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Christopher R Clarkson - One of the best experts on this subject based on the ideXlab platform.
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Production Data analysis of gas condensate reservoirs using two-phase viscosity and two-phase compressibility
Journal of Natural Gas Science and Engineering, 2017Co-Authors: Hamid Behmanesh, Hamidreza Hamdi, Christopher R ClarksonAbstract:Abstract Due to the occurrence of multi-phase flow in gas condensate reservoirs, Production Data of such reservoirs cannot be accurately analyzed using single-phase, dry gas models. In this work, we established a new semi-analytical method for rate-transient analysis of gas condensate reservoirs producing during boundary-dominated flow. In particular, the single-phase flow assumption in the development of the material-balance pseudotime function for dry gas reservoirs is alleviated by incorporating two-phase viscosity and two-phase compressibility into a modified two-phase pseudotime function. Introducing the definition of two-phase pseudotime achieves the goal of (approximately) linearizing the governing diffusivity equation. Using the principle of superposition applied to the constant rate solution, this methodology is extended for wells with dynamic changes in well operating conditions (i.e. rates and pressures). Original gas-in-place (OGIP) can also be quantified through a specialized plotting technique. To validate the developed method, synthetic Production Data using fine grid compositional simulations for both lean and rich gas condensate fluids are analyzed. In all cases, the proposed approach provides reasonable estimates of simulator input reservoir properties (e.g. OGIP). The presented technique is successfully applied to the analysis of field cases. This work provides a practical and simple engineering workflow for Production Data analysis of conventional gas condensate reservoirs with multi-phase flow during boundary-dominated flow.
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reconciling flowback and Production Data a novel history matching approach for liquid rich shale wells
Journal of Natural Gas Science and Engineering, 2016Co-Authors: M S Kanfar, Christopher R ClarksonAbstract:Abstract The recent downturn in petroleum prices poses a challenge to the oil and gas industry and in particular to the development of shale assets. This unfavorable economic situation calls for improved reservoir characterization and modeling that are necessary for the optimization of well design and cost. Current rate transient analysis (RTA) techniques are well-suited for the reservoir characterization of dry shale gas wells. However, they have limited accuracy for the more profitable liquid rich shale (LRS) wells. For example, RTA does not accurately model three-phase flow of gas, water, and condensate that can occur in the reservoir. Further, application of RTA typically ignores post-fracturing water flowback Data that can be analyzed for important information pertaining to hydraulic and secondary fractures. In this work, the reservoir of LRS wells is characterized by simulating and history matching the three-phase flow of early flowback and long-term Production. In this work, characterization and history matching are performed by utilizing a novel procedure that includes an equation of state (EOS), a compositional reservoir simulator, and a multi-objective optimization (MOO) algorithm. In addition, the simulation approach incorporates various water trapping mechanisms that are necessary to reproduce the water Production rate trends. The procedure minimizes the misfit between observed and simulated Production Data. It starts by first tuning the EOS to a surface recombined sample. Then, a triple-porosity (i.e., hydraulic fracture, secondary/natural fracture, and matrix porosities) simulation model is applied that utilizes the multiple interacting continua (MINC) method and accounts for permeability jail, pressure-dependent permeability, capillary pressure, and gravity segregation. Finally, an MOO algorithm is used, namely the fast nondominant sorting genetic algorithm (NSGA-II), to solve the misfit minimization problem. The introduced procedure has a number of advantages. One advantage is the simultaneous matching of flowback and Production Data which improves rock and fracture characterization. Another advantage is the utilization of MINC combined with logarithmic gridding that can model transient flow in a triple porosity system. Moreover, it can model different fracture shapes by adjusting a shape factor parameter. In addition to these, the use of MOO does not require predetermined weights unlike the aggregate function approach. In this article, detailed description of all the steps of the history matching workflow are presented, and its applicability demonstrated with a field example from the Montney Formation.
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Production Data analysis of tight gas condensate reservoirs
Journal of Natural Gas Science and Engineering, 2015Co-Authors: Hamid Behmanesh, Hamidreza Hamdi, Christopher R ClarksonAbstract:Abstract The current focus on liquids-rich shale (LRS) plays in North America underscores the need to develop reservoir engineering methods to analyze such reservoirs. Commercialization of LRS plays is now possible due to new technology, such as multi-fractured horizontal wells (MFHW). Efficient Production from such reservoirs necessitates understanding of flow mechanisms, reservoir properties and the controlling rock and fluid parameters. Production-decline analysis is an important technique for analysis of Production Data and obtaining estimates of recoverable reserves. Nevertheless, these techniques, developed for conventional reservoirs, are not appropriate for ultra-low permeability reservoirs. There are substantial differences in reservoir performance characteristics between conventional and ultra-low permeability reservoirs. LRS reservoirs produce much leaner wellstreams compared to conventional reservoirs due to very low permeabilities that result in very large drawdowns. Methods for analysis of two-phase flow in conventional reservoirs, with underlying simplifying assumptions, are no longer applicable. This paper discusses Production Data analysis of constant flowing bottomhole pressure (FBHP) wells producing from LRS (gas condensate) reservoirs. A theoretical basis is developed for a gas condensate reservoir during the transient matrix linear flow (drawdown) period. The governing flow equation is linearized using appropriately defined two-phase pseudopressure and pseudotime functions so that the solutions for liquids can be applied. The derived backward model is employed to compute the linear flow parameter, x f √ k . Simulation results show that the liquid yield will be approximately constant for LRS wells during the transient linear flow, from the early days of initial testing, if FBHP is almost constant. An analytical formulation is used to prove this finding for 1D transient linear flow of LRS wells. The proposed Production Data analysis (PDA) method is illustrated using simulated Production Data for different fluid models and relative permeability curves. Fine-grid compositional and black oil numerical models are used for this purpose.
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Production Data analysis of unconventional gas wells workflow
International Journal of Coal Geology, 2013Co-Authors: Christopher R ClarksonAbstract:Abstract Production Data analysis techniques for unconventional reservoirs are in their infancy. These techniques continue to evolve according to our improved understanding of the physics of fluid storage and flow. Analytical methods, which include type-curves, flow-regime analysis and analytical simulation, have recently been modified to account for desorption of gas, multi-phase flow, non-Darcy flow (slip flow and diffusion), and non-static (stress-dependent) permeability. Further, adaptations have been made to account for complex wellbore and hydraulic fracture geometries encountered in some developments. Empirical methods, including the popular Arps decline-curve methodology, have been adopted to account for long-term transient and transitional flow associated with some unconventional plays. New empirical methods have been developed to address the limitations of the Arps curves. Unlike conventional reservoirs, however, where decades of application have led to “rules of thumb” and guidance for decline parameter ranges that can be used for different reservoir and Production scenarios, application of empirical techniques to unconventional reservoirs can lead to significant uncertainty in Production forecasts. In this article, a workflow is described for the integrated use of analytical and empirical Production Data analysis methods for the purpose of: 1) reservoir and hydraulic fracture characterization and 2) Production forecasting/reserves determination. Details of the methodologies are provided in an accompanying review article ( Clarkson, 2013 ). It is recommended that newly-developed empirical techniques not be applied on their own, and that decline parameters should be adjusted to match Production forecasts generated using analytical techniques. Analytical methods better (although still imperfectly) account for the physics of fluid flow and storage in unconventional reservoirs, and can account for operational changes during the life of a well. Examples of the workflow are provided using a simulated and field example.
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Production Data analysis of unconventional gas wells review of theory and best practices
International Journal of Coal Geology, 2013Co-Authors: Christopher R ClarksonAbstract:Abstract Unconventional gas reservoirs, including coalbed methane (CBM), tight gas (TG) and shale gas (SG), have become a significant source of hydrocarbon supply in North America, and interest in these resource plays has been generated globally. Despite a growing exploitation history, there is still much to be learned about fluid storage and transport properties of these reservoirs. A key task of petroleum engineers and geoscientists is to use historical Production (reservoir fluid Production rate histories, and cumulative Production) for the purposes of 1) reservoir and well stimulation characterization and 2) Production forecasting for reserve estimation and development planning. Both of these subtasks fall within the domain of quantitative Production Data analysis (PDA). PDA can be performed analytically, where physical models are applied to historical Production and flowing pressure Data to first extract information about the reservoir (i.e. hydrocarbon-in-place, permeability-thickness product) and stimulation (i.e. skin or hydraulic fracture properties) and then generate a forecast using a model that has been “calibrated” to the dynamic Data (i.e. rates and pressures). Analytical Production Data analysis methods, often referred to as rate-transient analysis (RTA), utilize concepts analogous to pressure-transient analysis (PTA) for their implementation, and hence have a firm grounding in the physics of fluid storage and flow. Empirical methods, such as decline curve analysis, rely on empirical curve fits to historical Production Data, and projections to the future. These methods do not rigorously account for dynamic changes in well operating conditions (i.e. flowing pressures), or reservoir or fluid property changes. Quantitative PDA is now routinely applied for conventional reservoirs, where the physics of fluid storage and flow are relatively well-understood. RTA has evolved extensively over the past four decades, and empirical methods are now applied with constraints and “rules of thumb” developed by researchers with some confidence. For unconventional reservoirs, these techniques continue to evolve according to our improved understanding of the physics of fluid storage and flow. In this article, the latest techniques for quantitative PDA including type-curve analysis, straight-line (flow-regime) analysis, analytical and numerical simulation and empirical methods are briefly reviewed, specifically addressing their adaptation for CBM and SG reservoirs. Simulated and field examples are provided to demonstrate application. It is hoped that this article will serve as practical guide to Production analysis for unconventional reservoirs as well as reveal the latest advances in these techniques.
Robert De Kleine - One of the best experts on this subject based on the ideXlab platform.
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impact of updated material Production Data in the greet life cycle model
Journal of Industrial Ecology, 2014Co-Authors: Robert De Kleine, Gregory A Keoleian, Shelie A Miller, Andrew Burnham, John L SullivanAbstract:type="main"> The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne National Laboratory quantifies the life cycle energy consumption and air emissions resulting from the Production and use of light-duty vehicles in the United States. GREET is comprised of two components: GREET 1 represents the fuel cycle of various energy carriers, including automotive fuels, and GREET 2 represents the vehicle cycle, which accounts for the Production of vehicles and their constituent materials. The GREET model was updated in 2012 and now includes higher-resolution material processing and transformation Data. This study evaluated how model updates influence material and vehicle life cycle results. First, new primary energy demand and greenhouse gas (GHG) emissions results from GREET 2 for steel, aluminum, and plastics resins are compared herein with those from the previous version of the model as well as industrial results. A part of the comparison is a discussion about causes of differences between results. Included in this discussion is an assessment of the impact of the new material Production Data on vehicle life cycle results for conventional internal combustion engine (ICE) vehicles by comparing the energy and GHG emission values in the updated and previous versions of GREET 2. Finally, results from a sensitivity analysis are presented for identifying life cycle parameters that most affect vehicle life cycle estimates.
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impact of updated material Production Data in the greet life cycle model
Journal of Industrial Ecology, 2014Co-Authors: Robert De Kleine, Gregory A Keoleian, Shelie A Miller, Andrew Burnham, John SullivanAbstract:The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne National Laboratory quantifies the life cycle energy consumption and air emissions resulting from the Production and use of light-duty vehicles in the United States. GREET is comprised of two components: GREET 1 represents the fuel cycle of various energy carriers, including automotive fuels, and GREET 2 represents the vehicle cycle, which accounts for the Production of vehicles and their constituent materials. The GREET model was updated in 2012 and now includes higher-resolution material processing and transformation Data. This study evaluated how model updates influence material and vehicle life cycle results. First, new primary energy demand and greenhouse gas (GHG) emissions results from GREET 2 for steel, aluminum, and plastics resins are compared herein with those from the previous version of the model as well as industrial results. A part of the comparison is a discussion about causes of differences between results. Included in this discussion is an assessment of the impact of the new material Production Data on vehicle life cycle results for conventional internal combustion engine (ICE) vehicles by comparing the energy and GHG emission values in the updated and previous versions of GREET 2. Finally, results from a sensitivity analysis are presented for identifying life cycle parameters that most affect vehicle life cycle estimates.
John Sullivan - One of the best experts on this subject based on the ideXlab platform.
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impact of updated material Production Data in the greet life cycle model
Journal of Industrial Ecology, 2014Co-Authors: Robert De Kleine, Gregory A Keoleian, Shelie A Miller, Andrew Burnham, John SullivanAbstract:The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne National Laboratory quantifies the life cycle energy consumption and air emissions resulting from the Production and use of light-duty vehicles in the United States. GREET is comprised of two components: GREET 1 represents the fuel cycle of various energy carriers, including automotive fuels, and GREET 2 represents the vehicle cycle, which accounts for the Production of vehicles and their constituent materials. The GREET model was updated in 2012 and now includes higher-resolution material processing and transformation Data. This study evaluated how model updates influence material and vehicle life cycle results. First, new primary energy demand and greenhouse gas (GHG) emissions results from GREET 2 for steel, aluminum, and plastics resins are compared herein with those from the previous version of the model as well as industrial results. A part of the comparison is a discussion about causes of differences between results. Included in this discussion is an assessment of the impact of the new material Production Data on vehicle life cycle results for conventional internal combustion engine (ICE) vehicles by comparing the energy and GHG emission values in the updated and previous versions of GREET 2. Finally, results from a sensitivity analysis are presented for identifying life cycle parameters that most affect vehicle life cycle estimates.
John L Sullivan - One of the best experts on this subject based on the ideXlab platform.
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impact of updated material Production Data in the greet life cycle model
Journal of Industrial Ecology, 2014Co-Authors: Robert De Kleine, Gregory A Keoleian, Shelie A Miller, Andrew Burnham, John L SullivanAbstract:type="main"> The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne National Laboratory quantifies the life cycle energy consumption and air emissions resulting from the Production and use of light-duty vehicles in the United States. GREET is comprised of two components: GREET 1 represents the fuel cycle of various energy carriers, including automotive fuels, and GREET 2 represents the vehicle cycle, which accounts for the Production of vehicles and their constituent materials. The GREET model was updated in 2012 and now includes higher-resolution material processing and transformation Data. This study evaluated how model updates influence material and vehicle life cycle results. First, new primary energy demand and greenhouse gas (GHG) emissions results from GREET 2 for steel, aluminum, and plastics resins are compared herein with those from the previous version of the model as well as industrial results. A part of the comparison is a discussion about causes of differences between results. Included in this discussion is an assessment of the impact of the new material Production Data on vehicle life cycle results for conventional internal combustion engine (ICE) vehicles by comparing the energy and GHG emission values in the updated and previous versions of GREET 2. Finally, results from a sensitivity analysis are presented for identifying life cycle parameters that most affect vehicle life cycle estimates.
Gregory A Keoleian - One of the best experts on this subject based on the ideXlab platform.
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impact of updated material Production Data in the greet life cycle model
Journal of Industrial Ecology, 2014Co-Authors: Robert De Kleine, Gregory A Keoleian, Shelie A Miller, Andrew Burnham, John L SullivanAbstract:type="main"> The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne National Laboratory quantifies the life cycle energy consumption and air emissions resulting from the Production and use of light-duty vehicles in the United States. GREET is comprised of two components: GREET 1 represents the fuel cycle of various energy carriers, including automotive fuels, and GREET 2 represents the vehicle cycle, which accounts for the Production of vehicles and their constituent materials. The GREET model was updated in 2012 and now includes higher-resolution material processing and transformation Data. This study evaluated how model updates influence material and vehicle life cycle results. First, new primary energy demand and greenhouse gas (GHG) emissions results from GREET 2 for steel, aluminum, and plastics resins are compared herein with those from the previous version of the model as well as industrial results. A part of the comparison is a discussion about causes of differences between results. Included in this discussion is an assessment of the impact of the new material Production Data on vehicle life cycle results for conventional internal combustion engine (ICE) vehicles by comparing the energy and GHG emission values in the updated and previous versions of GREET 2. Finally, results from a sensitivity analysis are presented for identifying life cycle parameters that most affect vehicle life cycle estimates.
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impact of updated material Production Data in the greet life cycle model
Journal of Industrial Ecology, 2014Co-Authors: Robert De Kleine, Gregory A Keoleian, Shelie A Miller, Andrew Burnham, John SullivanAbstract:The Greenhouse gases, Regulated Emissions, and Energy use in Transportation (GREET) model developed by Argonne National Laboratory quantifies the life cycle energy consumption and air emissions resulting from the Production and use of light-duty vehicles in the United States. GREET is comprised of two components: GREET 1 represents the fuel cycle of various energy carriers, including automotive fuels, and GREET 2 represents the vehicle cycle, which accounts for the Production of vehicles and their constituent materials. The GREET model was updated in 2012 and now includes higher-resolution material processing and transformation Data. This study evaluated how model updates influence material and vehicle life cycle results. First, new primary energy demand and greenhouse gas (GHG) emissions results from GREET 2 for steel, aluminum, and plastics resins are compared herein with those from the previous version of the model as well as industrial results. A part of the comparison is a discussion about causes of differences between results. Included in this discussion is an assessment of the impact of the new material Production Data on vehicle life cycle results for conventional internal combustion engine (ICE) vehicles by comparing the energy and GHG emission values in the updated and previous versions of GREET 2. Finally, results from a sensitivity analysis are presented for identifying life cycle parameters that most affect vehicle life cycle estimates.
