Zobrazeno 1 - 10
of 66
pro vyhledávání: '"C. Esmersoy"'
Publikováno v:
Second EAGE Workshop on Deepwater Exploration in Mexico: Knowledge transfer and collaboration from shelf to deepwater.
Autor:
C. Esmersoy, Soumya Roy
Publikováno v:
First EAGE/PESGB Workshop on Velocities.
A detailed understanding of velocities in the overburden is critical for the planning and successful drilling of wells. An accurate 3D velocity model can play an important role in identifying potential drilling hazards along the well path. It is well
Autor:
J. Mathewson, C. Esmersoy
Publikováno v:
Proceedings.
There is always uncertainty involved in drilling a well, arising from incomplete knowledge of the subsurface. Before drilling starts an earth model in the vicinity of the well is normally produced using available information relating to geology, rock
Publikováno v:
10th Annual International Symposium on Geoscience and Remote Sensing.
Autor:
C. Esmersoy, Jae Lim
Publikováno v:
ICASSP
The performance of a pulse code modulation (PCM) speech coding system that incorporates quantization noise reduction is evaluated by a speech quality test. The results of the test indicate that quantization noise reduction improves the performance of
Autor:
S. Mallick, C. Esmersoy
Publikováno v:
67th EAGE Conference & Exhibition.
Publikováno v:
All Days.
A new technique provides conventional borehole seismic measurements in real time while a well is being drilled. The system consists of a logging-while-drilling (LWD) tool with seismic sensors positioned near the drill bit, a seismic source at the sur
Publikováno v:
All Days.
The limited resolution of conventional seismic surveys often results in challenges to successful reservoir development. Reservoir structure and thickness may not be determined from the seismic to the level of detail necessary, affecting the placement
Conference
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Autor:
C. Esmersoy
Publikováno v:
IEEE Transactions on Acoustics, Speech, and Signal Processing. 33:315-316
In this note, an upper bound on the signal power corresponding to a hypothesized model is derived assuming unknown signal and noise characteristics. A residual matrix is obtained from the observed data correlation matrix by subtracting a weighted out