scholarly journals Enhanced seismic interpretation using well logs and VSP data.

1986 ◽  
Vol 51 (1) ◽  
pp. 48-66
Author(s):  
Alastair John WINCHESTER ◽  
Shigeaki ASAKURA
Keyword(s):  
Seismic Interpretation ◽  
Well Logs ◽  
Vsp Data

Tomographic determination of three‐dimensional seismic velocity structure using well logs, vertical seismic profiles, and surface seismic data

Geophysics ◽  
1987 ◽  
Vol 52 (8) ◽  
pp. 1085-1098 ◽  
Author(s):  
Stephen K. L. Chiu ◽  
Robert R. Stewart
Keyword(s):  
Velocity Structure ◽  
Seismic Velocity ◽  
Random Noise ◽  
Three Dimensional ◽  
Real Data ◽  
Well Logs ◽  
Seismic Profiles ◽  
Vertical Seismic Profiles ◽  
The Earth ◽  
Vsp Data

A tomographic technique (traveltime inversion) has been developed to obtain a two‐ or three‐dimensional velocity structure of the subsurface from well logs, vertical seismic profiles (VSP), and surface seismic measurements. The earth was modeled by continuous curved interfaces (polynomial or sinusoidal series), separating regions of constant velocity or transversely isotropic velocity. Ray tracing for each seismic source‐receiver pair was performed by solving a system of nonlinear equations which satisfy the generalized Snell’s law. Surface‐to‐borehole and surface‐to‐surface rays were included. A damped least‐squares formulation provided the updating of the earth model by minimizing the difference between the traveltimes picked from the real data and calculated traveltimes. Synthetic results indicated the following conclusions. For noise‐free cases, the inversion converged closely from the initial guess to the true model for either surface or VSP data. Adding random noise to the observations and performing the inversion indicated that (1) using surface data alone allows reconstruction of the broad velocity structure but with some inaccuracy; (2) using VSP data alone gives a very accurate but laterally limited velocity structure; and (3) the integration of both data sets produces a more laterally extensive, accurate image of the subsurface. Finally, a field example illustrates the viability of the method to construct a velocity structure from real data.

Did you smooth your well logs the right way for seismic interpretation?

2011 ◽  
Vol 8 (4) ◽  
pp. 514-523 ◽  
Author(s):  
Mathieu J Duchesne ◽  
Philippe Gaillot
Keyword(s):  
Seismic Interpretation ◽  
Well Logs ◽  
The Right

scholarly journals Possibility of a more detailed seismic interpretation within the Miocene formations of the Carpathian Foredeep based on the well logs interpretation

Nafta-Gaz ◽  
2019 ◽  
Vol 75 (9) ◽  
pp. 527-544
Author(s):  
Andrzej Urbaniec ◽  
◽  
Marek Stadtmüller ◽  
Robert Bartoń ◽  
◽  
...  
Keyword(s):  
Seismic Interpretation ◽  
Well Logs ◽  
Carpathian Foredeep ◽  
Well Logs Interpretation

scholarly journals Hydrocarbon Reservoir Characterization of ‘Khume’ Field, Offshore Niger Delta, Nigeria

2021 ◽  
pp. 47-60
Author(s):  
K. A. Obakhume ◽  
O. M. Ekeng ◽  
C. Atuanya
Keyword(s):  
Niger Delta ◽  
Reservoir Characterization ◽  
Gamma Ray ◽  
Water Saturation ◽  
Seismic Interpretation ◽  
Effective Porosity ◽  
Well Logs ◽  
Integrative Approach ◽  
Neutron Density ◽  
Hydrocarbon Reservoir

The integrative approach of well log correlation and seismic interpretation was adopted in this study to adequately characterize and evaluate the hydrocarbon potentials of Khume field, offshore Niger Delta, Nigeria. 3-D seismic data and well logs data from ten (10) wells were utilized to delineate the geometry of the reservoirs in Khume field, and as well as to estimate the hydrocarbon reserves. Three hydrocarbon-bearing reservoirs of interest (D-04, D-06, and E-09A) were delineated using an array of gamma-ray logs, resistivity log, and neutron/density log suites. Stratigraphic interpretation of the lithologies in Khume field showed considerable uniform gross thickness across all three sand bodies. Results of petrophysical evaluations conducted on the three reservoirs correlated across the field showed that; shale volume ranged from 7-14%, total and effective porosity ranged from 19-26% and 17-23% respectively, NTG from 42 to 100%, water saturation from 40%-100% and permeability from 1265-2102 mD. Seismic interpretation established the presence of both synthetic and antithetic faults. A total of six synthetic and four antithetic faults were interpreted from the study area. Horizons interpretation was done both in the strike and dip directions. Time and depth structure maps revealed reservoir closures to be anticlinal and fault supported in the field. Hydrocarbon volumes were calculated using the deterministic (map-based) approach. Stock tank oil initially in place (STOIIP) for the proven oil column estimated for the D-04 reservoir was 11.13 MMSTB, 0.54 MMSTB for D-06, and 2.16 MMSTB for E-09A reservoir. For the possible oil reserves, a STOIIP value of 7.28 MMSTB was estimated for D-06 and 6.30 MMSTB for E-09A reservoir, while a hydrocarbon initially in place (HIIP) of 4.13 MMSTB of oil equivalents was derived for the undefined fluid (oil/gas) in D-06 reservoir. A proven gas reserve of 1.07 MMSCF was derived for the D-06 reservoir. This study demonstrated the effectiveness of 3-D seismic and well logs data in delineating reservoir structural architecture and in estimating hydrocarbon volumes

Inversion of multicomponent 3D vertical seismic profile data for porosity and CO2 saturation at the Cranfield injection site, Cranfield, MS

2014 ◽  
Vol 2 (2) ◽  
pp. SE77-SE89 ◽  
Author(s):  
Russell W. Carter ◽  
Kyle T. Spikes ◽  
Thomas Hess
Keyword(s):  
Rock Physics ◽  
Seismic Profile ◽  
Pore Fluid ◽  
Well Logs ◽  
Fluid Composition ◽  
Vertical Seismic Profile ◽  
Fluid Saturation ◽  
Vsp Data ◽  
Physics Model ◽  
Rock Physics Model

Studying how injected [Formula: see text] affects the seismic response of reservoir rocks is important because it can improve subsurface characterization where [Formula: see text] injection is taking place. This study uses multicomponent data from a 3D vertical seismic profile (VSP) and well logs to model and invert probabilistically for the porosity and [Formula: see text] saturation at the Cranfield reservoir. The well logs were used to calibrate a rock-physics model. Once the accuracy of the model was verified, P-impedance and [Formula: see text]/[Formula: see text] from inverted multicomponent VSP data were used to estimate the porosity and fluid saturation. This inversion generated probabilistic estimates of porosity and fluid saturation for the area of the reservoir sampled by PP- and PS-waves. Inversion results using the measured well log data for calibration indicated that the model was able to estimate porosity with a relatively high degree of accuracy, with the root-mean-square (rms) error being less than 3% for all calibration tests. Pore-fluid composition was estimated, however, with reduced accuracy, with rms errors ranging from 6% to 22% depending on the composition of the calibration fluid. Results from integrating the multicomponent VSP data with the rock-physics model indicated that estimated reservoir porosities are quite close to measured values at an observation well. Pore-fluid composition estimates indicated that this method can differentiate between areas containing [Formula: see text] and those that do not.

scholarly journals Unsupervised Machine Learning Applied to Seismic Interpretation: Towards an Unsupervised Automated Interpretation Tool

Sensors ◽  
2021 ◽  
Vol 21 (19) ◽  
pp. 6347
Author(s):  
Alimed Celecia ◽  
Karla Figueiredo ◽  
Carlos Rodriguez ◽  
Marley Vellasco ◽  
Edwin Maldonado ◽  
...  
Keyword(s):  
Machine Learning ◽  
Seismic Data ◽  
Clustering Algorithms ◽  
Seismic Interpretation ◽  
Hydrocarbon Exploration ◽  
Seismic Facies ◽  
Well Logs ◽  
Unsupervised Machine Learning ◽  
High Data ◽  
Novel Approach

Seismic interpretation is a fundamental process for hydrocarbon exploration. This activity comprises identifying geological information through the processing and analysis of seismic data represented by different attributes. The interpretation process presents limitations related to its high data volume, own complexity, time consumption, and uncertainties incorporated by the experts’ work. Unsupervised machine learning models, by discovering underlying patterns in the data, can represent a novel approach to provide an accurate interpretation without any reference or label, eliminating the human bias. Therefore, in this work, we propose exploring multiple methodologies based on unsupervised learning algorithms to interpret seismic data. Specifically, two strategies considering classical clustering algorithms and image segmentation methods, combined with feature selection, were evaluated to select the best possible approach. Additionally, the resultant groups of the seismic data were associated with groups obtained from well logs of the same area, producing an interpretation with aggregated lithologic information. The resultant seismic groups correctly represented the main seismic facies and correlated adequately with the groups obtained from the well logs data.

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