering departments to track the opportunity maturation process.\u0000 The advisory system has been implemented and tested in a giant mature field with over 300 wells. The solution identified more techno-economical feasible opportunities within hours instead of weeks or months with reduced risk of failure resulting into an improved economic success rate. The first set of opportunities under implementation and expected a gain of 2.5MM$ with in first one year and expected to have reoccurring gains in subsequent years. The ranked opportunities are incorporated into the business plan, RMP plans and drilling & workover schedule in accordance to field development targets. This advisory system helps in maximizing the profitability and minimizing CAPEX and OPEX. This further maximizes utilization of production optimization models by 30%. Currently the system was implemented in one of ADNOC Onshore field and expected to be scaled to other fields based on consistent value creation.\u0000 A hybrid approach of physics and machine learning based solution led to the development of automated workflows to identify and","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"63 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90620123","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}
Ameen Malkawi, S. Ganti, Zahra Aleid, Hussain Sharrofna, Naeem Minhas, Nicholas Barta
This paper discusses the considerations taken into account before printing additively manufactured (AM) parts, the challenges faced during the printing process, and the standards, methods, and techniques by which the parts are qualified for use. We discuss the four major categories of AM powder bed fusion (PBF) qualification process namely feedstock qualification, machine and process qualification, material qualification, and part qualification. We discuss what each of these qualification processes entails and provide suggestions where appropriate. In this paper, the activity and direction within the international standards community to help drive the widespread adoption of AM technology in various industries is also discussed.
{"title":"Considerations and challenges of qualifying a metal powder bed fusion 3D printing process","authors":"Ameen Malkawi, S. Ganti, Zahra Aleid, Hussain Sharrofna, Naeem Minhas, Nicholas Barta","doi":"10.2118/207628-ms","DOIUrl":"https://doi.org/10.2118/207628-ms","url":null,"abstract":"\u0000 This paper discusses the considerations taken into account before printing additively manufactured (AM) parts, the challenges faced during the printing process, and the standards, methods, and techniques by which the parts are qualified for use. We discuss the four major categories of AM powder bed fusion (PBF) qualification process namely feedstock qualification, machine and process qualification, material qualification, and part qualification. We discuss what each of these qualification processes entails and provide suggestions where appropriate.\u0000 In this paper, the activity and direction within the international standards community to help drive the widespread adoption of AM technology in various industries is also discussed.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77202135","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}
Yuan Liu, L. Mu, Zhengfeng Zhao, Xianwen Li, P. Enkababian
Well completion has evolved rapidly in the past two decades, as multistage completion has become the predominant practice to complete a well in many places. Although innovation in completion tool technology has been continuous in recent years, there are still gaps in the well completion optimization practice. In this paper, we add additional dimensions to well completion technology by incorporating geoengineering, measurement while pumping, and data mining, and we have evidence to show that those additional elements help to improve our understanding, on-site efficiency, and overall performance. Multistage completion optimization is about where and how to complete a well. Different methods were employed in the past, and even with a better-engineered completion design where both reservoir and completion quality are honored, there are still area for improvement. For example, 1) geological properties are not qualitatively utilized in the completion design; 2) real-time operational feedback during the execution phase is inadequate for in-time decisions for completion and fracturing adjustment; 3) the completion-to-well-performance cycle is so long that the learning curve is not fast enough, and too many influential factors are hidden in the details. Three extra dimensions were added to address the improvement areas. Geoengineering adds "space information" in enabling geological properties from a 3D space grid to be projected onto the wellbore as geology quality (GQ) so that the information can be used together with reservoir and completion quality (RQ and CQ) quantitatively to improve the fracturing treatment design. Measurement while pumping (MWP) adds "timely feedback" in that real-time operational feedback—either from the wellbore via high-frequency pressure monitoring or from the target zones via microseismic data in offset horizontal monitoring wells—can help with the completion and fracture diagnosis and decision making on-site. Data mining adds "pattern recognition" in that reservoir and operation data are collected and analyzed to generate a systematic understanding of the reservoir complexity, paving the way for the improved planning of future well completions in the same region. Each of the solutions comes with specific case studies in our work. Geoengineering, MWP, and data mining add three dimensions to the current well completion practice. In our case studies, these approaches have demonstrated the capability to improve the accuracy of the design, increase confidence in the execution, and accelerate the learning curve from evaluation. The extra dimensions added to the current completion practice are essentially space, time, and pattern, and together, they help to define the direction of future innovations for completion optimization.
{"title":"Adding Extra Dimensions for Completion Consideration: Case Studies with Geoengineering, Measurement While Pumping, and Data Mining","authors":"Yuan Liu, L. Mu, Zhengfeng Zhao, Xianwen Li, P. Enkababian","doi":"10.2118/207916-ms","DOIUrl":"https://doi.org/10.2118/207916-ms","url":null,"abstract":"\u0000 Well completion has evolved rapidly in the past two decades, as multistage completion has become the predominant practice to complete a well in many places. Although innovation in completion tool technology has been continuous in recent years, there are still gaps in the well completion optimization practice. In this paper, we add additional dimensions to well completion technology by incorporating geoengineering, measurement while pumping, and data mining, and we have evidence to show that those additional elements help to improve our understanding, on-site efficiency, and overall performance.\u0000 Multistage completion optimization is about where and how to complete a well. Different methods were employed in the past, and even with a better-engineered completion design where both reservoir and completion quality are honored, there are still area for improvement. For example, 1) geological properties are not qualitatively utilized in the completion design; 2) real-time operational feedback during the execution phase is inadequate for in-time decisions for completion and fracturing adjustment; 3) the completion-to-well-performance cycle is so long that the learning curve is not fast enough, and too many influential factors are hidden in the details.\u0000 Three extra dimensions were added to address the improvement areas. Geoengineering adds \"space information\" in enabling geological properties from a 3D space grid to be projected onto the wellbore as geology quality (GQ) so that the information can be used together with reservoir and completion quality (RQ and CQ) quantitatively to improve the fracturing treatment design. Measurement while pumping (MWP) adds \"timely feedback\" in that real-time operational feedback—either from the wellbore via high-frequency pressure monitoring or from the target zones via microseismic data in offset horizontal monitoring wells—can help with the completion and fracture diagnosis and decision making on-site. Data mining adds \"pattern recognition\" in that reservoir and operation data are collected and analyzed to generate a systematic understanding of the reservoir complexity, paving the way for the improved planning of future well completions in the same region. Each of the solutions comes with specific case studies in our work.\u0000 Geoengineering, MWP, and data mining add three dimensions to the current well completion practice. In our case studies, these approaches have demonstrated the capability to improve the accuracy of the design, increase confidence in the execution, and accelerate the learning curve from evaluation. The extra dimensions added to the current completion practice are essentially space, time, and pattern, and together, they help to define the direction of future innovations for completion optimization.","PeriodicalId":10981,"journal":{"name":"Day 4 Thu, November 18, 2021","volume":"20 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2021-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77351233","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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