TECHNICAL PROGRAMME | Energy Technologies – Future Pathways
Research, Technology Start-ups and Funding
Forum 19 | Hall 5 Digital Poster Plaza 4
13
October
10:00
12:00
UTC+3
Technology and innovation are the key to energy transition. Significant advancements have been achieved for conventional energies production in terms of efficiency and emission reductions. New energies such as solar, wind, hydrogen, nuclear, hydro, biomass etc. together with energy storage and complementary technologies, have boomed and are playing more and more important roles in energy transition. This forum will discuss the latest progress and achievements including research, experiments, applications, management and investment, with a particular focus on the roles of start-ups and venture capitals in projects initiation, planning and commercialisation.
Three emerging megatrends call for the reimagination of existing refinery-petrochemical complex to remain profitable in the coming decades – 1) Transport electrification that will exert downward pressure on fuel (gasoline, diesel) margins of the traditional refineries designed to convert more than 50% of the crude to transportation fuels, 2) Plant (chemicals, plastics) electrification that will produce excess methane, and 3) Plastics circularity that will lower the demand of virgin plastics, particularly PET, and result in sub-GDP growth of aromatics (BTX) demand. To address first challenge, refineries need to reconfigure to convert a higher fraction of the crude to light olefins. Several suits of technologies exist or are in the advanced stages of deployment to convert more than 40% of crude to light olefins but all of them are very capital intensive and invariably produce large quantities of both methane and aromatics. As the world moves to reduce carbon footprint of chemicals via electrification to respond to the regulatory pressure, increasingly greater amount of methane will become available with the source of energy shifting from fuel gas to electricity. Plastics recycling will put downward pressure on demand of virgin plastics, particularly PET, which can already be recycled at a relatively low “green premium” further dampening the demand for aromatics. In contrast, demand for light olefins is likely to track GDP growth. Taken together, these megatrends open up strategic space for need to invest in the development of low capital-intensive technologies to convert crude fractions currently allocated to gasoline and diesel to light olefins at high yields (> 70 wt%) without producing large excess of methane or aromatics. In this talk we will expand upon our view of how an integrated refinery-petrochemical complex can be reimagined to address aforesaid megatrends and emerging early-stage technologies that hold the promise to convert heavier feedstocks derived from crude to light olefins at lower capital intensity. To create durable competitive advantage, we believe that the evolving product slate in an electrified world calls for both the development of new cost-effective technologies and operational efficiencies gained through implementation of AI.
Decarbonizing Oil & Gas industry by strategizing carbon neutral technologies, ratification of International-Climate-Change Paris Agreement under UNFCCC-COP to achieve Net Zero by 2070 & impetus to promote Bio-products under National Biofuel Policy (NBP - 2018) are critical drivers to promulgate advanced Bio-Products like Green Nano-Cellulosic-Bio-Products (NCBP). NCBP are derived from cellulosic nanofibers & nanocrystals extracted from ligno-cellulosic-biomass like rice husk, wheat straw, sugarcane bagasse, corn cob, etc. Three main nano-cellulosic materials: Cellulose Nano Crystals (CNC), Cellulose Nano Fibrils (CNF) and Bacterial Cellulose exhibits unique exceptional mechanical (tensile strength & modulus, flexural & impact strength, fatigue resistance), thermal (thermal conductivity & stability, heat resistance) & optical properties making it immensely versatile for applications in Oil & gas industry futuristic domain. This paper critically reviews applications of Nano-Cellulosic-Bio-Products in upstream and downstream gambit of Oil & Gas industry. In upstream domain, unique rheological properties of Nano-cellulose based fluid helps in Enhanced Oil Recovery (EOR) due to modification of viscosity & flow behavior of reservoir fluids, helping to displace trapped oil and improve oil recovery rates. It also improve filtration control, lubricity, enhance stability, reduce friction and mitigate issues like wellbore instability of drilling fluids by control of fluid loss in drilling. In downstream arena, Nano-cellulose aids in Corrosion Protection as an additive in durable and cost effective corrosion-resistant coatings and materials designed to protect oil & gas infrastructure from corrosion. Nano-cellulose based additives are biodegradable and are eco-friendly alternative to synthetic additives for refinery applications making refineries & petrochemical plants Environmentally Sustainable. Nano cellulose based Bio-sensors due to high surface area & ability to be functionalized with specific biomolecules paves way for development of more efficient and sensitive futuristic Bio-ETPs. NCBP holds innovative potential to supplement National Green Hydrogen Mission-2023 (NGHM) by aiding in novel Energy Storage products like Bio-dielectric materials, bio-supercapacitors and bio-nanogenerators. Nano Cellulose high surface area & porosity makes it an excellent candidate for supercapacitor electrodes with exhibited enhanced energy storage capacity & rapid charge-discharge rates, improved performance of lithium-ion batteries as a component in anode or cathode materials with structural stability & electrochemical properties enhancing battery efficiency & longer lifespan. This paper aims to establish that Nano-Cellulosic-Bio-Products (NCBP) can be promising substrate for Products used in oil & gas industry. Implementation with scaled development, cost-effectiveness, & compatibility with existing processes plays prudent role in integrating NCBP into refinery operation. EIL in its gambit of expanding its portfolio in sunrise technologies is expanding its wings to venture in this novel bio-fuel domain. Hope this compilation will create lots of interest amongst Researcher and Practicing Engineers active in bio-derived products, Industrialist, Environmentalist towards meeting net zero goal in Upstream & Downstream Oil and Gas Industry with Green Innovative Novel Biomass Derived Green Nano-Cellulosic-Bio-Products.
Energy saving and combustion optimization are crucial for several reasons:
Improved Efficiency: Optimizing combustion processes in chemical & petrochemical industries, such as reformers, furnaces and boilers, can enhance energy efficiency, reducing fuel consumption and operational costs.
Reduced Emissions: Better combustion control leads to lower emissions of pollutants like carbon monoxide, nitrogen oxides, and particulate matter, contributing to cleaner air and a healthier environment.
Enhanced Process Quality: Accurate and repeatable measurement of excess air ensures consistent combustion, improving the quality of the end product in manufacturing processes.
Safety: Proper combustion optimization reduces the risk of hazardous conditions, such as explosions or fires, by maintaining safe operating parameters.
Improving Combustion Efficiency is achieved by measuring the excess oxygen and combustibles by an oxygen analyzer. Measuring excess oxygen is essential for controlling Air/Fuel Ratio and to make sure that there is no excess fuel or excess air. Measuring combustibles is required to ensure efficient combustion with no left over non burned CO which is harmful for environment. Improving Burner Control is done by a gas chromatograph for measuring real time heating vale (BTU) for the fuel gas and measuring Wobbe Index for Burner Optimization, this is very essential for Calculating Air/Fuel Ratio to improve burner control, improve efficiency, and reduce emissions. By focusing on energy saving and combustion optimization, we can achieve a more sustainable, cost-effective, and safer future.
Improved Efficiency: Optimizing combustion processes in chemical & petrochemical industries, such as reformers, furnaces and boilers, can enhance energy efficiency, reducing fuel consumption and operational costs.
Reduced Emissions: Better combustion control leads to lower emissions of pollutants like carbon monoxide, nitrogen oxides, and particulate matter, contributing to cleaner air and a healthier environment.
Enhanced Process Quality: Accurate and repeatable measurement of excess air ensures consistent combustion, improving the quality of the end product in manufacturing processes.
Safety: Proper combustion optimization reduces the risk of hazardous conditions, such as explosions or fires, by maintaining safe operating parameters.
Improving Combustion Efficiency is achieved by measuring the excess oxygen and combustibles by an oxygen analyzer. Measuring excess oxygen is essential for controlling Air/Fuel Ratio and to make sure that there is no excess fuel or excess air. Measuring combustibles is required to ensure efficient combustion with no left over non burned CO which is harmful for environment. Improving Burner Control is done by a gas chromatograph for measuring real time heating vale (BTU) for the fuel gas and measuring Wobbe Index for Burner Optimization, this is very essential for Calculating Air/Fuel Ratio to improve burner control, improve efficiency, and reduce emissions. By focusing on energy saving and combustion optimization, we can achieve a more sustainable, cost-effective, and safer future.
Low permeability carbonate reservoirs have been playing an increasingly important role in operators’ portfolios in the Middle East. In the Greater Burgan field of southeast Kuwait, the Mauddud is a relatively thin carbonate reservoir (20 – 60ft thickness) with low matrix permeability (0.1 – 5mD range). Under this scenario lower production performance is expected, this has been observed in practice with the Mauddud only showing good productivity when wells intercept clusters of natural fractures. In this work we describe the approach to estimate occurrence of natural fractures in the Mauddud reservoir across the Greater Burgan field, and future development scenarios.
We created a discrete fracture network (DFN) model that integrated data from several sources such as well logs, borehole images, core photos, 3D far-field sonic, 3D geomechanics and production data. Our approach was to recreate the evolution of tectonics across the Greater Burgan field to model stress perturbations around faults and predict location and characteristics of natural fractures. Borehole images, core photos and 3D far-field sonic provided hard data at well locations to calibrate the DFN, with well-level production data corroborating with results further.
Reservoir simulation models using a single porosity approach without natural fractures were not able to reproduce production results in many wells. However, results improved significantly when incorporating the DFN results into a dual permeability (DPDP) reservoir simulation model. For example, the DFN predicted intense natural fracturing in the Magwa region of the Greater Burgan field. Good production performance was observed from Mauddud completions in this area and successful simulation history matches were only obtained using a DPDP guided by the DFN. However, uncertainties in the DFN are an important action item we have identified for future work as we observed some wells with excellent production performance but modest natural fracturing from the DFN.
One way of addressing DFN uncertainty has been to incorporate horizontal multistage proppant or acid fracturing in Mauddud development wells within the next year. This has been a trend in some Middle Eastern operators as stimulation can help intercept clusters of natural fractures that do not cross the wellbore. Additional data such as higher resolution and azimuthal seismic can help narrow down uncertainties in the DFN modeling results.
We created a discrete fracture network (DFN) model that integrated data from several sources such as well logs, borehole images, core photos, 3D far-field sonic, 3D geomechanics and production data. Our approach was to recreate the evolution of tectonics across the Greater Burgan field to model stress perturbations around faults and predict location and characteristics of natural fractures. Borehole images, core photos and 3D far-field sonic provided hard data at well locations to calibrate the DFN, with well-level production data corroborating with results further.
Reservoir simulation models using a single porosity approach without natural fractures were not able to reproduce production results in many wells. However, results improved significantly when incorporating the DFN results into a dual permeability (DPDP) reservoir simulation model. For example, the DFN predicted intense natural fracturing in the Magwa region of the Greater Burgan field. Good production performance was observed from Mauddud completions in this area and successful simulation history matches were only obtained using a DPDP guided by the DFN. However, uncertainties in the DFN are an important action item we have identified for future work as we observed some wells with excellent production performance but modest natural fracturing from the DFN.
One way of addressing DFN uncertainty has been to incorporate horizontal multistage proppant or acid fracturing in Mauddud development wells within the next year. This has been a trend in some Middle Eastern operators as stimulation can help intercept clusters of natural fractures that do not cross the wellbore. Additional data such as higher resolution and azimuthal seismic can help narrow down uncertainties in the DFN modeling results.
Energy systems face continual deterioration of the mineral resource base and recurrent technological or economic shocks. Conventional planning models treat innovation as an external adjustment and leave the value of superior information unquantified. For the first time, resource degradation, endogenous innovation and the quantitative Value of Information (VOI) are integrated in a single linear-quadratic optimal-control problem. The framework yields an explicit technological-capital substitution rate, captures investment lags and maps the nonlinear variation of marginal returns across factor levels, so that capital is channelled into research or extraction only when this is provably optimal.
A four-factor translog production structure links physical assets, skilled labour, innovation effort and resource quality. Factor dynamics follow a stochastic control law that allows for market and geological uncertainty. New data and shocks enter a Kalman filter; every reduction in posterior variance is monetised as VOI, interpreted as the expected improvement of the objective caused by tighter control around the optimum. The optimal feedback rule is derived by the Pontryagin Maximum Principle and solved through an algebraic Riccati equation, redistributing expenditure among capacity expansion, innovation and extraction rate. Two fast-response scenarios are analysed—a sudden drop in resource quality and an abrupt technological breakthrough—after which the local rules are aggregated to the industry level under market-clearing and infrastructure constraints, producing a second optimisation that balances total output with innovation intensity.
Model experiments show that when resource quality worsens, the feedback law automatically shifts funds from physical expansion toward innovation, limiting output losses without locking in excess capital. Under a technology shock the rule reverses, curtailing non-essential projects and concentrating resources on monitoring and incremental upgrades. The analysis pinpoints a critical elasticity interval: below that range, extra information yields higher marginal benefit than new physical capacity, establishing a transparent threshold for investment committees. Aggregated across firms, the rules smooth sectoral production and stabilise research spending, indicating that VOI-driven innovation can offset resource decline and dampen volatility triggered by external shocks.
For operating companies the framework delivers a quantitative metric: the analytical VOI function directly converts uncertainty reduction into expected profit gain, allowing R&D to be benchmarked against capital alternatives. Regulators can embed VOI thresholds and substitution rates in licence terms, encouraging timely innovation and discouraging inefficient capacity growth. For the first time, resource degradation, delayed dynamics, variable substitution elasticities and the explicit cost of information are combined in a coherent optimisation of the production function, producing clear rules usable at both field and industry scales and providing a robust tool for balancing capital and research under deep uncertainty.
A four-factor translog production structure links physical assets, skilled labour, innovation effort and resource quality. Factor dynamics follow a stochastic control law that allows for market and geological uncertainty. New data and shocks enter a Kalman filter; every reduction in posterior variance is monetised as VOI, interpreted as the expected improvement of the objective caused by tighter control around the optimum. The optimal feedback rule is derived by the Pontryagin Maximum Principle and solved through an algebraic Riccati equation, redistributing expenditure among capacity expansion, innovation and extraction rate. Two fast-response scenarios are analysed—a sudden drop in resource quality and an abrupt technological breakthrough—after which the local rules are aggregated to the industry level under market-clearing and infrastructure constraints, producing a second optimisation that balances total output with innovation intensity.
Model experiments show that when resource quality worsens, the feedback law automatically shifts funds from physical expansion toward innovation, limiting output losses without locking in excess capital. Under a technology shock the rule reverses, curtailing non-essential projects and concentrating resources on monitoring and incremental upgrades. The analysis pinpoints a critical elasticity interval: below that range, extra information yields higher marginal benefit than new physical capacity, establishing a transparent threshold for investment committees. Aggregated across firms, the rules smooth sectoral production and stabilise research spending, indicating that VOI-driven innovation can offset resource decline and dampen volatility triggered by external shocks.
For operating companies the framework delivers a quantitative metric: the analytical VOI function directly converts uncertainty reduction into expected profit gain, allowing R&D to be benchmarked against capital alternatives. Regulators can embed VOI thresholds and substitution rates in licence terms, encouraging timely innovation and discouraging inefficient capacity growth. For the first time, resource degradation, delayed dynamics, variable substitution elasticities and the explicit cost of information are combined in a coherent optimisation of the production function, producing clear rules usable at both field and industry scales and providing a robust tool for balancing capital and research under deep uncertainty.
Three emerging megatrends call for the reimagination of existing refinery-petrochemical complex to remain profitable in the coming decades – 1) Transport electrification that will exert downward pressure on fuel (gasoline, diesel) margins of the traditional refineries designed to convert more than 50% of the crude to transportation fuels, 2) Plant (chemicals, plastics) electrification that will produce excess methane, and 3) Plastics circularity that will lower the demand of virgin plastics, particularly PET, and result in sub-GDP growth of aromatics (BTX) demand. To address first challenge, refineries need to reconfigure to convert a higher fraction of the crude to light olefins. Several suits of technologies exist or are in the advanced stages of deployment to convert more than 40% of crude to light olefins but all of them are very capital intensive and invariably produce large quantities of both methane and aromatics. As the world moves to reduce carbon footprint of chemicals via electrification to respond to the regulatory pressure, increasingly greater amount of methane will become available with the source of energy shifting from fuel gas to electricity. Plastics recycling will put downward pressure on demand of virgin plastics, particularly PET, which can already be recycled at a relatively low “green premium” further dampening the demand for aromatics. In contrast, demand for light olefins is likely to track GDP growth. Taken together, these megatrends open up strategic space for need to invest in the development of low capital-intensive technologies to convert crude fractions currently allocated to gasoline and diesel to light olefins at high yields (> 70 wt%) without producing large excess of methane or aromatics. In this talk we will expand upon our view of how an integrated refinery-petrochemical complex can be reimagined to address aforesaid megatrends and emerging early-stage technologies that hold the promise to convert heavier feedstocks derived from crude to light olefins at lower capital intensity. To create durable competitive advantage, we believe that the evolving product slate in an electrified world calls for both the development of new cost-effective technologies and operational efficiencies gained through implementation of AI.
Decarbonizing Oil & Gas industry by strategizing carbon neutral technologies, ratification of International-Climate-Change Paris Agreement under UNFCCC-COP to achieve Net Zero by 2070 & impetus to promote Bio-products under National Biofuel Policy (NBP - 2018) are critical drivers to promulgate advanced Bio-Products like Green Nano-Cellulosic-Bio-Products (NCBP). NCBP are derived from cellulosic nanofibers & nanocrystals extracted from ligno-cellulosic-biomass like rice husk, wheat straw, sugarcane bagasse, corn cob, etc. Three main nano-cellulosic materials: Cellulose Nano Crystals (CNC), Cellulose Nano Fibrils (CNF) and Bacterial Cellulose exhibits unique exceptional mechanical (tensile strength & modulus, flexural & impact strength, fatigue resistance), thermal (thermal conductivity & stability, heat resistance) & optical properties making it immensely versatile for applications in Oil & gas industry futuristic domain. This paper critically reviews applications of Nano-Cellulosic-Bio-Products in upstream and downstream gambit of Oil & Gas industry. In upstream domain, unique rheological properties of Nano-cellulose based fluid helps in Enhanced Oil Recovery (EOR) due to modification of viscosity & flow behavior of reservoir fluids, helping to displace trapped oil and improve oil recovery rates. It also improve filtration control, lubricity, enhance stability, reduce friction and mitigate issues like wellbore instability of drilling fluids by control of fluid loss in drilling. In downstream arena, Nano-cellulose aids in Corrosion Protection as an additive in durable and cost effective corrosion-resistant coatings and materials designed to protect oil & gas infrastructure from corrosion. Nano-cellulose based additives are biodegradable and are eco-friendly alternative to synthetic additives for refinery applications making refineries & petrochemical plants Environmentally Sustainable. Nano cellulose based Bio-sensors due to high surface area & ability to be functionalized with specific biomolecules paves way for development of more efficient and sensitive futuristic Bio-ETPs. NCBP holds innovative potential to supplement National Green Hydrogen Mission-2023 (NGHM) by aiding in novel Energy Storage products like Bio-dielectric materials, bio-supercapacitors and bio-nanogenerators. Nano Cellulose high surface area & porosity makes it an excellent candidate for supercapacitor electrodes with exhibited enhanced energy storage capacity & rapid charge-discharge rates, improved performance of lithium-ion batteries as a component in anode or cathode materials with structural stability & electrochemical properties enhancing battery efficiency & longer lifespan. This paper aims to establish that Nano-Cellulosic-Bio-Products (NCBP) can be promising substrate for Products used in oil & gas industry. Implementation with scaled development, cost-effectiveness, & compatibility with existing processes plays prudent role in integrating NCBP into refinery operation. EIL in its gambit of expanding its portfolio in sunrise technologies is expanding its wings to venture in this novel bio-fuel domain. Hope this compilation will create lots of interest amongst Researcher and Practicing Engineers active in bio-derived products, Industrialist, Environmentalist towards meeting net zero goal in Upstream & Downstream Oil and Gas Industry with Green Innovative Novel Biomass Derived Green Nano-Cellulosic-Bio-Products.
Oleg Zhdaneev
Speaker
Head
Technological Development Centre of the Fuel and Energy Complex
Russia
Energy systems face continual deterioration of the mineral resource base and recurrent technological or economic shocks. Conventional planning models treat innovation as an external adjustment and leave the value of superior information unquantified. For the first time, resource degradation, endogenous innovation and the quantitative Value of Information (VOI) are integrated in a single linear-quadratic optimal-control problem. The framework yields an explicit technological-capital substitution rate, captures investment lags and maps the nonlinear variation of marginal returns across factor levels, so that capital is channelled into research or extraction only when this is provably optimal.
A four-factor translog production structure links physical assets, skilled labour, innovation effort and resource quality. Factor dynamics follow a stochastic control law that allows for market and geological uncertainty. New data and shocks enter a Kalman filter; every reduction in posterior variance is monetised as VOI, interpreted as the expected improvement of the objective caused by tighter control around the optimum. The optimal feedback rule is derived by the Pontryagin Maximum Principle and solved through an algebraic Riccati equation, redistributing expenditure among capacity expansion, innovation and extraction rate. Two fast-response scenarios are analysed—a sudden drop in resource quality and an abrupt technological breakthrough—after which the local rules are aggregated to the industry level under market-clearing and infrastructure constraints, producing a second optimisation that balances total output with innovation intensity.
Model experiments show that when resource quality worsens, the feedback law automatically shifts funds from physical expansion toward innovation, limiting output losses without locking in excess capital. Under a technology shock the rule reverses, curtailing non-essential projects and concentrating resources on monitoring and incremental upgrades. The analysis pinpoints a critical elasticity interval: below that range, extra information yields higher marginal benefit than new physical capacity, establishing a transparent threshold for investment committees. Aggregated across firms, the rules smooth sectoral production and stabilise research spending, indicating that VOI-driven innovation can offset resource decline and dampen volatility triggered by external shocks.
For operating companies the framework delivers a quantitative metric: the analytical VOI function directly converts uncertainty reduction into expected profit gain, allowing R&D to be benchmarked against capital alternatives. Regulators can embed VOI thresholds and substitution rates in licence terms, encouraging timely innovation and discouraging inefficient capacity growth. For the first time, resource degradation, delayed dynamics, variable substitution elasticities and the explicit cost of information are combined in a coherent optimisation of the production function, producing clear rules usable at both field and industry scales and providing a robust tool for balancing capital and research under deep uncertainty.
A four-factor translog production structure links physical assets, skilled labour, innovation effort and resource quality. Factor dynamics follow a stochastic control law that allows for market and geological uncertainty. New data and shocks enter a Kalman filter; every reduction in posterior variance is monetised as VOI, interpreted as the expected improvement of the objective caused by tighter control around the optimum. The optimal feedback rule is derived by the Pontryagin Maximum Principle and solved through an algebraic Riccati equation, redistributing expenditure among capacity expansion, innovation and extraction rate. Two fast-response scenarios are analysed—a sudden drop in resource quality and an abrupt technological breakthrough—after which the local rules are aggregated to the industry level under market-clearing and infrastructure constraints, producing a second optimisation that balances total output with innovation intensity.
Model experiments show that when resource quality worsens, the feedback law automatically shifts funds from physical expansion toward innovation, limiting output losses without locking in excess capital. Under a technology shock the rule reverses, curtailing non-essential projects and concentrating resources on monitoring and incremental upgrades. The analysis pinpoints a critical elasticity interval: below that range, extra information yields higher marginal benefit than new physical capacity, establishing a transparent threshold for investment committees. Aggregated across firms, the rules smooth sectoral production and stabilise research spending, indicating that VOI-driven innovation can offset resource decline and dampen volatility triggered by external shocks.
For operating companies the framework delivers a quantitative metric: the analytical VOI function directly converts uncertainty reduction into expected profit gain, allowing R&D to be benchmarked against capital alternatives. Regulators can embed VOI thresholds and substitution rates in licence terms, encouraging timely innovation and discouraging inefficient capacity growth. For the first time, resource degradation, delayed dynamics, variable substitution elasticities and the explicit cost of information are combined in a coherent optimisation of the production function, producing clear rules usable at both field and industry scales and providing a robust tool for balancing capital and research under deep uncertainty.
Energy saving and combustion optimization are crucial for several reasons:
Improved Efficiency: Optimizing combustion processes in chemical & petrochemical industries, such as reformers, furnaces and boilers, can enhance energy efficiency, reducing fuel consumption and operational costs.
Reduced Emissions: Better combustion control leads to lower emissions of pollutants like carbon monoxide, nitrogen oxides, and particulate matter, contributing to cleaner air and a healthier environment.
Enhanced Process Quality: Accurate and repeatable measurement of excess air ensures consistent combustion, improving the quality of the end product in manufacturing processes.
Safety: Proper combustion optimization reduces the risk of hazardous conditions, such as explosions or fires, by maintaining safe operating parameters.
Improving Combustion Efficiency is achieved by measuring the excess oxygen and combustibles by an oxygen analyzer. Measuring excess oxygen is essential for controlling Air/Fuel Ratio and to make sure that there is no excess fuel or excess air. Measuring combustibles is required to ensure efficient combustion with no left over non burned CO which is harmful for environment. Improving Burner Control is done by a gas chromatograph for measuring real time heating vale (BTU) for the fuel gas and measuring Wobbe Index for Burner Optimization, this is very essential for Calculating Air/Fuel Ratio to improve burner control, improve efficiency, and reduce emissions. By focusing on energy saving and combustion optimization, we can achieve a more sustainable, cost-effective, and safer future.
Improved Efficiency: Optimizing combustion processes in chemical & petrochemical industries, such as reformers, furnaces and boilers, can enhance energy efficiency, reducing fuel consumption and operational costs.
Reduced Emissions: Better combustion control leads to lower emissions of pollutants like carbon monoxide, nitrogen oxides, and particulate matter, contributing to cleaner air and a healthier environment.
Enhanced Process Quality: Accurate and repeatable measurement of excess air ensures consistent combustion, improving the quality of the end product in manufacturing processes.
Safety: Proper combustion optimization reduces the risk of hazardous conditions, such as explosions or fires, by maintaining safe operating parameters.
Improving Combustion Efficiency is achieved by measuring the excess oxygen and combustibles by an oxygen analyzer. Measuring excess oxygen is essential for controlling Air/Fuel Ratio and to make sure that there is no excess fuel or excess air. Measuring combustibles is required to ensure efficient combustion with no left over non burned CO which is harmful for environment. Improving Burner Control is done by a gas chromatograph for measuring real time heating vale (BTU) for the fuel gas and measuring Wobbe Index for Burner Optimization, this is very essential for Calculating Air/Fuel Ratio to improve burner control, improve efficiency, and reduce emissions. By focusing on energy saving and combustion optimization, we can achieve a more sustainable, cost-effective, and safer future.
Low permeability carbonate reservoirs have been playing an increasingly important role in operators’ portfolios in the Middle East. In the Greater Burgan field of southeast Kuwait, the Mauddud is a relatively thin carbonate reservoir (20 – 60ft thickness) with low matrix permeability (0.1 – 5mD range). Under this scenario lower production performance is expected, this has been observed in practice with the Mauddud only showing good productivity when wells intercept clusters of natural fractures. In this work we describe the approach to estimate occurrence of natural fractures in the Mauddud reservoir across the Greater Burgan field, and future development scenarios.
We created a discrete fracture network (DFN) model that integrated data from several sources such as well logs, borehole images, core photos, 3D far-field sonic, 3D geomechanics and production data. Our approach was to recreate the evolution of tectonics across the Greater Burgan field to model stress perturbations around faults and predict location and characteristics of natural fractures. Borehole images, core photos and 3D far-field sonic provided hard data at well locations to calibrate the DFN, with well-level production data corroborating with results further.
Reservoir simulation models using a single porosity approach without natural fractures were not able to reproduce production results in many wells. However, results improved significantly when incorporating the DFN results into a dual permeability (DPDP) reservoir simulation model. For example, the DFN predicted intense natural fracturing in the Magwa region of the Greater Burgan field. Good production performance was observed from Mauddud completions in this area and successful simulation history matches were only obtained using a DPDP guided by the DFN. However, uncertainties in the DFN are an important action item we have identified for future work as we observed some wells with excellent production performance but modest natural fracturing from the DFN.
One way of addressing DFN uncertainty has been to incorporate horizontal multistage proppant or acid fracturing in Mauddud development wells within the next year. This has been a trend in some Middle Eastern operators as stimulation can help intercept clusters of natural fractures that do not cross the wellbore. Additional data such as higher resolution and azimuthal seismic can help narrow down uncertainties in the DFN modeling results.
We created a discrete fracture network (DFN) model that integrated data from several sources such as well logs, borehole images, core photos, 3D far-field sonic, 3D geomechanics and production data. Our approach was to recreate the evolution of tectonics across the Greater Burgan field to model stress perturbations around faults and predict location and characteristics of natural fractures. Borehole images, core photos and 3D far-field sonic provided hard data at well locations to calibrate the DFN, with well-level production data corroborating with results further.
Reservoir simulation models using a single porosity approach without natural fractures were not able to reproduce production results in many wells. However, results improved significantly when incorporating the DFN results into a dual permeability (DPDP) reservoir simulation model. For example, the DFN predicted intense natural fracturing in the Magwa region of the Greater Burgan field. Good production performance was observed from Mauddud completions in this area and successful simulation history matches were only obtained using a DPDP guided by the DFN. However, uncertainties in the DFN are an important action item we have identified for future work as we observed some wells with excellent production performance but modest natural fracturing from the DFN.
One way of addressing DFN uncertainty has been to incorporate horizontal multistage proppant or acid fracturing in Mauddud development wells within the next year. This has been a trend in some Middle Eastern operators as stimulation can help intercept clusters of natural fractures that do not cross the wellbore. Additional data such as higher resolution and azimuthal seismic can help narrow down uncertainties in the DFN modeling results.
