Hydraulic fracturing is a very robust tool in terms of not only increasing production in tight re... more Hydraulic fracturing is a very robust tool in terms of not only increasing production in tight reservoirs but also accelerating production from reservoirs with higher permeability and porosity. The success of a hydraulic fracturing treatment is highly correlated with control of the created fracture geometry. Control of fracture growth and geometry is challenging in formations where the boundary lithologies are not highly stressed compared to the pay zone, allowing out-of-zone migration of fractures. As several factors influence the growth and geometry of fractures, including the reservoir, wellbore, and fluid/proppant parameters, it requires a good understanding of reservoir parameters, including stress distribution along with appropriate use of corresponding wellbore components and fluid/proppant for successful and efficient results. Our objective is to outline the main differences between different fracture models along with the key parameters and their significance in fracture performance. Fracture treatment designs involve selecting fracturing fluids, additives, proppant materials, injection rate, pump schedule, and fracture dimensions. Although hydraulic fracturing has become more important due to development of unconventional resources in tight formations, the use of fracture models before implementation of the treatment is limited, leading to undesired fractures either with limited growth or propagation out of zone. Literature lacks a study that combines the evaluation of different fracture models in an optimization process where economics is taken into account with an objective function maximizing the net present value (NPV), providing detailed information on the financial side of this technical phenomenon, too. In this study, different numerical fracture models are used to design the fractures in a tight oil reservoir, the performance of designed fractures are analyzed to measure the success of different numerical models applied in design along with an overview of critical operational parameters together with the significance of parameters, including size, number, and location, phasing angle of perforations, fluid and proppant type, rock strength, porosity, and permeability on fracture design optimization‥ In our paper, a comprehensive literature review of previous studies on tight oil reservoirs and hydraulic fracturing provides a thorough theoretical comparison of fracture models used in oil industry along with the analysis of significance of each factor throughout the process of hydraulic fracturing tight oil reservoirs. An objective function that maximizes NPV is used in this study investigating the phenomenon not only from
Decision making in the oilfield is a crucial process in petroleum oilfield activities where numer... more Decision making in the oilfield is a crucial process in petroleum oilfield activities where numerous attributes and uncertainties exist in the complete process. In the development process of new fields, a well-organized historical database to minimize uncertainty is important as it decreases risks, provides better insight and robustness in decision making. Statistics is a strong tool to turn information or data into knowledge when used with care and physical understanding of the cause-effect relation between the attributes and the outcome. Unfortunately, historical data and learnings from the past cannot be used in an efficient way in oilfield decisions due to lack of systematically organized historical data where there is a huge potential of turning terrabytes of data into knowledge and understanding, for more successful decisions and results. On the other hand, using historical data on reservoir enginnering studies are generally very complex since it requires integration of vast amount data from several diciplines that have different data sources and from different scales, which is sometimes leading modelers to prefer new data collections from the field rather than using complex historical data on hand. In this sense, a multi-attribute based statistical model for each reservoir will greatly enhance the outcomes of future actions through bringing a statistical understanding to a physically more complex relation between the causes and effects or in other words, attributes and results. The data driven models are established for the desired phenomena by means of collecting relevant historical data, after which a multivariate regression is carried out to show the significance of each attribute in the model. These statistical models are then used to make future decisions in the same reservoir in such a way that attributes can be selected within the optimum ranges that yield best results and outcome. Attributes can consist of events and key parameters that influence the outcome. The model is illustrated to investigate the factor affecting the performance of vertical and horizontal wells in tight reservoirs. The data-driven model is validated with the numerical reservoir simulation model and used to determine the significance of each parameter and the optimum operating intervals.
Hydraulic fracturing is a very robust tool in terms of not only increasing production in tight re... more Hydraulic fracturing is a very robust tool in terms of not only increasing production in tight reservoirs but also accelerating production from reservoirs with higher permeability and porosity. The success of a hydraulic fracturing treatment is highly correlated with control of the created fracture geometry. Control of fracture growth and geometry is challenging in formations where the boundary lithologies are not highly stressed compared to the pay zone, allowing out-of-zone migration of fractures. As several factors influence the growth and geometry of fractures, including the reservoir, wellbore, and fluid/proppant parameters, it requires a good understanding of reservoir parameters, including stress distribution along with appropriate use of corresponding wellbore components and fluid/proppant for successful and efficient results. Our objective is to outline the main differences between different fracture models along with the key parameters and their significance in fracture performance. Fracture treatment designs involve selecting fracturing fluids, additives, proppant materials, injection rate, pump schedule, and fracture dimensions. Although hydraulic fracturing has become more important due to development of unconventional resources in tight formations, the use of fracture models before implementation of the treatment is limited, leading to undesired fractures either with limited growth or propagation out of zone. Literature lacks a study that combines the evaluation of different fracture models in an optimization process where economics is taken into account with an objective function maximizing the net present value (NPV), providing detailed information on the financial side of this technical phenomenon, too. In this study, different numerical fracture models are used to design the fractures in a tight oil reservoir, the performance of designed fractures are analyzed to measure the success of different numerical models applied in design along with an overview of critical operational parameters together with the significance of parameters, including size, number, and location, phasing angle of perforations, fluid and proppant type, rock strength, porosity, and permeability on fracture design optimization‥ In our paper, a comprehensive literature review of previous studies on tight oil reservoirs and hydraulic fracturing provides a thorough theoretical comparison of fracture models used in oil industry along with the analysis of significance of each factor throughout the process of hydraulic fracturing tight oil reservoirs. An objective function that maximizes NPV is used in this study investigating the phenomenon not only from
Decision making in the oilfield is a crucial process in petroleum oilfield activities where numer... more Decision making in the oilfield is a crucial process in petroleum oilfield activities where numerous attributes and uncertainties exist in the complete process. In the development process of new fields, a well-organized historical database to minimize uncertainty is important as it decreases risks, provides better insight and robustness in decision making. Statistics is a strong tool to turn information or data into knowledge when used with care and physical understanding of the cause-effect relation between the attributes and the outcome. Unfortunately, historical data and learnings from the past cannot be used in an efficient way in oilfield decisions due to lack of systematically organized historical data where there is a huge potential of turning terrabytes of data into knowledge and understanding, for more successful decisions and results. On the other hand, using historical data on reservoir enginnering studies are generally very complex since it requires integration of vast amount data from several diciplines that have different data sources and from different scales, which is sometimes leading modelers to prefer new data collections from the field rather than using complex historical data on hand. In this sense, a multi-attribute based statistical model for each reservoir will greatly enhance the outcomes of future actions through bringing a statistical understanding to a physically more complex relation between the causes and effects or in other words, attributes and results. The data driven models are established for the desired phenomena by means of collecting relevant historical data, after which a multivariate regression is carried out to show the significance of each attribute in the model. These statistical models are then used to make future decisions in the same reservoir in such a way that attributes can be selected within the optimum ranges that yield best results and outcome. Attributes can consist of events and key parameters that influence the outcome. The model is illustrated to investigate the factor affecting the performance of vertical and horizontal wells in tight reservoirs. The data-driven model is validated with the numerical reservoir simulation model and used to determine the significance of each parameter and the optimum operating intervals.
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