Horizontal Well Data in Seismic Inversion and Reservoir Description

Author(s):  
G. Chen ◽  
H. P. Chen ◽  
Y. F. Xie ◽  
Z. H. Fang ◽  
Y. J. Zhang ◽  
...  
2019 ◽  
Vol 38 (6) ◽  
pp. 474-479
Author(s):  
Mohamed G. El-Behiry ◽  
Said M. Dahroug ◽  
Mohamed Elattar

Seismic reservoir characterization becomes challenging when reservoir thickness goes beyond the limits of seismic resolution. Geostatistical inversion techniques are being considered to overcome the resolution limitations of conventional inversion methods and to provide an intuitive understanding of subsurface uncertainty. Geostatistical inversion was applied on a highly compartmentalized area of Sapphire gas field, offshore Nile Delta, Egypt, with the aim of understanding the distribution of thin sands and their impact on reservoir connectivity. The integration of high-resolution well data with seismic partial-angle-stack volumes into geostatistical inversion has resulted in multiple elastic property realizations at the desired resolution. The multitude of inverted elastic properties are analyzed to improve reservoir characterization and reflect the inversion nonuniqueness. These property realizations are then classified into facies probability cubes and ranked based on pay sand volumes to quantify the volumetric uncertainty in static reservoir modeling. Stochastic connectivity analysis was also applied on facies models to assess the possible connected volumes. Sand connectivity analysis showed that the connected pay sand volume derived from the posterior mean of property realizations, which is analogous to deterministic inversion, is much smaller than the volumes generated by any high-frequency realization. This observation supports the role of thin interbed reservoirs in facilitating connectivity between the main sand units.


2021 ◽  
Vol 19 (3) ◽  
pp. 125-138
Author(s):  
S. Inichinbia ◽  
A.L. Ahmed

This paper presents a rigorous but pragmatic and data driven approach to the science of making seismic-to-well ties. This pragmatic  approach is consistent with the interpreter’s desire to correlate geology to seismic information by the use of the convolution model,  together with least squares matching techniques and statistical measures of fit and accuracy to match the seismic data to the well data. Three wells available on the field provided a chance to estimate the wavelet (both in terms of shape and timing) directly from the seismic and also to ascertain the level of confidence that should be placed in the wavelet. The reflections were interpreted clearly as hard sand at H1000 and soft sand at H4000. A synthetic seismogram was constructed and matched to a real seismic trace and features from the well are correlated to the seismic data. The prime concept in constructing the synthetic is the convolution model, which represents a seismic reflection signal as a sequence of interfering reflection pulses of different amplitudes and polarity but all of the same shape. This pulse shape is the seismic wavelet which is formally, the reflection waveform returned by an isolated reflector of unit strength at the target  depth. The wavelets are near zero phase. The goal and the idea behind these seismic-to-well ties was to obtain information on the sediments, calibration of seismic processing parameters, correlation of formation tops and seismic reflectors, and the derivation of a  wavelet for seismic inversion among others. Three seismic-to-well ties were done using three partial angle stacks and basically two formation tops were correlated. Keywords: seismic, well logs, tie, synthetics, angle stacks, correlation,


2017 ◽  
Author(s):  
Baohua Chang ◽  
Hedong Sun ◽  
Wen Cao ◽  
Zhiliang Liu ◽  
Shiyin Li ◽  
...  

2019 ◽  
Vol 125 ◽  
pp. 15001
Author(s):  
Benny Abraham Bungasalu ◽  
M. Syamsu Rosid ◽  
Don S. Basuki

The subsurface pressure analysis is used to detect the overpressure and problems in the well that will be drilled based on exploration well data. Various problems were found while drilling operations carried out on A and B wells, namely, Kick and Pipe sticking which cause a high Non-Productive Time (NPT). This research is conducted to identify the mechanism of overpressure formation in Tight Sand Gas and Shale Gas in the Jambi Sub-Basin. Furthermore, to predict pore pressure using the Drilling Efficiency and Mechanical Specific Energy (DEMSE) and Bowers method. The final result will be a 3D pore pressure cube in the area based on quantitative analysis of post-stack seismic inversion. The results of the pore pressure analysis from the wells and the 3D pore pressure model indicate that top of overpressure occurs in the Gumai Formation, then it is decreasing gradually approaching the hydrostatic pressure on the Basement. The mechanisms of overpressure are caused by under compaction, fluid expansion (kerogen maturation). The Gumai Formation and Talang Akar Formation are shale rocks so the type of mud weight that is well used is oil based mud (OBM).


1990 ◽  
Vol 30 (1) ◽  
pp. 342 ◽  
Author(s):  
W. J. Mudge ◽  
A. B. Thomson

RESMAP is an Exxon proprietary program that makes possible the building of three-dimensional geological models. The models consist of millions of tabular cells contained within a stratigraphic framework. The models are generated by the integration of log analysis, seismic interpretation and the geologist's well picks and interpretation.RESMAP models are easy to access for generating structural and stratigraphic geological cross-sections. Maps such as structure, top of porosity and net sand percentage can be generated as well as subcrop and supercrop maps. Hydrocarbon-in-place estimates are also made from the model.The Kingfish and West Kingfish fields each possess a complex stratigraphic framework and exhibit rapid facies changes in the nearshore marine sediments. Techniques available in RESMAP enable the integration of the well data and seismic maps to produce an accurate stratigraphic framework containing the geometries of truncation onlap and downlap. The interpolation of porosity data from the wells successfully captures the facies distribution. This is due to the excellent well spacing in the vicinity of the facies changes and the ability to place geological constraints on the interpolation.The model provides a valuable communication tool for the mass of data present in a field. The model can also be updated quickly upon receipt of new data, providing a dynamic reservoir description on which to base future field management.


2002 ◽  
Vol 21 (10) ◽  
pp. 1002-1008 ◽  
Author(s):  
Marcelo Benabentos ◽  
Subhashis Mallick ◽  
Mario Sigismondi ◽  
Juan Soldo

2003 ◽  
Author(s):  
Emilio Sanchez ◽  
Luis Pianelli ◽  
Carlos Saavedra ◽  
Marcelo Benabentos ◽  
Juan Perdomo ◽  
...  

Geophysics ◽  
2010 ◽  
Vol 75 (6) ◽  
pp. O57-O67 ◽  
Author(s):  
Daria Tetyukhina ◽  
Lucas J. van Vliet ◽  
Stefan M. Luthi ◽  
Kees Wapenaar

Fluvio-deltaic sedimentary systems are of great interest for explorationists because they can form prolific hydrocarbon plays. However, they are also among the most complex and heterogeneous ones encountered in the subsurface, and potential reservoir units are often close to or below seismic resolution. For seismic inversion, it is therefore important to integrate the seismic data with higher resolution constraints obtained from well logs, whereby not only the acoustic properties are used but also the detailed layering characteristics. We have applied two inversion approaches for poststack, time-migrated seismic data to a clinoform sequence in the North Sea. Both methods are recursive trace-based techniques that use well data as a priori constraints but differ in the way they incorporate structural information. One method uses a discrete layer model from the well that is propagated laterally along the clinoform layers, which are modeled as sigmoids. The second method uses a constant sampling rate from the well data and uses horizontal and vertical regularization parameters for lateral propagation. The first method has a low level of parameterization embedded in a geologic framework and is computationally fast. The second method has a much higher degree of parameterization but is flexible enough to detect deviations in the geologic settings of the reservoir; however, there is no explicit geologic significance and the method is computationally much less efficient. Forward seismic modeling of the two inversion results indicates a good match of both methods with the actual seismic data.


2017 ◽  
Vol 5 (4) ◽  
pp. T523-T530
Author(s):  
Ehsan Zabihi Naeini ◽  
Mark Sams

Broadband reprocessed seismic data from the North West Shelf of Australia were inverted using wavelets estimated with a conventional approach. The inversion method applied was a facies-based inversion, in which the low-frequency model is a product of the inversion process itself, constrained by facies-dependent input trends, the resultant facies distribution, and the match to the seismic. The results identified the presence of a gas reservoir that had recently been confirmed through drilling. The reservoir is thin, with up to 15 ms of maximum thickness. The bandwidth of the seismic data is approximately 5–70 Hz, and the well data used to extract the wavelet used in the inversion are only 400 ms long. As such, there was little control on the lowest frequencies of the wavelet. Different wavelets were subsequently estimated using a variety of new techniques that attempt to address the limitations of short well-log segments and low-frequency seismic. The revised inversion showed greater gas-sand continuity and an extension of the reservoir at one flank. Noise-free synthetic examples indicate that thin-bed delineation can depend on the accuracy of the low-frequency content of the wavelets used for inversion. Underestimation of the low-frequency contents can result in missing thin beds, whereas underestimation of high frequencies can introduce false thin beds. Therefore, it is very important to correctly capture the full frequency content of the seismic data in terms of the amplitude and phase spectra of the estimated wavelets, which subsequently leads to a more accurate thin-bed reservoir characterization through inversion.


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