Reservoir Prediction and Application Case by Seismic Pattern Recognition in Dense Well Pattern Development Area

2016 ◽  
Author(s):  
Jiang Yan ◽  
Cheng Shunguo ◽  
Zou Mingde ◽  
Zhang Xiuli ◽  
Fu Xiandi
Keyword(s):  
Pattern Recognition ◽  
Reservoir Prediction ◽  
Well Pattern ◽  
Pattern Development ◽  
Development Area

Reservoir prediction via SVM pattern recognition

2004 ◽  
Author(s):  
Jiakang Li ◽  
John Castagna ◽  
Dong‐an Li ◽  
Xiyan Bian
Keyword(s):  
Pattern Recognition ◽  
Reservoir Prediction

An Integrated Reservoir Predicting Approach with Geological Constraints from Stratigraphic Forward Modeling of the Miocene Fan Delta in Albert Basin, Uganda

2021 ◽  
Author(s):  
Wei Xu ◽  
Lei Fang ◽  
Jingyun Zou ◽  
Fuxin Guo ◽  
Yingchun Zhang ◽  
...  
Keyword(s):  
Deposition Process ◽  
Sediment Supply ◽  
Heavy Minerals ◽  
Depositional Model ◽  
Sedimentary Process ◽  
Reservoir Prediction ◽  
Fan Delta ◽  
Well Pattern ◽  
Well Data ◽  
Exploration And Development

Abstract Reservoir prediction is a core area of research in oilfield exploration and development, and it is generally constructed on a combination of well data, seismic attributes or inversion. However, reservoir prediction in sparse well areas poses great challenges due to insufficient well control. If the quality of seismic data is poor, the spatial distribution characteristics of reservoirs cannot be effectively characterized through inversion or attribute analysis, which seriously affects the prediction accuracy. This paper proposes a new method to solve the difficulty in reservoir prediction of oilfields with sparse data and poor quality seismic cube, which evolves from depositional models, forward stratigraphic modeling (FSM) to geocellular modeling. First, based on the comprehensive analysis of core, seismic, grain size, heavy minerals, dip data, it is believed that a special fan delta developed in the Miocene strata in the south of Albert Basin. The reservoirs are dominated by distributary channels, which are in medium-coarse grains, and the provenance is from the southwest to flowing to the northeast. The formation thickness of the stratum decreases from the boundary fault to the direction of the basin. Then, the input parameters of FSM modeling are quantitatively expressed based on the sedimentary model research, including model boundary conditions, basic input information, sediment supply and transportation. FSM results were used to quantitatively characterize the deposition process. The FSM simulation results are compared with the depositional model and well data to verify the reliability. Finally, the shale content model in FSM results is resampled to the geocellular grids and used as the constraint for facies model and property model in geological modeling. This model is used for well pattern design and optimization. This new approach integrates the conceptual depositional model with quantitative FSM results. It improves the accuracy of reservoir prediction and provides a new technical workflow for reservoir characterization. Furthermore, it helps to obtain more insight into the sedimentary process and reduces the risk of oilfield exploration and development.

Reservoir prediction in a development area with a high-density well pattern using seismic sedimentology: An example from the BB2 block, Changyuan LMD oil field, Songliao Basin, China

2015 ◽  
Vol 3 (3) ◽  
pp. SS87-SS99 ◽  
Author(s):  
Shunguo Cheng ◽  
Yan Jiang ◽  
Jie Li ◽  
Cao Li ◽  
Yanhui Wang ◽  
...  
Keyword(s):  
Average Density ◽  
Oil Field ◽  
Reservoir Prediction ◽  
Log Data ◽  
Field Development ◽  
Well Pattern ◽  
Infill Drilling ◽  
Seismic Sedimentology ◽  
Reservoir Description ◽  
Sand Bodies

The Daqing Changyuan oil field is primarily composed of large, fluvial-deltaic thin sandstones and shales with a high degree of heterogeneity. Over the past 50 years of development, the geologic study of this reservoir has relied on a large amount of well-log data in the field. However, a detailed reservoir description based only on wireline-log data cannot meet the requirements of oil field development. There is still some uncertainty about the sand boundary and geometry, due to reliance only on data from fields with an average density of approximately [Formula: see text]. Such uncertainty may severely affect the potential for producing the remaining oil in these mature oil fields. In this study, seismic-sedimentology guided reservoir prediction is examined in an area of dense wells in BB2 block in the Changyuan LMD oil field. The spatial distribution of channel-sand bodies was identified and recognized by facies analysis, sandstone thickness mapping, and seismic stratal slicing of reservoir units, using the principles and methods of seismic sedimentology. The results showed that the seismic amplitude can be correlated to log lithologies. The interpretation of sandstone can be improved by 90°-phase seismic data, and the distribution of channel sand with a thickness greater than 5 m can be directly predicted. The identification and prediction of the boundaries of channel-sand bodies are thus improved. The results have proved useful in new infill drilling and reperforations.

Software pattern recognition applied to nanodiffraction patterns from a STEM instrument

1986 ◽  
Vol 44 ◽  
pp. 694-695
Author(s):  
G.Y. Fan ◽  
J.M. Cowley
Keyword(s):  
Image Processing ◽  
Pattern Recognition ◽  
Computer Software ◽  
Processing System ◽  
Light Level ◽  
Host Computer ◽  
Low Light ◽  
Image Processing System ◽  
Recent Developments ◽  
On Line

In recent developments, the ASU HB5 has been modified so that the timing, positioning, and scanning of the finely focused electron probe can be entirely controlled by a host computer. This made the asynchronized handshake possible between the HB5 STEM and the image processing system which consists of host computer (PDP 11/34), DeAnza image processor (IP 5000) which is interfaced with a low-light level TV camera, array processor (AP 400) and various peripheral devices. This greatly facilitates the pattern recognition technique initiated by Monosmith and Cowley. Software called NANHB5 is under development which, instead of employing a set of photo-diodes to detect strong spots on a TV screen, uses various software techniques including on-line fast Fourier transform (FFT) to recognize patterns of greater complexity, taking advantage of the sophistication of our image processing system and the flexibility of computer software.

Bayesian removal of noise for increased sensitivity in vector pattern recognition lattice imaging of interfaces

1993 ◽  
Vol 51 ◽  
pp. 994-995
Author(s):  
L. Fei ◽  
P. Fraundorf
Keyword(s):  
Pattern Recognition ◽  
Perfect Crystal ◽  
Unit Cell ◽  
Ion Milling ◽  
Beam Radiation ◽  
Major Interest ◽  
Unit Cells ◽  
Mapping Process ◽  
Increased Sensitivity ◽  
Electron Microscopes

Interface structure is of major interest in microscopy. With high resolution transmission electron microscopes (TEMs) and scanning probe microscopes, it is possible to reveal structure of interfaces in unit cells, in some cases with atomic resolution. A. Ourmazd et al. proposed quantifying such observations by using vector pattern recognition to map chemical composition changes across the interface in TEM images with unit cell resolution. The sensitivity of the mapping process, however, is limited by the repeatability of unit cell images of perfect crystal, and hence by the amount of delocalized noise, e.g. due to ion milling or beam radiation damage. Bayesian removal of noise, based on statistical inference, can be used to reduce the amount of non-periodic noise in images after acquisition. The basic principle of Bayesian phase-model background subtraction, according to our previous study, is that the optimum (rms error minimizing strategy) Fourier phases of the noise can be obtained provided the amplitudes of the noise is given, while the noise amplitude can often be estimated from the image itself.

Pattern Recognition: The Basis of All Cognition?

1989 ◽  
Vol 34 (11) ◽  
pp. 988-989
Author(s):  
Erwin M. Segal
Keyword(s):  
Pattern Recognition
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