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resource in place estimate
Petroleum Engineering, University of Houston, 2. Metarock Laboratories, 3. Department of Earth and Atmospheric Sciences, University of Houston) 16:00-16:30 Break and Walk to Bizzell Museum 16:30-17:30 Tour: History of Science Collections, Bizzell Memorial Library, The University of Oklahoma 17:30-19:00 Networking Reception: Thurman J. White Forum Building
- Research Report > New Finding (0.93)
- Overview (0.68)
- Geology > Geological Subdiscipline > Geomechanics (1.00)
- Geology > Mineral (0.72)
- Geology > Rock Type > Sedimentary Rock > Carbonate Rock (0.68)
- (2 more...)
- Geophysics > Borehole Geophysics (1.00)
- Geophysics > Seismic Surveying > Seismic Modeling > Velocity Modeling (0.93)
Integration of light air-coupled ground-penetrating radar (GPR) on drones has been booming in the last years. Compared to ground surveys, larger spatial coverage, shorter survey times and better safety for operators are obvious advantages. However, the data quality (signal-to-noise ratio and resolution) as well as the penetration depth are also greatly reduced in most cases. These issues are related to the physics of electromagnetic (EM) wave propagation and mostly because the amount of energy reflected at the interface between the air and the propagation medium (ground, rock, soil, snow, ice) is large. In many cases, the amount of energy reflected at the air/soil interface is greater than 50 %, making the loss in data quality and penetration depth too large to make drone GPR a viable solution compared to conventional ground surveys.
ABSTRACT Ground-penetrating radar systems with a single central frequency suffer limitations due to the unavoidable trade-off between resolution and penetration depth that multifrequency equipments can overcome. A new semisupervised multifrequency merging algorithm was developed based on deep learning and specifically on bi-directional long-short term memory to automatically merge varying numbers of data sets collected at different frequencies. A new training strategy, based only on the data set of interest, without synthetic or real training data sets was implemented. The proposed methodology is tested on synthetic and field data, to evaluate its performance and robustness. The merging algorithm can manage the complementarity of information at different central frequencies, properly merging different types of data. Results indicate not only a smooth transition in time, but, even more important, a remarkable broadening of the bandwidth thus increasing the overall resolution. Our approach is not limited to specific frequency components or geologic settings but can be potentially exploited to merge any type of data set with different spectral components.
Abstract In unconventional reservoirs, spacing and stacking directly influence the hydrocarbon resources available to be drained by a given lateral. Hypothetically, these available resources, rock properties and stimulation effectiveness will drive the well performance (i.e., Estimated Ultimate Recovery (EUR)). Characterization of the effectively contacted volume is an important element in understanding the well performance and the depletion efficiency of the intended development. This paper will present a simple but novel way of characterizing a well's drainage volume and demonstrates how this characterization can be applied to improve the understanding of expected well recovery, primary depletion efficiency (i.e., recovery factor), and their relationship with petrophysics and geology. The methodology is proposed as a method to help lead to optimum development and resource economic value for the operator. The proposed method uses the concept of no flow boundaries driven by frac geometry established between wells to define a drainage polygon surrounding neighboring laterals. Incorporating supplementary datasets allows further characterization (i.e., well-log to obtain fluid-in-place distribution). The method provides insights which can be tied back to the well performance. For example, the method shows the importance of geology and petrophysics, reflected through the Original Oil in Place (OOIP) within the drainage volume, driving the well's EUR and recovery factor. Significance/Novelty: Improved reservoir drainage volume and well performance characterization can significantly impact the optimum development plan, maximizing both the exploitation efficiency and value for operators.
- North America > United States > Texas > Permian Basin > Yeso Formation (0.99)
- North America > United States > Texas > Permian Basin > Yates Formation (0.99)
- North America > United States > Texas > Permian Basin > Wolfcamp Formation (0.99)
- (24 more...)
Integration of light air-coupled ground-penetrating radar (GPR) on drones has been booming in the last years. Compared to ground surveys, larger spatial coverage, shorter survey times and better safety for operators are obvious advantages. However, the data quality (signal-to-noise ratio and resolution) as well as the penetration depth are also greatly reduced in most cases. These issues are related to the physics of electromagnetic (EM) wave propagation and mostly because the amount of energy reflected at the interface between the air and the propagation medium (ground, rock, soil, snow, ice) is large. In many cases, the amount of energy reflected at the air/soil interface is greater than 50 %, making the loss in data quality and penetration depth too large to make drone GPR a viable solution compared to conventional ground surveys.
Volumetrics is a static measurement based on a geologic model that uses geometry to describe the volume of hydrocarbons in the reservoir. Volumetrics, volumetric estimation, is currently the only way that is available to assess hydrocarbons-in-place prior to drilling. The purpose of calculating a volumetric estimation is to evaluate a reservoir and calculate the potential reserves of the reservoir in question. Once drilling has started pressure and production data is collected giving a greater insight into the volume that needs to be evaluated. Volumetrics is an integration of geological, fluid and the modeled relationships.
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)
Ground-penetrating radar (GPR) is an active geophysical subsurface exploration method. The method uses the reception of radiated electromagnetic waves that have entered the ground and returned after reflecting off objects in the subsurface. The method can be compared with active seismic acquisition which uses reflected acoustic waves. Although both use reflected waves, they are fundamentally different and thus have distinct applications. GPR data can be acquired in a number of ways: using small-area ground-based instrumentation such as is displayed in Figure 1 to the left, using aircraft-based instrumentation to gather data for a larger area, and using satellite-based observations to gather regional-scale data.
- Geology > Rock Type (0.71)
- Geology > Geological Subdiscipline (0.49)
- Geophysics > Seismic Surveying (1.00)
- Geophysics > Electromagnetic Surveying (1.00)
Steven Cosway was a 2006 recipient of the Cecil Green Enterprise Award with Peter Annan, David Leggatt, and Lowry Chua as a co-founder of Sensors & Software. SEG is honoring Peter Annan, David Leggatt, Steven Cosway, and Lowry Chua with the Cecil Green Enterprise Award for founding Sensors & Software, Inc. in 1988. The company was established to commercialize the ground-penetrating radar technology developed by A-Cubed, a research and development entity. With no outside funding and no salaries for the initial year, the four founders worked together to launch Sensors & Software. Today the company employs 50 people, has products working in 80-90 countries around the world, has a 30-40% market share globally for GPR equipment, and US 5-7 million in annual revenue.
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)
Lowry Chua was a 2006 recipient of the Cecil Green Enterprise Award with Peter Annan, David Leggatt, and Steven Cosway as a co-founder of Sensors & Software. SEG is honoring Peter Annan, David Leggatt, Steven Cosway, and Lowry Chua with the Cecil Green Enterprise Award for founding Sensors & Software, Inc. in 1988. The company was established to commercialize the ground-penetrating radar technology developed by A-Cubed, a research and development entity. With no outside funding and no salaries for the initial year, the four founders worked together to launch Sensors & Software. Today the company employs 50 people, has products working in 80-90 countries around the world, has a 30-40% market share globally for GPR equipment, and US 5-7 million in annual revenue.
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)
- Information Technology > Knowledge Management (0.40)
- Information Technology > Communications > Collaboration (0.40)